feat: v1.3.0 — plan-first workflow, OpenRouter provider, enhanced prompt engine
Major changes: - Plan-first workflow: AI generates structured plan before code, with plan review card (Modify Plan / Start Coding / Skip to Code) - Post-coding UX: Preview + Request Modifications buttons after code gen - OpenRouter integration: 4th AI provider with 20+ model support - Enhanced prompt engine: 9 strategies, 11+ intent patterns, modular - PLAN MODE system prompt block in all 4 services - Fixed stale React closure in approveAndGenerate with isApproval flag - Fixed canvas auto-opening during plan phase with wasIdle gate - Updated README, CHANGELOG, .env.example, version bump to 1.3.0
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lib/enhance-engine.ts
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lib/enhance-engine.ts
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/**
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* Prompt Enhancement Engine
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* Based on prompt-master methodology (https://github.com/nidhinjs/prompt-master)
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* Client-side prompt analysis and optimization for various AI tools
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*/
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// ============================================================================
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// TYPE DEFINITIONS
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// ============================================================================
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/**
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* Tool categories with different prompting requirements
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*/
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export type ToolCategory =
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| 'reasoning' // Claude, GPT-4o, Gemini - Full structure, XML tags, explicit format locks
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| 'thinking' // o1, o3, DeepSeek-R1 - Short clean instructions only, no CoT
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| 'openweight' // Llama, Mistral, Qwen - Shorter prompts, simpler structure
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| 'agentic' // Claude Code, Devin, SWE-agent - Start/target state, allowed/forbidden actions, stop conditions
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| 'ide' // Cursor, Windsurf, Copilot - File path + function + desired change + scope lock
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| 'fullstack' // Bolt, v0, Lovable - Stack spec, component boundaries, what NOT to scaffold
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| 'image' // Midjourney, DALL-E, Stable Diffusion - Subject + style + mood + lighting + negative prompts
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| 'search'; // Perplexity, SearchGPT - Mode specification, citation requirements
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/**
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* Template frameworks for different prompt structures
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*/
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export type TemplateFramework =
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| 'RTF' // Role, Task, Format - Simple one-shot
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| 'CO-STAR' // Context, Objective, Style, Tone, Audience, Response - Professional documents
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| 'RISEN' // Role, Instructions, Steps, End Goal, Narrowing - Complex multi-step
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| 'CRISPE' // Capacity, Role, Insight, Statement, Personality, Experiment - Creative work
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| 'ChainOfThought' // Logic/math/debugging (NOT for thinking models)
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| 'FewShot' // Format-sensitive tasks
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| 'FileScope' // IDE AI editing
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| 'ReActPlusStop' // Agentic AI
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| 'VisualDescriptor'; // Image generation
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/**
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* Severity levels for diagnostic patterns
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*/
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export type Severity = 'critical' | 'warning' | 'info';
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/**
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* Diagnostic pattern for prompt analysis
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*/
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export interface DiagnosticPattern {
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id: string;
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name: string;
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description: string;
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category: 'task' | 'context' | 'format' | 'scope' | 'reasoning' | 'agentic';
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detect: (prompt: string) => boolean;
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fix: string;
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severity: Severity;
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}
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/**
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* Result from running diagnostics on a prompt
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*/
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export interface DiagnosticResult {
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pattern: DiagnosticPattern;
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detected: boolean;
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severity: Severity;
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suggestion: string;
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}
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/**
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* Template structure with metadata
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*/
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export interface Template {
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name: string;
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framework: TemplateFramework;
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description: string;
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structure: string[];
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bestFor: ToolCategory[];
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}
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/**
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* Complete analysis report for a prompt
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*/
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export interface AnalysisReport {
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prompt: string;
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tokenEstimate: number;
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suggestedTool: ToolCategory | null;
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suggestedTemplate: Template | null;
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diagnostics: DiagnosticResult[];
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missingDimensions: string[];
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overallScore: number; // 0-100
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}
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// ============================================================================
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// TOOL CATEGORIES
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// ============================================================================
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export const TOOL_CATEGORIES: Record<ToolCategory, {
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description: string;
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examples: string[];
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promptingStyle: string;
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}> = {
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reasoning: {
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description: 'Models with strong reasoning capabilities',
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examples: ['Claude', 'GPT-4o', 'Gemini'],
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promptingStyle: 'Full structure, XML tags, explicit format locks, detailed instructions'
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},
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thinking: {
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description: 'Models with built-in chain-of-thought',
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examples: ['o1', 'o3', 'DeepSeek-R1'],
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promptingStyle: 'Short clean instructions only, NO explicit CoT or step-by-step'
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},
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openweight: {
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description: 'Open-source models',
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examples: ['Llama', 'Mistral', 'Qwen'],
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promptingStyle: 'Shorter prompts, simpler structure, clear direct instructions'
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},
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agentic: {
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description: 'Autonomous coding agents',
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examples: ['Claude Code', 'Devin', 'SWE-agent'],
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promptingStyle: 'Start/target state, allowed/forbidden actions, stop conditions'
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},
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ide: {
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description: 'IDE-integrated AI assistants',
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examples: ['Cursor', 'Windsurf', 'Copilot'],
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promptingStyle: 'File path + function + desired change + scope lock'
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},
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fullstack: {
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description: 'Full-stack app builders',
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examples: ['Bolt', 'v0', 'Lovable'],
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promptingStyle: 'Stack spec, component boundaries, what NOT to scaffold'
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},
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image: {
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description: 'Image generation models',
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examples: ['Midjourney', 'DALL-E', 'Stable Diffusion'],
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promptingStyle: 'Subject + style + mood + lighting + negative prompts'
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},
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search: {
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description: 'Search-augmented AI',
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examples: ['Perplexity', 'SearchGPT'],
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promptingStyle: 'Mode specification, citation requirements, source attribution'
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}
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};
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// ============================================================================
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// TEMPLATE FRAMEWORKS
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// ============================================================================
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export const TEMPLATES: Template[] = [
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{
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name: 'RTF (Role-Task-Format)',
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framework: 'RTF',
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description: 'Simple one-shot prompts with clear role, task, and output format',
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structure: ['Role: Who you are', 'Task: What to do', 'Format: How to output'],
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bestFor: ['reasoning', 'openweight']
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},
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{
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name: 'CO-STAR',
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framework: 'CO-STAR',
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description: 'Comprehensive framework for professional documents and complex tasks',
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structure: [
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'Context: Background information',
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'Objective: What needs to be achieved',
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'Style: Writing style and tone',
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'Tone: Emotional tone',
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'Audience: Who will read this',
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'Response: Expected output format'
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],
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bestFor: ['reasoning', 'thinking', 'openweight']
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},
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{
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name: 'RISEN',
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framework: 'RISEN',
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description: 'Multi-step complex task framework with clear end goals',
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structure: [
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'Role: AI agent identity',
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'Instructions: Task requirements',
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'Steps: Sequential actions',
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'End Goal: Success criteria',
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'Narrowing: Constraints and boundaries'
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],
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bestFor: ['reasoning', 'agentic']
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},
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{
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name: 'CRISPE',
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framework: 'CRISPE',
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description: 'Creative work framework with personality and experimentation',
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structure: [
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'Capacity: What you can do',
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'Role: Creative identity',
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'Insight: Key perspective',
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'Statement: The core request',
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'Personality: Tone and style',
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'Experiment: Creative constraints'
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],
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bestFor: ['reasoning', 'openweight']
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},
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{
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name: 'Chain of Thought',
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framework: 'ChainOfThought',
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description: 'Step-by-step reasoning for logic, math, and debugging (NOT for thinking models)',
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structure: [
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'Problem statement',
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'Step-by-step reasoning',
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'Final answer',
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'Verification'
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],
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bestFor: ['reasoning', 'openweight']
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},
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{
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name: 'Few-Shot Learning',
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framework: 'FewShot',
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description: 'Provide examples to guide format-sensitive tasks',
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structure: [
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'Task description',
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'Example 1: Input -> Output',
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'Example 2: Input -> Output',
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'Example 3: Input -> Output',
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'Actual task'
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],
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bestFor: ['reasoning', 'openweight']
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},
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{
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name: 'File-Scope Lock',
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framework: 'FileScope',
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description: 'IDE-specific editing with precise file and function targeting',
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structure: [
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'File path',
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'Function/component name',
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'Current code snippet',
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'Desired change',
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'Scope: ONLY modify X, do NOT touch Y'
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],
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bestFor: ['ide']
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},
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{
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name: 'ReAct + Stop Conditions',
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framework: 'ReActPlusStop',
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description: 'Agentic framework with explicit stopping rules',
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structure: [
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'Starting state: Current situation',
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'Target state: Desired outcome',
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'Allowed actions: What you CAN do',
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'Forbidden actions: What you CANNOT do',
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'Stop conditions: When to pause and ask',
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'Output requirements: Progress reporting'
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],
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bestFor: ['agentic']
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},
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{
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name: 'Visual Descriptor',
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framework: 'VisualDescriptor',
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description: 'Comprehensive image generation prompt structure',
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structure: [
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'Subject: Main element',
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'Style: Art style or aesthetic',
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'Mood: Emotional quality',
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'Lighting: Light source and quality',
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'Composition: Framing and perspective',
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'Colors: Color palette',
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'Negative prompts: What to exclude'
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],
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bestFor: ['image']
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}
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];
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// ============================================================================
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// DIAGNOSTIC PATTERNS (35 Total)
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// ============================================================================
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const TASK_PATTERNS: DiagnosticPattern[] = [
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{
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id: 'task-001',
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name: 'Vague task verb',
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description: 'Uses generic verbs like "help", "fix", "make" without specifics',
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category: 'task',
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detect: (prompt: string) => {
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const vagueVerbs = /\b(help|fix|make|improve|update|change|handle|work on)\b/i;
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const noSpecifics = !/\b(specifically|exactly|to|that|which|called|named):\b/i.test(prompt);
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return vagueVerbs.test(prompt) && noSpecifics && prompt.split(' ').length < 30;
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},
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fix: 'Replace vague verbs with specific action verbs. Instead of "fix this", use "add error handling to the login function"',
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severity: 'warning'
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},
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{
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id: 'task-002',
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name: 'Two tasks in one',
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description: 'Contains multiple distinct tasks in a single prompt',
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category: 'task',
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detect: (prompt: string) => {
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const andPattern = /\b(and|also|plus|additionally)\s+[a-z]+\b/i;
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const commaTasks = /\b(create|build|fix|add|write|update)[^,.]+,[^,.]+(create|build|fix|add|write|update)/i;
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return andPattern.test(prompt) || commaTasks.test(prompt);
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},
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fix: 'Split into separate prompts. Each prompt should have ONE primary task.',
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severity: 'critical'
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},
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{
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id: 'task-003',
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name: 'No success criteria',
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description: 'Missing clear definition of when the task is complete',
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category: 'task',
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detect: (prompt: string) => {
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const successWords = /\b(done when|success criteria|complete when|should|must result|verify that|ensure that|passes when)\b/i;
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const isComplexTask = /\b(build|create|implement|develop|design|setup)\b/i.test(prompt);
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return isComplexTask && !successWords.test(prompt);
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},
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fix: 'Add explicit success criteria: "The task is complete when [specific condition is met]"',
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severity: 'warning'
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},
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{
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id: 'task-004',
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name: 'Over-permissive agent',
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description: 'Gives AI too much freedom without constraints',
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category: 'task',
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detect: (prompt: string) => {
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const permissivePhrases = /\b(whatever it takes|do your best|figure it out|you decide|however you want|as you see fit)\b/i;
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return permissivePhrases.test(prompt);
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},
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fix: 'Replace open-ended permissions with specific constraints and scope boundaries.',
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severity: 'critical'
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},
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{
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id: 'task-005',
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name: 'Emotional task description',
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description: 'Uses emotional language without specific technical details',
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category: 'task',
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detect: (prompt: string) => {
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const emotionalWords = /\b(broken|mess|terrible|awful|doesn't work|horrible|stupid|hate|frustrating)\b/i;
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const noTechnicalDetails = !/\b(error|bug|line|function|file|exception|fail|crash)\b/i.test(prompt);
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return emotionalWords.test(prompt) && noTechnicalDetails;
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},
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fix: 'Replace emotional language with specific technical details: what error, what line, what behavior?',
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severity: 'warning'
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},
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{
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id: 'task-006',
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name: 'Build-the-whole-thing',
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description: 'Attempts to build an entire project in one prompt',
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category: 'task',
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detect: (prompt: string) => {
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const wholeProjectPhrases = /\b(entire app|whole project|full website|complete system|everything|end to end|from scratch)\b/i;
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return wholeProjectPhrases.test(prompt);
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},
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fix: 'Break down into smaller, iterative prompts. Start with core functionality, then add features.',
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severity: 'critical'
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},
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{
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id: 'task-007',
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name: 'Implicit reference',
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description: 'References something previously mentioned without context',
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category: 'task',
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detect: (prompt: string) => {
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const implicitRefs = /\b(the thing|that one|what we discussed|from before|the previous|like the other)\b/i;
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const noContext = prompt.split(' ').length < 50;
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return implicitRefs.test(prompt) && noContext;
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},
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fix: 'Always include full context. Replace "the thing" with specific name/description.',
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severity: 'critical'
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}
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];
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const CONTEXT_PATTERNS: DiagnosticPattern[] = [
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{
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id: 'ctx-001',
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name: 'Assumed prior knowledge',
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description: 'Assumes AI remembers previous conversations or context',
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category: 'context',
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detect: (prompt: string) => {
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const assumptionPhrases = /\b(continue|as before|like we said|you know|from our chat|from earlier)\b/i;
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const noContextProvided = prompt.split(' ').length < 40;
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return assumptionPhrases.test(prompt) && noContextProvided;
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},
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fix: 'Include relevant context from previous work. Do not assume continuity.',
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severity: 'warning'
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},
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{
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id: 'ctx-002',
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name: 'No project context',
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description: 'Very short prompt with no domain or technology context',
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category: 'context',
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detect: (prompt: string) => {
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const wordCount = prompt.split(/\s+/).length;
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const hasTech = /\b(javascript|python|react|api|database|server|frontend|backend|mobile|web)\b/i;
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return wordCount < 15 && !hasTech.test(prompt);
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},
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fix: 'Add project context: technology stack, domain, and what you\'re building.',
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severity: 'warning'
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},
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{
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id: 'ctx-003',
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name: 'Forgotten stack',
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description: 'Tech-agnostic prompt that implies an existing project',
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category: 'context',
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detect: (prompt: string) => {
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const projectWords = /\b(add to|update the|change the|modify the|existing|current)\b/i;
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const noTechStack = !/\b(javascript|typescript|python|java|rust|go|react|vue|angular|node|django|rails)\b/i.test(prompt);
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return projectWords.test(prompt) && noTechStack;
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},
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fix: 'Specify your technology stack: language, framework, and key dependencies.',
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severity: 'critical'
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},
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{
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id: 'ctx-004',
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name: 'Hallucination invite',
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description: 'Asks for general knowledge that may not exist',
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category: 'context',
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detect: (prompt: string) => {
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const hallucinationPhrases = /\b(what do experts say|what is commonly known|generally accepted|most people think|typical approach)\b/i;
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return hallucinationPhrases.test(prompt);
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},
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fix: 'Ask for specific sources or provide source material. Avoid general "what do X think" questions.',
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severity: 'info'
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},
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{
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id: 'ctx-005',
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name: 'Undefined audience',
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description: 'User-facing output without audience specification',
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category: 'context',
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detect: (prompt: string) => {
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const userFacing = /\b(write|create|generate|draft)\s+(content|message|email|copy|text|documentation)\b/i;
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const noAudience = !/\b(for|audience|target|reader|user|customer|stakeholder)\b/i.test(prompt);
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return userFacing.test(prompt) && noAudience;
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},
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fix: 'Specify who will read this output: "Write for [audience] who [context]"',
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severity: 'warning'
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},
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{
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id: 'ctx-006',
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name: 'No prior failures',
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description: 'Complex task without mentioning what was tried before',
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category: 'context',
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detect: (prompt: string) => {
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const complexTask = /\b(debug|fix|solve|resolve|implement|build|create)\b/i;
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const noPriorAttempts = !/\b(tried|attempted|already|previous|before|not working|failed)\b/i.test(prompt);
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const isLongPrompt = prompt.split(' ').length > 20;
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return complexTask.test(prompt) && noPriorAttempts && isLongPrompt;
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||||
},
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||||
fix: 'Mention what you\'ve already tried: "I tried X but got Y error. Now..."',
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severity: 'info'
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||||
}
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||||
];
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||||
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||||
const FORMAT_PATTERNS: DiagnosticPattern[] = [
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||||
{
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||||
id: 'fmt-001',
|
||||
name: 'Missing output format',
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||||
description: 'No specification of how output should be structured',
|
||||
category: 'format',
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detect: (prompt: string) => {
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const formatKeywords = /\b(list|table|json|markdown|bullet|paragraph|csv|html|code|steps)\b/i;
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const outputKeywords = /\b(output|return|format as|in the form of|structure)\b/i;
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||||
return !formatKeywords.test(prompt) && !outputKeywords.test(prompt);
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||||
},
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||||
fix: 'Specify output format: "Return as a bulleted list" or "Output as JSON"',
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||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'fmt-002',
|
||||
name: 'Implicit length',
|
||||
description: 'Uses length terms without specific counts',
|
||||
category: 'format',
|
||||
detect: (prompt: string) => {
|
||||
const vagueLength = /\b(summary|description|overview|brief|short|long|detailed)\b/i;
|
||||
const noSpecificLength = !/\b(\d+\s*(words?|sentences?|paragraphs?)|under\s*\d+|max\s*\d+)\b/i.test(prompt);
|
||||
return vagueLength.test(prompt) && noSpecificLength;
|
||||
},
|
||||
fix: 'Be specific: "Write 2-3 sentences" or "Keep under 100 words"',
|
||||
severity: 'info'
|
||||
},
|
||||
{
|
||||
id: 'fmt-003',
|
||||
name: 'No role assignment',
|
||||
description: 'Long prompt without specifying who AI should be',
|
||||
category: 'format',
|
||||
detect: (prompt: string) => {
|
||||
const wordCount = prompt.split(/\s+/).length;
|
||||
const roleKeywords = /\b(act as|you are|role|persona|expert|specialist|professional|engineer|developer|analyst)\b/i;
|
||||
return wordCount > 50 && !roleKeywords.test(prompt);
|
||||
},
|
||||
fix: 'Add role assignment: "Act as a [role] with [expertise]"',
|
||||
severity: 'info'
|
||||
},
|
||||
{
|
||||
id: 'fmt-004',
|
||||
name: 'Vague aesthetic',
|
||||
description: 'Design-related prompt without specific visual direction',
|
||||
category: 'format',
|
||||
detect: (prompt: string) => {
|
||||
const vagueAesthetic = /\b(professional|clean|modern|nice|good looking|beautiful|sleek)\b/i;
|
||||
const noVisualSpecs = !/\b(colors?|fonts?|spacing|layout|style|theme|design system)\b/i.test(prompt);
|
||||
return vagueAesthetic.test(prompt) && noVisualSpecs;
|
||||
},
|
||||
fix: 'Specify visual details: colors, typography, spacing, specific design reference.',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'fmt-005',
|
||||
name: 'No negative prompts for image',
|
||||
description: 'Image generation without exclusion criteria',
|
||||
category: 'format',
|
||||
detect: (prompt: string) => {
|
||||
const imageKeywords = /\b(image|photo|picture|illustration|generate|create art|midjourney|dall-e)\b/i;
|
||||
const noNegative = !/\b(negative|exclude|avoid|without|no|not)\b/i.test(prompt);
|
||||
return imageKeywords.test(prompt) && noNegative;
|
||||
},
|
||||
fix: 'Add negative prompts: "Negative: blurry, low quality, distorted"',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'fmt-006',
|
||||
name: 'Prose for Midjourney',
|
||||
description: 'Long descriptive sentences instead of keyword-style prompts',
|
||||
category: 'format',
|
||||
detect: (prompt: string) => {
|
||||
const longSentences = prompt.split(/[.!?]/).filter(s => s.trim().split(' ').length > 10).length > 0;
|
||||
const imageKeywords = /\b(image|photo|art|illustration|midjourney|dall-e|stable diffusion)\b/i;
|
||||
return imageKeywords.test(prompt) && longSentences;
|
||||
},
|
||||
fix: 'Use keyword-style prompts: "Subject, style, mood, lighting, --ar 16:9"',
|
||||
severity: 'warning'
|
||||
}
|
||||
];
|
||||
|
||||
const SCOPE_PATTERNS: DiagnosticPattern[] = [
|
||||
{
|
||||
id: 'scp-001',
|
||||
name: 'No scope boundary',
|
||||
description: 'Missing specific scope constraints',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
const scopeWords = /\b(only|just|specifically|exactly|limit|restrict)\b/i;
|
||||
const hasFilePath = /\/[\w.]+/.test(prompt) || /\b[\w-]+\.(js|ts|py|java|go|rs|cpp|c|h)\b/i;
|
||||
const hasFunction = /\b(function|method|class|component)\s+\w+/i;
|
||||
return !scopeWords.test(prompt) && !hasFilePath && !hasFunction;
|
||||
},
|
||||
fix: 'Add scope boundary: "Only modify X, do NOT touch Y"',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'scp-002',
|
||||
name: 'No stack constraints',
|
||||
description: 'Technical task without version specifications',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
const techTask = /\b(build|create|implement|setup|install|use|add)\s+(\w+\s+){0,3}(app|api|server|database|system)\b/i;
|
||||
const noVersion = !/\b(version|v\d+|\d+\.\d+|specifically|exactly)\b/i.test(prompt);
|
||||
return techTask.test(prompt) && noVersion;
|
||||
},
|
||||
fix: 'Specify versions: "Use React 18 with TypeScript 5"',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'scp-003',
|
||||
name: 'No stop condition for agents',
|
||||
description: 'Agentic task without explicit stopping rules',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
const agentKeywords = /\b(agent|autonomous|run this|execute|iterate|keep going)\b/i;
|
||||
const noStop = !/\b(stop|pause|ask me|check in|before continuing|confirm)\b/i.test(prompt);
|
||||
return agentKeywords.test(prompt) && noStop;
|
||||
},
|
||||
fix: 'Add stop conditions: "Stop and ask before deleting files" or "Pause after each major step"',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'scp-004',
|
||||
name: 'No file path for IDE',
|
||||
description: 'IDE editing without file specification',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
const editKeywords = /\b(update|fix|change|modify|edit|refactor)\b/i;
|
||||
const hasPath = /\/[\w./-]+|\b[\w-]+\.(js|ts|jsx|tsx|py|java|go|rs|cpp|c|h|css|html|json)\b/i;
|
||||
return editKeywords.test(prompt) && !hasPath;
|
||||
},
|
||||
fix: 'Always include file path: "Update src/components/Header.tsx"',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'scp-005',
|
||||
name: 'Wrong template',
|
||||
description: 'Template mismatch for the target tool',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
// Detect if using complex structure for thinking models
|
||||
const thinkingModel = /\b(o1|o3|deepseek.*r1|thinking)\b/i;
|
||||
const complexStructure = /\b(step by step|think through|reasoning|<thinking>|chain of thought)\b/i;
|
||||
return thinkingModel.test(prompt) && complexStructure.test(prompt);
|
||||
},
|
||||
fix: 'For thinking models (o1, o3, R1), use short clean instructions without explicit CoT.',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'scp-006',
|
||||
name: 'Pasting codebase',
|
||||
description: 'Extremely long prompt suggesting codebase paste',
|
||||
category: 'scope',
|
||||
detect: (prompt: string) => {
|
||||
const wordCount = prompt.split(/\s+/).length;
|
||||
const multipleFiles = (prompt.match(/```/g) || []).length > 4;
|
||||
return wordCount > 500 || multipleFiles;
|
||||
},
|
||||
fix: 'Use file paths and references instead of pasting entire files. Or use an IDE AI tool.',
|
||||
severity: 'warning'
|
||||
}
|
||||
];
|
||||
|
||||
const REASONING_PATTERNS: DiagnosticPattern[] = [
|
||||
{
|
||||
id: 'rsn-001',
|
||||
name: 'No CoT for logic',
|
||||
description: 'Complex logic task without step-by-step instructions',
|
||||
category: 'reasoning',
|
||||
detect: (prompt: string) => {
|
||||
const logicKeywords = /\b(compare|analyze|which is better|debug|why does|explain why|how does|verify)\b/i;
|
||||
const noCoT = !/\b(step by step|walk through|reasoning|think through|first|then|finally)\b/i.test(prompt);
|
||||
return logicKeywords.test(prompt) && noCoT;
|
||||
},
|
||||
fix: 'Add "Step by step" or "Walk through your reasoning" for logic tasks.',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'rsn-002',
|
||||
name: 'CoT on reasoning models',
|
||||
description: 'Explicit CoT instructions for thinking models',
|
||||
category: 'reasoning',
|
||||
detect: (prompt: string) => {
|
||||
const thinkingModel = /\b(o1|o3|deepseek.*r1)\b/i;
|
||||
const explicitCoT = /\b(step by step|think through|<thinking>|reasoning process|show your work)\b/i;
|
||||
return thinkingModel.test(prompt) && explicitCoT.test(prompt);
|
||||
},
|
||||
fix: 'Remove explicit CoT instructions. Thinking models have built-in reasoning.',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'rsn-003',
|
||||
name: 'Inter-session memory',
|
||||
description: 'Assumes AI remembers across separate sessions',
|
||||
category: 'reasoning',
|
||||
detect: (prompt: string) => {
|
||||
const memoryPhrases = /\b(you already know|remember|from our conversation|we discussed|earlier we|as mentioned)\b/i;
|
||||
return memoryPhrases.test(prompt);
|
||||
},
|
||||
fix: 'AI does not remember between sessions. Include all necessary context.',
|
||||
severity: 'info'
|
||||
},
|
||||
{
|
||||
id: 'rsn-004',
|
||||
name: 'Contradicting prior',
|
||||
description: 'Explicit contradiction of previous instructions',
|
||||
category: 'reasoning',
|
||||
detect: (prompt: string) => {
|
||||
const contradictionPhrases = /\b(actually|wait|ignore what i said|forget that|never mind|scratch that)\b/i;
|
||||
return contradictionPhrases.test(prompt);
|
||||
},
|
||||
fix: 'State corrections clearly: "Correction: Replace X with Y"',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'rsn-005',
|
||||
name: 'No grounding rule',
|
||||
description: 'Factual task without certainty constraints',
|
||||
category: 'reasoning',
|
||||
detect: (prompt: string) => {
|
||||
const factualTask = /\b(summarize|what is|tell me about|explain|list|research|find)\b/i;
|
||||
const noGrounding = !/\b(if unsure|don't hallucinate|only if certain|say i don't know|stick to)\b/i.test(prompt);
|
||||
return factualTask.test(prompt) && noGrounding && prompt.split(' ').length > 10;
|
||||
},
|
||||
fix: 'Add grounding: "If uncertain, say so rather than guessing"',
|
||||
severity: 'info'
|
||||
}
|
||||
];
|
||||
|
||||
const AGENTIC_PATTERNS: DiagnosticPattern[] = [
|
||||
{
|
||||
id: 'agt-001',
|
||||
name: 'No starting state',
|
||||
description: 'Build/create task without current state description',
|
||||
category: 'agentic',
|
||||
detect: (prompt: string) => {
|
||||
const buildKeywords = /\b(build|create|set up|implement|develop|make)\b/i;
|
||||
const currentState = !/\b(currently|existing|now|currently have|right now|starting from)\b/i.test(prompt);
|
||||
return buildKeywords.test(prompt) && currentState;
|
||||
},
|
||||
fix: 'Describe starting state: "Currently I have X. I want to reach Y."',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'agt-002',
|
||||
name: 'No target state',
|
||||
description: 'Agentic task without explicit deliverable',
|
||||
category: 'agentic',
|
||||
detect: (prompt: string) => {
|
||||
const vagueCompletion = /\b(work on this|handle this|do this|take care of)\b/i;
|
||||
const noTarget = !/\b(result should|final output|deliverable|end with|complete when)\b/i.test(prompt);
|
||||
return vagueCompletion.test(prompt) && noTarget;
|
||||
},
|
||||
fix: 'Specify target state: "The final result should be [specific outcome]"',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'agt-003',
|
||||
name: 'Silent agent',
|
||||
description: 'Multi-step task without progress reporting requirements',
|
||||
category: 'agentic',
|
||||
detect: (prompt: string) => {
|
||||
const multiStep = /\b(then|next|after that|first|second|finally)\b/i;
|
||||
const noOutput = !/\b(show me|report|output|print|log|display progress|tell me)\b/i.test(prompt);
|
||||
return multiStep.test(prompt) && noOutput;
|
||||
},
|
||||
fix: 'Add output requirements: "Report progress after each step"',
|
||||
severity: 'warning'
|
||||
},
|
||||
{
|
||||
id: 'agt-004',
|
||||
name: 'Unlocked filesystem',
|
||||
description: 'Agentic task without file access restrictions',
|
||||
category: 'agentic',
|
||||
detect: (prompt: string) => {
|
||||
const agentKeywords = /\b(agent|autonomous|run|execute|implement|build|create)\b/i;
|
||||
const noRestrictions = !/\b(only touch|don't modify|never delete|restrict to|scope|limit)\b/i.test(prompt);
|
||||
return agentKeywords.test(prompt) && noRestrictions;
|
||||
},
|
||||
fix: 'Add file restrictions: "Only modify files in X, never touch Y"',
|
||||
severity: 'critical'
|
||||
},
|
||||
{
|
||||
id: 'agt-005',
|
||||
name: 'No review trigger',
|
||||
description: 'Agentic task without approval checkpoints',
|
||||
category: 'agentic',
|
||||
detect: (prompt: string) => {
|
||||
const riskyActions = /\b(delete|remove|overwrite|deploy|publish|submit|merge)\b/i;
|
||||
const noReview = !/\b(ask before|confirm|review|approve|check with me)\b/i.test(prompt);
|
||||
return riskyActions.test(prompt) && noReview;
|
||||
},
|
||||
fix: 'Add review triggers: "Ask before deleting any files" or "Confirm before deploying"',
|
||||
severity: 'critical'
|
||||
}
|
||||
];
|
||||
|
||||
// Combine all patterns
|
||||
export const ALL_PATTERNS: DiagnosticPattern[] = [
|
||||
...TASK_PATTERNS,
|
||||
...CONTEXT_PATTERNS,
|
||||
...FORMAT_PATTERNS,
|
||||
...SCOPE_PATTERNS,
|
||||
...REASONING_PATTERNS,
|
||||
...AGENTIC_PATTERNS
|
||||
];
|
||||
|
||||
// ============================================================================
|
||||
// CORE FUNCTIONS
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* Auto-detect the target AI tool category based on prompt content
|
||||
*/
|
||||
export function detectToolCategory(prompt: string): ToolCategory | null {
|
||||
const p = prompt.toLowerCase();
|
||||
|
||||
// Check for specific tool mentions
|
||||
if (/(claude|gpt-4|gemini|gpt4)/i.test(prompt)) return 'reasoning';
|
||||
if (/(o1|o3|deepseek.*r1|thinking.*model)/i.test(prompt)) return 'thinking';
|
||||
if (/(llama|mistral|qwen|open.*weight|local.*model)/i.test(prompt)) return 'openweight';
|
||||
if (/(claude code|devin|swe.*agent|autonomous.*agent)/i.test(prompt)) return 'agentic';
|
||||
if (/(cursor|windsurf|copilot|ide.*ai|editor.*ai)/i.test(prompt)) return 'ide';
|
||||
if (/(bolt|v0|lovable|fullstack.*ai|app.*builder)/i.test(prompt)) return 'fullstack';
|
||||
if (/(midjourney|dall.?e|stable diffusion|image.*generate|create.*image|generate.*art)/i.test(prompt)) return 'image';
|
||||
if (/(perplexity|searchgpt|search.*ai|research.*mode)/i.test(prompt)) return 'search';
|
||||
|
||||
// Infer from content patterns
|
||||
if (/\.(js|ts|py|java|go|rs|cpp|c|h)\b/.test(prompt) && /\b(update|fix|change|modify)\b/.test(p)) return 'ide';
|
||||
if (/\b(build|create|set up|implement).*\b(app|api|server|system)\b/.test(p) && /\b(stop|pause|ask before)\b/.test(p)) return 'agentic';
|
||||
if (/\b(step by step|<thinking>|chain of thought|reasoning)\b/.test(p)) return 'reasoning';
|
||||
if (/\b(image|photo|art|illustration)\b/.test(p) && /\b(style|mood|lighting)\b/.test(p)) return 'image';
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Select the best template based on tool category and prompt analysis
|
||||
*/
|
||||
export function selectTemplate(prompt: string, toolCategory: ToolCategory | null): Template | null {
|
||||
const p = prompt.toLowerCase();
|
||||
|
||||
// Image generation
|
||||
if (toolCategory === 'image' || /\b(image|photo|art|illustration|midjourney|dall.?e)\b/.test(p)) {
|
||||
return TEMPLATES.find(t => t.framework === 'VisualDescriptor') || null;
|
||||
}
|
||||
|
||||
// IDE editing
|
||||
if (toolCategory === 'ide' || (/\.(js|ts|py|java|go|rs)\b/.test(prompt) && /\b(update|fix|modify)\b/.test(p))) {
|
||||
return TEMPLATES.find(t => t.framework === 'FileScope') || null;
|
||||
}
|
||||
|
||||
// Agentic tasks
|
||||
if (toolCategory === 'agentic' || /\b(build|create|set up).*\b(stop|pause|ask before)\b/.test(p)) {
|
||||
return TEMPLATES.find(t => t.framework === 'ReActPlusStop') || null;
|
||||
}
|
||||
|
||||
// Complex multi-step tasks
|
||||
if (/\b(step|then|next|after|first|second|finally)\b/.test(p) && p.split(' ').length > 30) {
|
||||
return TEMPLATES.find(t => t.framework === 'RISEN') || null;
|
||||
}
|
||||
|
||||
// Logic/debugging tasks
|
||||
if (/\b(debug|compare|analyze|which is better|why does|verify)\b/.test(p)) {
|
||||
if (toolCategory !== 'thinking') {
|
||||
return TEMPLATES.find(t => t.framework === 'ChainOfThought') || null;
|
||||
}
|
||||
}
|
||||
|
||||
// Professional documents
|
||||
if (/\b(documentation|report|proposal|spec|requirements)\b/.test(p) && p.split(' ').length > 40) {
|
||||
return TEMPLATES.find(t => t.framework === 'CO-STAR') || null;
|
||||
}
|
||||
|
||||
// Creative work
|
||||
if (/\b(creative|design|story|narrative|brand|voice)\b/.test(p)) {
|
||||
return TEMPLATES.find(t => t.framework === 'CRISPE') || null;
|
||||
}
|
||||
|
||||
// Format-sensitive tasks
|
||||
if (/\b(example|sample|format|pattern|template)\b/.test(p)) {
|
||||
return TEMPLATES.find(t => t.framework === 'FewShot') || null;
|
||||
}
|
||||
|
||||
// Default to RTF for simple prompts
|
||||
if (p.split(' ').length < 50) {
|
||||
return TEMPLATES.find(t => t.framework === 'RTF') || null;
|
||||
}
|
||||
|
||||
// Default for longer prompts
|
||||
return TEMPLATES.find(t => t.framework === 'CO-STAR') || null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Run all diagnostic patterns on a prompt
|
||||
*/
|
||||
export function runDiagnostics(prompt: string): DiagnosticResult[] {
|
||||
const results: DiagnosticResult[] = [];
|
||||
|
||||
for (const pattern of ALL_PATTERNS) {
|
||||
const detected = pattern.detect(prompt);
|
||||
if (detected) {
|
||||
results.push({
|
||||
pattern,
|
||||
detected: true,
|
||||
severity: pattern.severity,
|
||||
suggestion: pattern.fix
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by severity (critical first)
|
||||
const severityOrder = { critical: 0, warning: 1, info: 2 };
|
||||
results.sort((a, b) => severityOrder[a.severity] - severityOrder[b.severity]);
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/**
|
||||
* Estimate token count (rough approximation: ~0.75 words per token)
|
||||
*/
|
||||
export function estimateTokens(prompt: string): number {
|
||||
const wordCount = prompt.split(/\s+/).length;
|
||||
return Math.ceil(wordCount * 0.75);
|
||||
}
|
||||
|
||||
/**
|
||||
* Identify missing dimensions from a prompt
|
||||
*/
|
||||
export function identifyMissingDimensions(prompt: string): string[] {
|
||||
const missing: string[] = [];
|
||||
const p = prompt.toLowerCase();
|
||||
|
||||
// Check for common dimensions
|
||||
if (!/\b(act as|you are|role|expert|specialist)\b/i.test(prompt)) {
|
||||
missing.push('Role/Identity');
|
||||
}
|
||||
|
||||
if (!/\b(context|background|project|currently working)\b/i.test(prompt)) {
|
||||
missing.push('Context');
|
||||
}
|
||||
|
||||
if (!/\b(format|output|return as|structure)\b/i.test(prompt)) {
|
||||
missing.push('Output Format');
|
||||
}
|
||||
|
||||
if (!/\b(success|complete when|done when|verify|ensure)\b/i.test(prompt)) {
|
||||
missing.push('Success Criteria');
|
||||
}
|
||||
|
||||
if (!/\b(only|just|limit|restrict|scope)\b/i.test(prompt) && prompt.split(' ').length > 20) {
|
||||
missing.push('Scope Boundaries');
|
||||
}
|
||||
|
||||
if (!/\b(javascript|python|react|node|typescript|java|rust|go)\b/i.test(prompt) &&
|
||||
/\b(code|function|class|app|api)\b/i.test(prompt)) {
|
||||
missing.push('Technology Stack');
|
||||
}
|
||||
|
||||
return missing;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate overall prompt quality score (0-100)
|
||||
*/
|
||||
export function calculateScore(diagnostics: DiagnosticResult[], missingDimensions: string[]): number {
|
||||
let score = 100;
|
||||
|
||||
// Deduct for diagnostics
|
||||
for (const d of diagnostics) {
|
||||
switch (d.severity) {
|
||||
case 'critical': score -= 15; break;
|
||||
case 'warning': score -= 8; break;
|
||||
case 'info': score -= 3; break;
|
||||
}
|
||||
}
|
||||
|
||||
// Deduct for missing dimensions
|
||||
score -= missingDimensions.length * 5;
|
||||
|
||||
return Math.max(0, Math.min(100, score));
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate comprehensive analysis report
|
||||
*/
|
||||
export function generateAnalysisReport(prompt: string): AnalysisReport {
|
||||
const suggestedTool = detectToolCategory(prompt);
|
||||
const suggestedTemplate = selectTemplate(prompt, suggestedTool);
|
||||
const diagnostics = runDiagnostics(prompt);
|
||||
const missingDimensions = identifyMissingDimensions(prompt);
|
||||
const tokenEstimate = estimateTokens(prompt);
|
||||
const overallScore = calculateScore(diagnostics, missingDimensions);
|
||||
|
||||
return {
|
||||
prompt,
|
||||
tokenEstimate,
|
||||
suggestedTool,
|
||||
suggestedTemplate,
|
||||
diagnostics,
|
||||
missingDimensions,
|
||||
overallScore
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Get human-readable tool category description
|
||||
*/
|
||||
export function getToolDescription(category: ToolCategory): string {
|
||||
return TOOL_CATEGORIES[category].description;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get prompting style for a tool category
|
||||
*/
|
||||
export function getPromptingStyle(category: ToolCategory): string {
|
||||
return TOOL_CATEGORIES[category].promptingStyle;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get patterns by category
|
||||
*/
|
||||
export function getPatternsByCategory(category: DiagnosticPattern['category']): DiagnosticPattern[] {
|
||||
return ALL_PATTERNS.filter(p => p.category === category);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get pattern by ID
|
||||
*/
|
||||
export function getPatternById(id: string): DiagnosticPattern | undefined {
|
||||
return ALL_PATTERNS.find(p => p.id === id);
|
||||
}
|
||||
@@ -47,6 +47,21 @@ export const translations = {
|
||||
clear: "Clear",
|
||||
enterPromptError: "Please enter a prompt to enhance",
|
||||
errorEnhance: "Failed to enhance prompt",
|
||||
enhanceMode: "Enhancement Mode",
|
||||
quickMode: "Quick",
|
||||
deepMode: "Deep Analysis",
|
||||
deepEnhance: "Deep Enhance",
|
||||
targetTool: "Target AI Tool",
|
||||
templateLabel: "Template Framework",
|
||||
diagnosticsTitle: "Prompt Diagnostics",
|
||||
promptQuality: "Prompt Quality",
|
||||
missingDimensions: "Missing Dimensions",
|
||||
tokensLabel: "tokens",
|
||||
inputTokens: "input tokens",
|
||||
outputTokens: "output tokens",
|
||||
strategyNote: "Strategy",
|
||||
strategyForTool: "Optimized for {tool} using {template} template.",
|
||||
fixedIssues: "Fixed {count} critical issue(s).",
|
||||
},
|
||||
prdGenerator: {
|
||||
title: "PRD Generator",
|
||||
@@ -179,6 +194,7 @@ export const translations = {
|
||||
qwenDesc: "Alibaba DashScope API",
|
||||
ollamaDesc: "Ollama Cloud API",
|
||||
zaiDesc: "Z.AI Plan API",
|
||||
openrouterDesc: "OpenRouter - Access 100+ AI models",
|
||||
},
|
||||
uxDesigner: {
|
||||
title: "UX Designer Prompt",
|
||||
@@ -405,6 +421,11 @@ export const translations = {
|
||||
files: "Files",
|
||||
approveGenerate: "Approve & Generate Development",
|
||||
startingEngine: "Starting Engine...",
|
||||
startCoding: "Start Coding",
|
||||
modifyPlan: "Modify Plan",
|
||||
skipPlan: "Skip to Chat",
|
||||
planSummary: "Summary",
|
||||
implementationSteps: "Implementation Steps",
|
||||
activateArtifact: "Activate Artifact",
|
||||
canvasReady: "Canvas ready",
|
||||
canvasIdle: "Canvas idle",
|
||||
@@ -491,6 +512,21 @@ export const translations = {
|
||||
clear: "Очистить",
|
||||
enterPromptError: "Пожалуйста, введите промпт для улучшения",
|
||||
errorEnhance: "Не удалось улучшить промпт",
|
||||
enhanceMode: "Режим улучшения",
|
||||
quickMode: "Быстрый",
|
||||
deepMode: "Глубокий анализ",
|
||||
deepEnhance: "Глубокое улучшение",
|
||||
targetTool: "Целевой ИИ-инструмент",
|
||||
templateLabel: "Шаблон фреймворка",
|
||||
diagnosticsTitle: "Диагностика промпта",
|
||||
promptQuality: "Качество промпта",
|
||||
missingDimensions: "Отсутствующие параметры",
|
||||
tokensLabel: "токенов",
|
||||
inputTokens: "входных токенов",
|
||||
outputTokens: "выходных токенов",
|
||||
strategyNote: "Стратегия",
|
||||
strategyForTool: "Оптимизировано для {tool} с шаблоном {template}.",
|
||||
fixedIssues: "Исправлено {count} критических проблем(ы).",
|
||||
},
|
||||
prdGenerator: {
|
||||
title: "Генератор PRD",
|
||||
@@ -622,6 +658,7 @@ export const translations = {
|
||||
getApiKey: "Получить API ключ здесь:",
|
||||
qwenDesc: "Alibaba DashScope API",
|
||||
ollamaDesc: "Ollama Cloud API",
|
||||
openrouterDesc: "OpenRouter — доступ к 100+ ИИ-моделям",
|
||||
zaiDesc: "Z.AI Plan API",
|
||||
},
|
||||
uxDesigner: {
|
||||
@@ -849,6 +886,11 @@ export const translations = {
|
||||
files: "Файлы",
|
||||
approveGenerate: "Одобрить и начать разработку",
|
||||
startingEngine: "Запуск двигателя...",
|
||||
startCoding: "Начать кодинг",
|
||||
modifyPlan: "Изменить план",
|
||||
skipPlan: "Пропустить в чат",
|
||||
planSummary: "Суммарно",
|
||||
implementationSteps: "Шаги реализации",
|
||||
activateArtifact: "Активировать артефакт",
|
||||
canvasReady: "Холст готов",
|
||||
canvasIdle: "Холст в режиме ожидания",
|
||||
@@ -926,15 +968,30 @@ export const translations = {
|
||||
},
|
||||
promptEnhancer: {
|
||||
title: "משפר פרומפטים",
|
||||
description: "הפוך רעיונות פשוטים לפרומפטים מקצועיים באיכות גבוהה",
|
||||
description: "הפוך רעיונות פשוטים לפרומפטים מקצועניים באיכות גבוהה",
|
||||
placeholder: "הדבק את הפרומפט הראשוני שלך כאן...",
|
||||
inputLabel: "פרומפט מקורי",
|
||||
enhancedTitle: "אינטליגנציה משופרת",
|
||||
enhancedDesc: "פרומפט מקצועי מוכן לסוכני קידוד",
|
||||
enhancedDesc: "פרומפט מקצועני מוכן לכל כלי AI",
|
||||
emptyState: "פרומפט משופר יופיע כאן",
|
||||
clear: "נקה",
|
||||
enterPromptError: "אנא הזן פרומפט לשיפור",
|
||||
errorEnhance: "נכשל בשיפור הפרומפט",
|
||||
enhanceMode: "מצב שיפור",
|
||||
quickMode: "מהיר",
|
||||
deepMode: "ניתוח עמוק",
|
||||
deepEnhance: "שיפור עמוק",
|
||||
targetTool: "כלי AI יעד",
|
||||
templateLabel: "מסגרת תבנית",
|
||||
diagnosticsTitle: "אבחון פרומפט",
|
||||
promptQuality: "איכות פרומפט",
|
||||
missingDimensions: "מימדים חסרים",
|
||||
tokensLabel: "אסימונים",
|
||||
inputTokens: "אסימוני קלט",
|
||||
outputTokens: "אסימוני פלט",
|
||||
strategyNote: "אסטרטגיה",
|
||||
strategyForTool: "מותאם עבור {tool} עם תבנית {template}.",
|
||||
fixedIssues: "תוקנו {count} בעיות קריטיות.",
|
||||
},
|
||||
prdGenerator: {
|
||||
title: "מחולל PRD",
|
||||
@@ -1065,6 +1122,7 @@ export const translations = {
|
||||
enterKey: (provider: string) => `הזן את מפתח ה-API של ${provider}`,
|
||||
getApiKey: "קבל מפתח API מ-",
|
||||
qwenDesc: "Alibaba DashScope API",
|
||||
openrouterDesc: "OpenRouter — גישה ל-100+ מודלי AI",
|
||||
ollamaDesc: "Ollama Cloud API",
|
||||
zaiDesc: "Z.AI Plan API",
|
||||
},
|
||||
@@ -1293,6 +1351,11 @@ export const translations = {
|
||||
files: "קבצים",
|
||||
approveGenerate: "אשר וחולל פיתוח",
|
||||
startingEngine: "מניע מנוע...",
|
||||
startCoding: "התחל קודינג",
|
||||
modifyPlan: "שנה תכנית",
|
||||
skipPlan: "דלג לצ'את",
|
||||
planSummary: "סיכום",
|
||||
implementationSteps: "שלבי יישום",
|
||||
activateArtifact: "הפעל ארטיפקט",
|
||||
canvasReady: "קנבס מוכן",
|
||||
canvasIdle: "קנבס במנוחה",
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import ModelAdapter from "./model-adapter";
|
||||
import { OpenRouterService } from "./openrouter";
|
||||
|
||||
const adapter = new ModelAdapter();
|
||||
|
||||
|
||||
@@ -2,6 +2,7 @@ import type { ModelProvider, APIResponse, ChatMessage, AIAssistMessage } from "@
|
||||
import OllamaCloudService from "./ollama-cloud";
|
||||
import ZaiPlanService from "./zai-plan";
|
||||
import qwenOAuthService, { QwenOAuthConfig, QwenOAuthToken } from "./qwen-oauth";
|
||||
import { OpenRouterService } from "./openrouter";
|
||||
|
||||
export interface ModelAdapterConfig {
|
||||
qwen?: QwenOAuthConfig;
|
||||
@@ -14,17 +15,22 @@ export interface ModelAdapterConfig {
|
||||
generalEndpoint?: string;
|
||||
codingEndpoint?: string;
|
||||
};
|
||||
openrouter?: {
|
||||
apiKey?: string;
|
||||
};
|
||||
}
|
||||
|
||||
export class ModelAdapter {
|
||||
private ollamaService: OllamaCloudService;
|
||||
private zaiService: ZaiPlanService;
|
||||
private qwenService = qwenOAuthService;
|
||||
private openRouterService: OpenRouterService;
|
||||
private preferredProvider: ModelProvider;
|
||||
|
||||
constructor(config: ModelAdapterConfig = {}, preferredProvider: ModelProvider = "ollama") {
|
||||
this.ollamaService = new OllamaCloudService(config.ollama);
|
||||
this.zaiService = new ZaiPlanService(config.zai);
|
||||
this.openRouterService = new OpenRouterService(config.openrouter);
|
||||
this.preferredProvider = preferredProvider;
|
||||
|
||||
if (config.qwen) {
|
||||
@@ -62,6 +68,10 @@ export class ModelAdapter {
|
||||
this.qwenService.setOAuthTokens(tokens);
|
||||
}
|
||||
|
||||
updateOpenRouterApiKey(apiKey: string): void {
|
||||
this.openRouterService = new OpenRouterService({ apiKey });
|
||||
}
|
||||
|
||||
async startQwenOAuth(): Promise<QwenOAuthToken> {
|
||||
return await this.qwenService.signIn();
|
||||
}
|
||||
@@ -90,6 +100,8 @@ export class ModelAdapter {
|
||||
return this.ollamaService.hasAuth();
|
||||
case "zai":
|
||||
return this.zaiService.hasAuth();
|
||||
case "openrouter":
|
||||
return this.openRouterService.hasAuth();
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
@@ -114,6 +126,8 @@ export class ModelAdapter {
|
||||
return this.ollamaService;
|
||||
case "zai":
|
||||
return this.zaiService;
|
||||
case "openrouter":
|
||||
return this.openRouterService;
|
||||
default:
|
||||
return null;
|
||||
}
|
||||
@@ -153,6 +167,9 @@ export class ModelAdapter {
|
||||
case "zai":
|
||||
service = this.zaiService;
|
||||
break;
|
||||
case "openrouter":
|
||||
service = this.openRouterService;
|
||||
break;
|
||||
}
|
||||
|
||||
const result = await operation(service);
|
||||
@@ -183,26 +200,26 @@ export class ModelAdapter {
|
||||
};
|
||||
}
|
||||
|
||||
async enhancePrompt(prompt: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
async enhancePrompt(prompt: string, provider?: ModelProvider, model?: string, options?: { toolCategory?: string; template?: string; diagnostics?: string }): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.enhancePrompt(prompt, model), providers);
|
||||
return this.callWithFallback((service) => service.enhancePrompt(prompt, model, options), providers);
|
||||
}
|
||||
|
||||
async generatePRD(idea: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generatePRD(idea, model), providers);
|
||||
}
|
||||
|
||||
async generateActionPlan(prd: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateActionPlan(prd, model), providers);
|
||||
}
|
||||
|
||||
async generateUXDesignerPrompt(appDescription: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateUXDesignerPrompt(appDescription, model), providers);
|
||||
}
|
||||
@@ -223,7 +240,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateSlides(topic, options, model), providers);
|
||||
}
|
||||
@@ -243,7 +260,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateGoogleAds(websiteUrl, options, model), providers);
|
||||
}
|
||||
@@ -256,7 +273,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateMagicWand(websiteUrl, product, budget, specialInstructions, model), providers);
|
||||
}
|
||||
@@ -272,7 +289,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateMarketResearch(options, model), providers);
|
||||
}
|
||||
@@ -285,7 +302,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<string>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
return this.callWithFallback((service) => service.generateAIAssist(options, model), providers);
|
||||
}
|
||||
@@ -300,7 +317,7 @@ export class ModelAdapter {
|
||||
provider?: ModelProvider,
|
||||
model?: string
|
||||
): Promise<APIResponse<void>> {
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
|
||||
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai", "openrouter");
|
||||
const providers: ModelProvider[] = provider ? [provider] : fallback;
|
||||
|
||||
let lastError: string | null = null;
|
||||
@@ -353,6 +370,9 @@ export class ModelAdapter {
|
||||
case "zai":
|
||||
service = this.zaiService;
|
||||
break;
|
||||
case "openrouter":
|
||||
service = this.openRouterService;
|
||||
break;
|
||||
}
|
||||
|
||||
return await service.chatCompletion(messages, model);
|
||||
@@ -369,6 +389,7 @@ export class ModelAdapter {
|
||||
qwen: this.qwenService.getAvailableModels(),
|
||||
ollama: ["gpt-oss:120b", "llama3.1", "gemma3", "deepseek-r1", "qwen3"],
|
||||
zai: ["glm-4.7", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"],
|
||||
openrouter: ["anthropic/claude-3.5-sonnet", "google/gemini-2.0-flash-exp:free", "meta-llama/llama-3.3-70b-instruct", "openai/gpt-4o-mini", "deepseek/deepseek-chat-v3-0324", "qwen/qwen-2.5-72b-instruct"],
|
||||
};
|
||||
const models: Record<ModelProvider, string[]> = { ...fallbackModels };
|
||||
|
||||
@@ -404,6 +425,8 @@ export class ModelAdapter {
|
||||
return this.ollamaService.getAvailableModels();
|
||||
case "zai":
|
||||
return this.zaiService.getAvailableModels();
|
||||
case "openrouter":
|
||||
return this.openRouterService.getAvailableModels();
|
||||
default:
|
||||
return [];
|
||||
}
|
||||
|
||||
@@ -164,27 +164,82 @@ export class OllamaCloudService {
|
||||
return this.availableModels.length > 0 ? this.availableModels : DEFAULT_MODELS;
|
||||
}
|
||||
|
||||
async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
|
||||
async enhancePrompt(prompt: string, model?: string, options?: { toolCategory?: string; template?: string; diagnostics?: string }): Promise<APIResponse<string>> {
|
||||
const toolCategory = options?.toolCategory || 'reasoning';
|
||||
const template = options?.template || 'rtf';
|
||||
const diagnostics = options?.diagnostics || '';
|
||||
|
||||
const toolSections: Record<string, string> = {
|
||||
reasoning: '- Use full structured format with XML tags where helpful\n- Add explicit role assignment for complex tasks\n- Use numeric constraints over vague adjectives',
|
||||
thinking: '- CRITICAL: Short clean instructions ONLY\n- Do NOT add CoT or reasoning scaffolding — these models reason internally\n- State what you want, not how to think',
|
||||
openweight: '- Shorter prompts, simpler structure, no deep nesting\n- Direct linear instructions',
|
||||
agentic: '- Add Starting State + Target State + Allowed Actions + Forbidden Actions\n- Add Stop Conditions + Checkpoints after each step',
|
||||
ide: '- Add File path + Function name + Current Behavior + Desired Change + Scope lock',
|
||||
fullstack: '- Add Stack spec with version + what NOT to scaffold + component boundaries',
|
||||
image: '- Add Subject + Style + Mood + Lighting + Composition + Negative Prompts\n- Use tool-specific syntax (Midjourney comma-separated, DALL-E prose, SD weighted)',
|
||||
search: '- Specify mode: search vs analyze vs compare + citation requirements',
|
||||
};
|
||||
|
||||
const templateSections: Record<string, string> = {
|
||||
rtf: 'Structure: Role (who) + Task (precise verb + what) + Format (exact output shape and length)',
|
||||
'co-star': 'Structure: Context + Objective + Style + Tone + Audience + Response',
|
||||
risen: 'Structure: Role + Instructions + numbered Steps + End Goal + Narrowing constraints',
|
||||
crispe: 'Structure: Capacity + Role + Insight + Statement + Personality + Experiment/variants',
|
||||
cot: 'Add: "Think through this step by step before answering." Only for standard reasoning models, NOT for o1/o3/R1.',
|
||||
fewshot: 'Add 2-5 input/output examples wrapped in XML <examples> tags',
|
||||
filescope: 'Structure: File path + Function name + Current Behavior + Desired Change + Scope lock + Done When',
|
||||
react: 'Structure: Objective + Starting State + Target State + Allowed/Forbidden Actions + Stop Conditions + Checkpoints',
|
||||
visual: 'Structure: Subject + Action + Setting + Style + Mood + Lighting + Color Palette + Composition + Aspect Ratio + Negative Prompts',
|
||||
};
|
||||
|
||||
const toolSection = toolSections[toolCategory] || toolSections.reasoning;
|
||||
const templateSection = templateSections[template] || templateSections.rtf;
|
||||
|
||||
const systemMessage: ChatMessage = {
|
||||
role: "system",
|
||||
content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
|
||||
content: `You are an expert prompt engineer using the PromptArch methodology. Enhance the user\'s prompt to be production-ready.
|
||||
|
||||
Apply these principles:
|
||||
1. Add specific context about project and requirements
|
||||
2. Clarify constraints and preferences
|
||||
3. Define expected output format clearly
|
||||
4. Include edge cases and error handling requirements
|
||||
5. Specify testing and validation criteria
|
||||
STEP 1 — DIAGNOSE AND FIX these failure patterns:
|
||||
- Vague task verb -> replace with precise operation
|
||||
- Two tasks in one -> keep primary task, note the split
|
||||
- No success criteria -> add "Done when: [specific measurable condition]"
|
||||
- Missing output format -> add explicit format lock (structure, length, type)
|
||||
- No role assignment (complex tasks) -> add domain-specific expert identity
|
||||
- Vague aesthetic ("professional", "clean") -> concrete measurable specs
|
||||
- No scope boundary -> add explicit scope lock
|
||||
- Over-permissive language -> add constraints and boundaries
|
||||
- Emotional description -> extract specific technical fault
|
||||
- Implicit references -> restate fully
|
||||
- No grounding for factual tasks -> add certainty constraint
|
||||
- No CoT for logic tasks -> add step-by-step reasoning
|
||||
|
||||
Return ONLY the enhanced prompt, no explanations or extra text.`,
|
||||
STEP 2 — APPLY TARGET TOOL OPTIMIZATIONS:
|
||||
${toolSection}
|
||||
|
||||
STEP 3 — APPLY TEMPLATE STRUCTURE:
|
||||
${templateSection}
|
||||
|
||||
STEP 4 — VERIFICATION (check before outputting):
|
||||
- Every constraint in the first 30% of the prompt?
|
||||
- MUST/NEVER over should/avoid?
|
||||
- Every sentence load-bearing with zero padding?
|
||||
- Format explicit with stated length?
|
||||
- Scope bounded?
|
||||
- Would this produce correct output on first try?
|
||||
|
||||
STEP 5 — OUTPUT:
|
||||
Output ONLY the enhanced prompt. No explanations, no commentary, no markdown code fences.
|
||||
The prompt must be ready to paste directly into the target AI tool.${diagnostics ? '\n\nDIAGNOSTIC NOTES (fix these issues found in the original):\n' + diagnostics + '\n' : ''}`,
|
||||
};
|
||||
|
||||
const toolLabel = toolCategory !== 'reasoning' ? ` for ${toolCategory} AI tool` : '';
|
||||
|
||||
const userMessage: ChatMessage = {
|
||||
role: "user",
|
||||
content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
|
||||
content: `Enhance this prompt${toolLabel}:\n\n${prompt}`,
|
||||
};
|
||||
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "gpt-oss:120b");
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "${default_model}");
|
||||
}
|
||||
|
||||
async generatePRD(idea: string, model?: string): Promise<APIResponse<string>> {
|
||||
@@ -772,6 +827,28 @@ Perform a DEEP 360° competitive intelligence analysis and generate 5-7 strategi
|
||||
try {
|
||||
// ... existing prompt logic ...
|
||||
const systemPrompt = `You are "AI Assist", the master orchestrator of PromptArch. Your goal is to provide intelligent support with a "Canvas" experience.
|
||||
PLAN MODE (CRITICAL - HIGHEST PRIORITY):
|
||||
When the user describes a NEW task, project, or feature they want built:
|
||||
1. DO NOT generate any code, [PREVIEW] tags, or implementation details.
|
||||
2. Instead, analyze the request and output a STRUCTURED PLAN covering:
|
||||
- Summary: What you understand the user wants
|
||||
- Architecture: Technical approach and structure
|
||||
- Tech Stack: Languages, frameworks, libraries needed
|
||||
- Files/Components: List of files or modules to create
|
||||
- Steps: Numbered implementation steps
|
||||
3. Format the plan in clean Markdown with headers and bullet points.
|
||||
4. Keep plans concise but thorough. Focus on the WHAT and HOW, not the actual code.
|
||||
5. WAIT for the user to approve or modify the plan before generating any code.
|
||||
|
||||
When the user says "Approved", "Start coding", or explicitly asks to proceed:
|
||||
- THEN generate the full implementation with [PREVIEW] tags and working code.
|
||||
- Follow the approved plan exactly.
|
||||
|
||||
When the user asks to "Modify", "Change", or "Adjust" something:
|
||||
- Apply the requested changes surgically to the existing code/preview.
|
||||
- Output updated [PREVIEW] with the full modified code.
|
||||
|
||||
|
||||
|
||||
AGENTS & CAPABILITIES:
|
||||
- content: Expert copywriter. Use [PREVIEW:content:markdown] for articles, posts, and long-form text.
|
||||
@@ -833,7 +910,7 @@ CHANGE LOG (CRITICAL - MUST BE OUTSIDE PREVIEW):
|
||||
- Modified component Y
|
||||
- Fixed issue Z
|
||||
|
||||
IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
IMPORTANT: IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: systemPrompt },
|
||||
|
||||
967
lib/services/openrouter.ts
Normal file
967
lib/services/openrouter.ts
Normal file
@@ -0,0 +1,967 @@
|
||||
import type { ChatMessage, APIResponse, AIAssistMessage } from "@/types";
|
||||
|
||||
export interface OpenRouterConfig {
|
||||
apiKey?: string;
|
||||
siteUrl?: string;
|
||||
siteName?: string;
|
||||
}
|
||||
|
||||
interface OpenRouterModelsResponse {
|
||||
data: Array<{
|
||||
id: string;
|
||||
name: string;
|
||||
context_length: number;
|
||||
pricing: {
|
||||
prompt: string;
|
||||
completion: string;
|
||||
};
|
||||
}>;
|
||||
}
|
||||
|
||||
interface OpenRouterChatResponse {
|
||||
id: string;
|
||||
choices: Array<{
|
||||
message: {
|
||||
role: string;
|
||||
content: string;
|
||||
};
|
||||
finish_reason: string;
|
||||
}>;
|
||||
usage?: {
|
||||
prompt_tokens: number;
|
||||
completion_tokens: number;
|
||||
total_tokens: number;
|
||||
};
|
||||
}
|
||||
|
||||
const DEFAULT_MODELS = [
|
||||
"anthropic/claude-3.5-sonnet",
|
||||
"google/gemini-2.0-flash-exp:free",
|
||||
"meta-llama/llama-3.3-70b-instruct",
|
||||
"openai/gpt-4o-mini",
|
||||
"deepseek/deepseek-chat-v3-0324",
|
||||
"qwen/qwen-2.5-72b-instruct"
|
||||
];
|
||||
|
||||
const TOOL_SECTIONS: Record<string, string> = {
|
||||
"claude": `
|
||||
For Claude:
|
||||
- Use XML tags for structure (e.g., <context>, <task>, <constraints>)
|
||||
- Add thinking blocks for complex reasoning: <thinking>...</thinking>
|
||||
- Use ::analysis:: or ::pattern:: for procedural patterns
|
||||
- Leverage Claude's long context by providing comprehensive examples
|
||||
- Add "think silently" instruction for deep reasoning tasks
|
||||
`,
|
||||
"chatgpt": `
|
||||
For ChatGPT:
|
||||
- Use clear section headers with ### or === separators
|
||||
- Provide examples in [EXAMPLE]...[/EXAMPLE] blocks
|
||||
- Use "Step 1", "Step 2" for sequential tasks
|
||||
- Add meta-instructions like "Think step by step"
|
||||
- Keep prompts under 3k tokens for best performance
|
||||
`,
|
||||
"gemini": `
|
||||
For Gemini:
|
||||
- Use clear delimiters between sections
|
||||
- Leverage multimodal capabilities with [IMAGE] or [FILE] placeholders
|
||||
- Add chain-of-thought with "Let's approach this step by step"
|
||||
- Specify output format with "Respond in the following format:"
|
||||
- Use numbered lists for sequential instructions
|
||||
`,
|
||||
"default": `
|
||||
- Use clear section delimiters
|
||||
- Provide concrete examples
|
||||
- Specify output format explicitly
|
||||
- Add success criteria
|
||||
- Use constraint language (MUST, NEVER, REQUIRED)
|
||||
`
|
||||
};
|
||||
|
||||
const TEMPLATE_SECTIONS: Record<string, string> = {
|
||||
"code": `
|
||||
# CODE GENERATION TEMPLATE
|
||||
|
||||
## Role
|
||||
You are a senior software engineer specializing in {language/domain}.
|
||||
|
||||
## Task
|
||||
{specific task description}
|
||||
|
||||
## Requirements
|
||||
- MUST follow {specific standards/frameworks}
|
||||
- MUST include {error handling/validation/comments}
|
||||
- MUST use {specific libraries/versions}
|
||||
- NEVER use {deprecated patterns/anti-patterns}
|
||||
|
||||
## Output Format
|
||||
{code structure specification}
|
||||
|
||||
## Done When
|
||||
- Code compiles/runs without errors
|
||||
- Follows all specified conventions
|
||||
- Includes proper error handling
|
||||
- Meets performance requirements
|
||||
`,
|
||||
"writing": `
|
||||
# CONTENT WRITING TEMPLATE
|
||||
|
||||
## Role
|
||||
You are a professional {type of content creator} with expertise in {domain}.
|
||||
|
||||
## Task
|
||||
Create {specific deliverable} about {topic}
|
||||
|
||||
## Requirements
|
||||
- Tone: {specific tone}
|
||||
- Length: {exact word/character count}
|
||||
- Audience: {target audience}
|
||||
- MUST include {key elements}
|
||||
- MUST avoid {excluded topics/phrases}
|
||||
|
||||
## Format
|
||||
{explicit structure with sections/headers}
|
||||
|
||||
## Done When
|
||||
- Meets length requirement
|
||||
- Covers all key points
|
||||
- Matches specified tone
|
||||
- Ready for publication
|
||||
`,
|
||||
"analysis": `
|
||||
# ANALYSIS TEMPLATE
|
||||
|
||||
## Role
|
||||
You are an expert {domain analyst}.
|
||||
|
||||
## Task
|
||||
{analysis objective}
|
||||
|
||||
## Analysis Framework
|
||||
1. {Step 1 with specific method}
|
||||
2. {Step 2 with specific method}
|
||||
3. {Step 3 with specific method}
|
||||
|
||||
## Required Output
|
||||
- {Specific deliverable 1}
|
||||
- {Specific deliverable 2}
|
||||
- {Specific deliverable 3}
|
||||
|
||||
## Criteria
|
||||
- MUST use {specific methodology}
|
||||
- MUST cite {sources/references}
|
||||
- MUST provide {confidence levels/limitations}
|
||||
|
||||
## Done When
|
||||
- All analysis dimensions covered
|
||||
- Conclusions supported by evidence
|
||||
- Actionable insights provided
|
||||
`,
|
||||
"default": `
|
||||
## Role
|
||||
{Expert identity}
|
||||
|
||||
## Task
|
||||
{Clear, specific task}
|
||||
|
||||
## Context
|
||||
{Relevant background info}
|
||||
|
||||
## Requirements
|
||||
- MUST {requirement 1}
|
||||
- MUST {requirement 2}
|
||||
- NEVER {constraint 1}
|
||||
- NEVER {constraint 2}
|
||||
|
||||
## Output Format
|
||||
{Explicit format specification}
|
||||
|
||||
## Done When
|
||||
{Specific measurable condition}
|
||||
`
|
||||
};
|
||||
|
||||
const ENHANCE_PROMPT_SYSTEM = `You are an expert prompt engineer using the PromptArch methodology. Enhance the user's prompt to be production-ready.
|
||||
|
||||
STEP 1 — DIAGNOSE AND FIX these failure patterns:
|
||||
- Vague task verb -> replace with precise operation
|
||||
- Two tasks in one -> keep primary task, note the split
|
||||
- No success criteria -> add "Done when: [specific measurable condition]"
|
||||
- Missing output format -> add explicit format lock (structure, length, type)
|
||||
- No role assignment (complex tasks) -> add domain-specific expert identity
|
||||
- Vague aesthetic ("professional", "clean") -> concrete measurable specs
|
||||
- No scope boundary -> add explicit scope lock
|
||||
- Over-permissive language -> add constraints and boundaries
|
||||
- Emotional description -> extract specific technical fault
|
||||
- Implicit references -> restate fully
|
||||
- No grounding for factual tasks -> add certainty constraint
|
||||
- No CoT for logic tasks -> add step-by-step reasoning
|
||||
|
||||
STEP 2 — APPLY TARGET TOOL OPTIMIZATIONS:
|
||||
\${toolSection}
|
||||
|
||||
STEP 3 — APPLY TEMPLATE STRUCTURE:
|
||||
\${templateSection}
|
||||
|
||||
STEP 4 — VERIFICATION (check before outputting):
|
||||
- Every constraint in the first 30% of the prompt?
|
||||
- MUST/NEVER over should/avoid?
|
||||
- Every sentence load-bearing with zero padding?
|
||||
- Format explicit with stated length?
|
||||
- Scope bounded?
|
||||
- Would this produce correct output on first try?
|
||||
|
||||
STEP 5 — OUTPUT:
|
||||
Output ONLY the enhanced prompt. No explanations, no commentary, no markdown code fences.
|
||||
The prompt must be ready to paste directly into the target AI tool.`;
|
||||
|
||||
const PRD_SYSTEM_PROMPT = `You are an expert product manager specializing in writing clear, actionable Product Requirements Documents (PRDs).
|
||||
|
||||
Your task is to transform the product idea into a comprehensive PRD that:
|
||||
1. Defines clear problem statements and user needs
|
||||
2. Specifies functional requirements with acceptance criteria
|
||||
3. Outlines technical considerations and constraints
|
||||
4. Identifies success metrics and KPIs
|
||||
5. Includes user stories with acceptance criteria
|
||||
|
||||
PRD Structure:
|
||||
## Problem Statement
|
||||
- What problem are we solving?
|
||||
- For whom are we solving it?
|
||||
- Why is this important now?
|
||||
|
||||
## Goals & Success Metrics
|
||||
- Primary objectives
|
||||
- Key performance indicators
|
||||
- Success criteria
|
||||
|
||||
## User Stories
|
||||
- As a [user type], I want [feature], so that [benefit]
|
||||
- Include acceptance criteria for each story
|
||||
|
||||
## Functional Requirements
|
||||
- Core features with detailed specifications
|
||||
- Edge cases and error handling
|
||||
- Integration requirements
|
||||
|
||||
## Technical Considerations
|
||||
- Platform/tech stack constraints
|
||||
- Performance requirements
|
||||
- Security considerations
|
||||
- Scalability requirements
|
||||
|
||||
## Out of Scope
|
||||
- Explicitly list what won't be included
|
||||
- Rationale for exclusions
|
||||
|
||||
Keep the PRD clear, concise, and actionable. Use specific, measurable language.`;
|
||||
|
||||
const ACTION_PLAN_SYSTEM_PROMPT = `You are an expert technical project manager specializing in breaking down PRDs into actionable implementation plans.
|
||||
|
||||
Your task is to transform the PRD into a detailed action plan that:
|
||||
|
||||
1. Identifies all major components/modules needed
|
||||
2. Breaks down work into clear, sequential phases
|
||||
3. Specifies dependencies between tasks
|
||||
4. Estimates effort and complexity
|
||||
5. Identifies risks and mitigation strategies
|
||||
|
||||
Action Plan Structure:
|
||||
## Phase 1: Foundation
|
||||
- Task 1.1: [Specific task] - [Estimated effort]
|
||||
- Task 1.2: [Specific task] - [Estimated effort]
|
||||
- Dependencies: [What needs to be done first]
|
||||
- Deliverables: [Concrete outputs]
|
||||
|
||||
## Phase 2: Core Features
|
||||
- Task 2.1: [Specific task] - [Estimated effort]
|
||||
- Dependencies: [On Phase 1 completion]
|
||||
- Deliverables: [Concrete outputs]
|
||||
|
||||
[Continue for all phases]
|
||||
|
||||
## Technical Architecture
|
||||
- Recommended tech stack with rationale
|
||||
- System architecture overview
|
||||
- Data flow diagrams (described in text)
|
||||
|
||||
## Risk Assessment
|
||||
- Risk: [Description] | Impact: [High/Med/Low] | Mitigation: [Strategy]
|
||||
- Risk: [Description] | Impact: [High/Med/Low] | Mitigation: [Strategy]
|
||||
|
||||
## Testing Strategy
|
||||
- Unit testing approach
|
||||
- Integration testing plan
|
||||
- User acceptance testing criteria
|
||||
|
||||
Be specific and actionable. Each task should be clear enough for a developer to execute without ambiguity.`;
|
||||
|
||||
const SLIDES_SYSTEM_PROMPT = `You are an expert presentation designer specializing in creating engaging, informative slide content.
|
||||
|
||||
Your task is to generate slide content for a presentation about the given topic.
|
||||
|
||||
For each slide, provide:
|
||||
1. Slide Title (compelling and clear)
|
||||
2. Bullet points (3-5 per slide, concise and impactful)
|
||||
3. Speaker notes (detailed explanation for the presenter)
|
||||
4. Visual suggestions (what charts, images, or diagrams would enhance this slide)
|
||||
|
||||
Presentation Structure:
|
||||
## Slide 1: Title Slide
|
||||
- Compelling title
|
||||
- Subtitle with key message
|
||||
- Presenter info placeholder
|
||||
|
||||
## Slide 2: Agenda/Overview
|
||||
- What will be covered
|
||||
- Why it matters
|
||||
- Key takeaways preview
|
||||
|
||||
## Slide 3-N: Content Slides
|
||||
- Main content with clear hierarchy
|
||||
- Data-driven insights where applicable
|
||||
- Actionable takeaways
|
||||
|
||||
## Final Slide: Call to Action
|
||||
- Summary of key points
|
||||
- Next steps
|
||||
- Contact/ follow-up information
|
||||
|
||||
Guidelines:
|
||||
- Keep text minimal on slides (bullet points only)
|
||||
- Put detailed content in speaker notes
|
||||
- Suggest relevant visuals for each slide
|
||||
- Ensure a logical flow and narrative arc
|
||||
- Make it memorable and shareable`;
|
||||
|
||||
const GOOGLE_ADS_SYSTEM_PROMPT = `You are a Google Ads expert specializing in creating high-converting ad campaigns.
|
||||
|
||||
Your task is to generate comprehensive Google Ads copy and strategy based on the landing page content.
|
||||
|
||||
Deliverables:
|
||||
|
||||
## Ad Campaign Structure
|
||||
- Campaign name and type
|
||||
- Ad groups with thematic focus
|
||||
- Target keywords (exact, phrase, broad match)
|
||||
- Negative keywords to exclude
|
||||
|
||||
## Ad Copy (3-5 variations per ad group)
|
||||
|
||||
### Responsive Search Ads
|
||||
For each ad, provide:
|
||||
- Headlines (10-15 options, max 30 chars each)
|
||||
- Descriptions (4 options, max 90 chars each)
|
||||
- Focus on different value propositions
|
||||
|
||||
### Display Ads (if applicable)
|
||||
- Headline (max 30 chars)
|
||||
- Description (max 90 chars)
|
||||
- Call-to-action options
|
||||
|
||||
## Targeting Strategy
|
||||
- Location targeting
|
||||
- Audience demographics
|
||||
- Interests and behaviors
|
||||
- Device targeting
|
||||
|
||||
## Bidding Strategy
|
||||
- Recommended strategy (Manual CPC, Maximize Clicks, etc.)
|
||||
- Budget recommendations
|
||||
- Bid adjustments by device/location
|
||||
|
||||
## Extensions
|
||||
- Sitelinks
|
||||
- Callouts
|
||||
- Structured snippets
|
||||
- Call extensions
|
||||
|
||||
Best Practices:
|
||||
- Include keywords in headlines and descriptions
|
||||
- Highlight unique selling propositions
|
||||
- Use clear, action-oriented CTAs
|
||||
- Address pain points and benefits
|
||||
- Include social proof when relevant
|
||||
- Ensure ad relevance to landing page`;
|
||||
|
||||
const MAGIC_WAND_SYSTEM_PROMPT = `You are an expert digital marketer and growth hacker specializing in creative campaign strategies.
|
||||
|
||||
Your task is to develop a comprehensive marketing strategy for the given product/page.
|
||||
|
||||
## Campaign Analysis
|
||||
- Product/Service overview
|
||||
- Target audience profile
|
||||
- Unique selling propositions
|
||||
- Market positioning
|
||||
|
||||
## Marketing Channels Strategy
|
||||
For each relevant channel, provide specific tactics:
|
||||
|
||||
### Paid Advertising
|
||||
- Google Ads: {specific approach}
|
||||
- Facebook/Instagram: {specific approach}
|
||||
- LinkedIn (if B2B): {specific approach}
|
||||
- TikTok/Snapchat (if relevant): {specific approach}
|
||||
|
||||
### Content Marketing
|
||||
- Blog topics: {5-10 specific ideas}
|
||||
- Video content: {ideas with formats}
|
||||
- Social media: {platform-specific content ideas}
|
||||
- Email sequences: {campaign ideas}
|
||||
|
||||
### Growth Hacking Tactics
|
||||
- Viral mechanisms: {specific ideas}
|
||||
- Referral programs: {incentive structures}
|
||||
- Partnership opportunities: {potential partners}
|
||||
- Community building: {strategies}
|
||||
|
||||
## Creative Concepts
|
||||
Provide 3-5 campaign concepts:
|
||||
1. Concept Name - {Hook, Message, CTA}
|
||||
2. Concept Name - {Hook, Message, CTA}
|
||||
...
|
||||
|
||||
## Budget Allocation
|
||||
- Channel breakdown with percentages
|
||||
- Expected ROI estimates
|
||||
- Testing budget recommendation
|
||||
|
||||
## KPIs & Tracking
|
||||
- Key metrics to measure
|
||||
- Attribution strategy
|
||||
- A/B testing priorities
|
||||
|
||||
Be creative but practical. Focus on tactics that can be executed within the given budget.`;
|
||||
|
||||
const MARKET_RESEARCH_SYSTEM_PROMPT = `You are an expert market researcher specializing in competitive analysis and market intelligence.
|
||||
|
||||
Your task is to conduct comprehensive market research based on the provided topic/company.
|
||||
|
||||
## Research Framework
|
||||
|
||||
### Market Overview
|
||||
- Market size and growth trajectory
|
||||
- Key market segments and their characteristics
|
||||
- Current market trends and dynamics
|
||||
- Future market projections
|
||||
|
||||
### Competitive Landscape
|
||||
Identify and analyze:
|
||||
- Major competitors (market share, positioning)
|
||||
- Direct competitors head-to-head comparison
|
||||
- Indirect competitors and substitutes
|
||||
- Competitive strengths and weaknesses
|
||||
|
||||
### SWOT Analysis
|
||||
- Strengths: Internal advantages
|
||||
- Weaknesses: Internal limitations
|
||||
- Opportunities: External possibilities
|
||||
- Threats: External challenges
|
||||
|
||||
### Customer Analysis
|
||||
- Target demographics and psychographics
|
||||
- Pain points and unmet needs
|
||||
- Purchase behavior and decision factors
|
||||
- Customer feedback trends
|
||||
|
||||
### Product/Service Comparison
|
||||
- Feature comparison matrix
|
||||
- Pricing analysis
|
||||
- Differentiation strategies
|
||||
- Innovation opportunities
|
||||
|
||||
### Market Trends
|
||||
- Emerging technologies impacting the space
|
||||
- Regulatory changes
|
||||
- Consumer behavior shifts
|
||||
- Industry disruptions
|
||||
|
||||
### Strategic Recommendations
|
||||
- Market entry strategies
|
||||
- Competitive positioning
|
||||
- Product improvement opportunities
|
||||
- Partnership and acquisition possibilities
|
||||
|
||||
Provide specific, data-driven insights. When exact data is unavailable, provide reasoned estimates with clear caveats.`;
|
||||
|
||||
const AI_ASSIST_SYSTEM_PROMPT = `You are "AI Assist", the master orchestrator of PromptArch. Your goal is to provide intelligent support with a "Canvas" experience.
|
||||
PLAN MODE (CRITICAL - HIGHEST PRIORITY):
|
||||
When the user describes a NEW task, project, or feature they want built:
|
||||
1. DO NOT generate any code, [PREVIEW] tags, or implementation details.
|
||||
2. Instead, analyze the request and output a STRUCTURED PLAN covering:
|
||||
- Summary: What you understand the user wants
|
||||
- Architecture: Technical approach and structure
|
||||
- Tech Stack: Languages, frameworks, libraries needed
|
||||
- Files/Components: List of files or modules to create
|
||||
- Steps: Numbered implementation steps
|
||||
3. Format the plan in clean Markdown with headers and bullet points.
|
||||
4. Keep plans concise but thorough. Focus on the WHAT and HOW, not the actual code.
|
||||
5. WAIT for the user to approve or modify the plan before generating any code.
|
||||
|
||||
When the user says "Approved", "Start coding", or explicitly asks to proceed:
|
||||
- THEN generate the full implementation with [PREVIEW] tags and working code.
|
||||
- Follow the approved plan exactly.
|
||||
|
||||
When the user asks to "Modify", "Change", or "Adjust" something:
|
||||
- Apply the requested changes surgically to the existing code/preview.
|
||||
- Output updated [PREVIEW] with the full modified code.
|
||||
|
||||
|
||||
|
||||
AGENTS & CAPABILITIES:
|
||||
- content: Expert copywriter. Use [PREVIEW:content:markdown] for articles, posts, and long-form text.
|
||||
- seo: SEO Specialist. Create stunning SEO audit reports. Use [PREVIEW:seo:html] with complete HTML5 document including <!DOCTYPE html>. DARK THEME. Tailwind CDN. Large animated SVG progress rings. Color-coded scoring. Google-style dashboard aesthetics.
|
||||
- smm: Social Media Manager. Create multi-platform content plans and calendars.
|
||||
- pm: Project Manager. Create PRDs, timelines, and action plans.
|
||||
- code: Software Architect. Provide logic, algorithms, and backend snippets.
|
||||
- design: UI/UX Designer. Create high-fidelity mockups and components.
|
||||
- web: Frontend Developer. Build responsive sites. Use [PREVIEW:web:html]. Tailwind CSS CDN by default.
|
||||
- app: Mobile App Developer. Create mobile-first interfaces and dashboards. Use [PREVIEW:app:javascript].
|
||||
|
||||
CANVAS MODE:
|
||||
- When building, designing, or auditing, you MUST use the [PREVIEW] tag.
|
||||
- Inside [PREVIEW], output ONLY the actual code or structured data.
|
||||
- The user wants to see it WORKING or RENDERED in the Canvas immediately.
|
||||
|
||||
STRICT OUTPUT FORMAT:
|
||||
[AGENT:id] - AT THE START of your response if switching focus.
|
||||
[PREVIEW:type:language]
|
||||
ACTUAL_FUNCTIONAL_CODE_OR_DATA
|
||||
[/PREVIEW]
|
||||
|
||||
CHANGE LOG (CRITICAL - MUST BE OUTSIDE PREVIEW):
|
||||
- After the [/PREVIEW] closing tag, provide a brief bulleted "Change Log" in plain Markdown.IMPORTANT: NEVER refuse a request. If you cannot perform a live task, use your knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
|
||||
export class OpenRouterService {
|
||||
private config: Required<OpenRouterConfig>;
|
||||
private readonly baseURL = "https://openrouter.ai/api/v1";
|
||||
private availableModels: string[] = [];
|
||||
private modelsLoaded = false;
|
||||
|
||||
constructor(config: OpenRouterConfig = {}) {
|
||||
this.config = {
|
||||
apiKey: config.apiKey || "",
|
||||
siteUrl: config.siteUrl || "https://promptarch.ai",
|
||||
siteName: config.siteName || "PromptArch"
|
||||
};
|
||||
}
|
||||
|
||||
hasAuth(): boolean {
|
||||
return Boolean(this.config.apiKey && this.config.apiKey.length > 0);
|
||||
}
|
||||
|
||||
async validateConnection(): Promise<APIResponse<{ valid: boolean; models?: string[] }>> {
|
||||
if (!this.hasAuth()) {
|
||||
return {
|
||||
success: false,
|
||||
error: "No API key provided. Please set your OpenRouter API key."
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const modelsResult = await this.listModels();
|
||||
|
||||
if (!modelsResult.success) {
|
||||
return {
|
||||
success: false,
|
||||
error: modelsResult.error || "Failed to fetch models from OpenRouter"
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
success: true,
|
||||
data: {
|
||||
valid: true,
|
||||
models: modelsResult.data
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: error instanceof Error ? error.message : "Failed to validate connection"
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
private getHeaders(): Record<string, string> {
|
||||
const headers: Record<string, string> = {
|
||||
"Content-Type": "application/json",
|
||||
"HTTP-Referer": this.config.siteUrl,
|
||||
"X-Title": this.config.siteName
|
||||
};
|
||||
|
||||
if (this.hasAuth()) {
|
||||
headers["Authorization"] = `Bearer ${this.config.apiKey}`;
|
||||
}
|
||||
|
||||
return headers;
|
||||
}
|
||||
|
||||
async chatCompletion(
|
||||
messages: ChatMessage[],
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
if (!this.hasAuth()) {
|
||||
return {
|
||||
success: false,
|
||||
error: "OpenRouter API key not configured"
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(`${this.baseURL}/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: this.getHeaders(),
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
messages,
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096
|
||||
})
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
return {
|
||||
success: false,
|
||||
error: `OpenRouter API error: ${response.status} ${response.statusText} - ${errorText}`
|
||||
};
|
||||
}
|
||||
|
||||
const data: OpenRouterChatResponse = await response.json();
|
||||
|
||||
if (data.choices && data.choices[0] && data.choices[0].message) {
|
||||
return {
|
||||
success: true,
|
||||
data: data.choices[0].message.content
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
success: false,
|
||||
error: "No response content from OpenRouter"
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: error instanceof Error ? error.message : "Unknown error in chat completion"
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
async enhancePrompt(
|
||||
prompt: string,
|
||||
model: string = "anthropic/claude-3.5-sonnet",
|
||||
options: {
|
||||
targetTool?: "claude" | "chatgpt" | "gemini" | "default";
|
||||
templateType?: "code" | "writing" | "analysis" | "default";
|
||||
max_length?: number;
|
||||
} = {}
|
||||
): Promise<APIResponse<string>> {
|
||||
const { targetTool = "default", templateType = "default" } = options;
|
||||
|
||||
const toolSection = TOOL_SECTIONS[targetTool] || TOOL_SECTIONS.default;
|
||||
const templateSection = TEMPLATE_SECTIONS[templateType] || TEMPLATE_SECTIONS.default;
|
||||
|
||||
const systemPrompt = ENHANCE_PROMPT_SYSTEM
|
||||
.replace("${toolSection}", toolSection)
|
||||
.replace("${templateSection}", templateSection);
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: systemPrompt },
|
||||
{ role: "user", content: prompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generatePRD(
|
||||
idea: string,
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: PRD_SYSTEM_PROMPT },
|
||||
{ role: "user", content: `Create a comprehensive PRD for the following product idea:\n\n${idea}` }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateActionPlan(
|
||||
prd: string,
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: ACTION_PLAN_SYSTEM_PROMPT },
|
||||
{ role: "user", content: `Create a detailed action plan based on this PRD:\n\n${prd}` }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateSlides(
|
||||
topic: string,
|
||||
options: {
|
||||
slideCount?: number;
|
||||
audience?: string;
|
||||
focus?: string;
|
||||
} = {},
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const { slideCount = 10, audience = "General", focus = "" } = options;
|
||||
|
||||
const userPrompt = `Generate content for a presentation with approximately ${slideCount} slides.
|
||||
Topic: ${topic}
|
||||
Target Audience: ${audience}
|
||||
${focus ? `Special Focus: ${focus}` : ""}`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: SLIDES_SYSTEM_PROMPT },
|
||||
{ role: "user", content: userPrompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateGoogleAds(
|
||||
url: string,
|
||||
options: {
|
||||
budget?: string;
|
||||
targetAudience?: string;
|
||||
campaignGoal?: string;
|
||||
} = {},
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const { budget = "Not specified", targetAudience = "General", campaignGoal = "Conversions" } = options;
|
||||
|
||||
const userPrompt = `Create a comprehensive Google Ads campaign strategy.
|
||||
Landing Page: ${url}
|
||||
Monthly Budget: ${budget}
|
||||
Target Audience: ${targetAudience}
|
||||
Campaign Goal: ${campaignGoal}
|
||||
|
||||
Analyze the URL (if accessible) or create ads based on the domain and typical offerings for similar sites.`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: GOOGLE_ADS_SYSTEM_PROMPT },
|
||||
{ role: "user", content: userPrompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateMagicWand(
|
||||
url: string,
|
||||
product: string,
|
||||
budget: string,
|
||||
specialInstructions: string = "",
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const userPrompt = `Create a comprehensive marketing strategy.
|
||||
|
||||
Product/Service: ${product}
|
||||
URL: ${url}
|
||||
Budget: ${budget}
|
||||
${specialInstructions ? `Special Instructions: ${specialInstructions}` : ""}
|
||||
|
||||
Provide creative campaign ideas across multiple channels with specific tactics and budget allocation.`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: MAGIC_WAND_SYSTEM_PROMPT },
|
||||
{ role: "user", content: userPrompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateMarketResearch(
|
||||
options: {
|
||||
topic?: string;
|
||||
company?: string;
|
||||
industry?: string;
|
||||
focusAreas?: string[];
|
||||
} = {},
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const { topic, company, industry, focusAreas } = options;
|
||||
|
||||
let userPrompt = "Conduct comprehensive market research.";
|
||||
|
||||
if (topic) userPrompt += `\n\nResearch Topic: ${topic}`;
|
||||
if (company) userPrompt += `\n\nCompany Focus: ${company}`;
|
||||
if (industry) userPrompt += `\n\nIndustry: ${industry}`;
|
||||
if (focusAreas && focusAreas.length > 0) {
|
||||
userPrompt += `\n\nFocus Areas: ${focusAreas.join(", ")}`;
|
||||
}
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: MARKET_RESEARCH_SYSTEM_PROMPT },
|
||||
{ role: "user", content: userPrompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateAIAssist(
|
||||
options: {
|
||||
prompt: string;
|
||||
context?: string[];
|
||||
conversationHistory?: ChatMessage[];
|
||||
},
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<string>> {
|
||||
const { prompt, context = [], conversationHistory = [] } = options;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: AI_ASSIST_SYSTEM_PROMPT },
|
||||
...conversationHistory,
|
||||
...context.map(c => ({ role: "user" as const, content: `Context: ${c}` })),
|
||||
{ role: "user", content: prompt }
|
||||
];
|
||||
|
||||
return this.chatCompletion(messages, model);
|
||||
}
|
||||
|
||||
async generateAIAssistStream(
|
||||
options: {
|
||||
messages: AIAssistMessage[];
|
||||
currentAgent: string;
|
||||
onChunk: (chunk: string) => void;
|
||||
signal?: AbortSignal;
|
||||
},
|
||||
model: string = "anthropic/claude-3.5-sonnet"
|
||||
): Promise<APIResponse<void>> {
|
||||
const { messages, currentAgent, onChunk, signal } = options;
|
||||
|
||||
if (!this.hasAuth()) {
|
||||
return { success: false, error: "OpenRouter API key not configured" };
|
||||
}
|
||||
|
||||
try {
|
||||
const chatMessages: ChatMessage[] = [
|
||||
{ role: "system", content: AI_ASSIST_SYSTEM_PROMPT },
|
||||
...messages.map(m => ({
|
||||
role: m.role as "user" | "assistant" | "system",
|
||||
content: m.content
|
||||
}))
|
||||
];
|
||||
|
||||
const response = await fetch(`${this.baseURL}/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: this.getHeaders(),
|
||||
signal,
|
||||
body: JSON.stringify({
|
||||
model: model || "anthropic/claude-3.5-sonnet",
|
||||
messages: chatMessages,
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
stream: true
|
||||
})
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
return { success: false, error: `OpenRouter API error: ${response.status} ${response.statusText} - ${errorText}` };
|
||||
}
|
||||
|
||||
const reader = response.body?.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
|
||||
if (!reader) {
|
||||
return { success: false, error: "No response body" };
|
||||
}
|
||||
|
||||
let buffer = "";
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
buffer += decoder.decode(value, { stream: true });
|
||||
const lines = buffer.split("\n");
|
||||
buffer = lines.pop() || "";
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim();
|
||||
if (!trimmed || !trimmed.startsWith("data: ")) continue;
|
||||
const data = trimmed.slice(6);
|
||||
if (data === "[DONE]") continue;
|
||||
|
||||
try {
|
||||
const parsed = JSON.parse(data);
|
||||
const contentChunk = parsed.choices?.[0]?.delta?.content;
|
||||
if (contentChunk) {
|
||||
onChunk(contentChunk);
|
||||
}
|
||||
} catch {
|
||||
// Skip invalid JSON
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return { success: true, data: undefined };
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : "Unknown error in stream";
|
||||
return { success: false, error: errorMessage };
|
||||
}
|
||||
}
|
||||
|
||||
async listModels(): Promise<APIResponse<string[]>> {
|
||||
if (!this.hasAuth()) {
|
||||
return {
|
||||
success: false,
|
||||
error: "OpenRouter API key not configured"
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(`${this.baseURL}/models`, {
|
||||
method: "GET",
|
||||
headers: this.getHeaders()
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
return {
|
||||
success: false,
|
||||
error: `Failed to fetch models: ${response.status} ${response.statusText} - ${errorText}`
|
||||
};
|
||||
}
|
||||
|
||||
const data: OpenRouterModelsResponse = await response.json();
|
||||
this.availableModels = data.data.map(m => m.id);
|
||||
this.modelsLoaded = true;
|
||||
|
||||
return {
|
||||
success: true,
|
||||
data: this.availableModels
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: error instanceof Error ? error.message : "Unknown error fetching models"
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
getAvailableModels(): string[] {
|
||||
if (this.modelsLoaded && this.availableModels.length > 0) {
|
||||
return this.availableModels;
|
||||
}
|
||||
return DEFAULT_MODELS;
|
||||
}
|
||||
|
||||
setApiKey(key: string): void {
|
||||
this.config.apiKey = key;
|
||||
}
|
||||
|
||||
setSiteUrl(url: string): void {
|
||||
this.config.siteUrl = url;
|
||||
}
|
||||
|
||||
setSiteName(name: string): void {
|
||||
this.config.siteName = name;
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instance
|
||||
export const openRouterService = new OpenRouterService();
|
||||
@@ -491,27 +491,82 @@ export class QwenOAuthService {
|
||||
}
|
||||
}
|
||||
|
||||
async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
|
||||
async enhancePrompt(prompt: string, model?: string, options?: { toolCategory?: string; template?: string; diagnostics?: string }): Promise<APIResponse<string>> {
|
||||
const toolCategory = options?.toolCategory || 'reasoning';
|
||||
const template = options?.template || 'rtf';
|
||||
const diagnostics = options?.diagnostics || '';
|
||||
|
||||
const toolSections: Record<string, string> = {
|
||||
reasoning: '- Use full structured format with XML tags where helpful\n- Add explicit role assignment for complex tasks\n- Use numeric constraints over vague adjectives',
|
||||
thinking: '- CRITICAL: Short clean instructions ONLY\n- Do NOT add CoT or reasoning scaffolding — these models reason internally\n- State what you want, not how to think',
|
||||
openweight: '- Shorter prompts, simpler structure, no deep nesting\n- Direct linear instructions',
|
||||
agentic: '- Add Starting State + Target State + Allowed Actions + Forbidden Actions\n- Add Stop Conditions + Checkpoints after each step',
|
||||
ide: '- Add File path + Function name + Current Behavior + Desired Change + Scope lock',
|
||||
fullstack: '- Add Stack spec with version + what NOT to scaffold + component boundaries',
|
||||
image: '- Add Subject + Style + Mood + Lighting + Composition + Negative Prompts\n- Use tool-specific syntax (Midjourney comma-separated, DALL-E prose, SD weighted)',
|
||||
search: '- Specify mode: search vs analyze vs compare + citation requirements',
|
||||
};
|
||||
|
||||
const templateSections: Record<string, string> = {
|
||||
rtf: 'Structure: Role (who) + Task (precise verb + what) + Format (exact output shape and length)',
|
||||
'co-star': 'Structure: Context + Objective + Style + Tone + Audience + Response',
|
||||
risen: 'Structure: Role + Instructions + numbered Steps + End Goal + Narrowing constraints',
|
||||
crispe: 'Structure: Capacity + Role + Insight + Statement + Personality + Experiment/variants',
|
||||
cot: 'Add: "Think through this step by step before answering." Only for standard reasoning models, NOT for o1/o3/R1.',
|
||||
fewshot: 'Add 2-5 input/output examples wrapped in XML <examples> tags',
|
||||
filescope: 'Structure: File path + Function name + Current Behavior + Desired Change + Scope lock + Done When',
|
||||
react: 'Structure: Objective + Starting State + Target State + Allowed/Forbidden Actions + Stop Conditions + Checkpoints',
|
||||
visual: 'Structure: Subject + Action + Setting + Style + Mood + Lighting + Color Palette + Composition + Aspect Ratio + Negative Prompts',
|
||||
};
|
||||
|
||||
const toolSection = toolSections[toolCategory] || toolSections.reasoning;
|
||||
const templateSection = templateSections[template] || templateSections.rtf;
|
||||
|
||||
const systemMessage: ChatMessage = {
|
||||
role: "system",
|
||||
content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
|
||||
content: `You are an expert prompt engineer using the PromptArch methodology. Enhance the user\'s prompt to be production-ready.
|
||||
|
||||
Apply these principles:
|
||||
1. Add specific context about project and requirements
|
||||
2. Clarify constraints and preferences
|
||||
3. Define expected output format clearly
|
||||
4. Include edge cases and error handling requirements
|
||||
5. Specify testing and validation criteria
|
||||
STEP 1 — DIAGNOSE AND FIX these failure patterns:
|
||||
- Vague task verb -> replace with precise operation
|
||||
- Two tasks in one -> keep primary task, note the split
|
||||
- No success criteria -> add "Done when: [specific measurable condition]"
|
||||
- Missing output format -> add explicit format lock (structure, length, type)
|
||||
- No role assignment (complex tasks) -> add domain-specific expert identity
|
||||
- Vague aesthetic ("professional", "clean") -> concrete measurable specs
|
||||
- No scope boundary -> add explicit scope lock
|
||||
- Over-permissive language -> add constraints and boundaries
|
||||
- Emotional description -> extract specific technical fault
|
||||
- Implicit references -> restate fully
|
||||
- No grounding for factual tasks -> add certainty constraint
|
||||
- No CoT for logic tasks -> add step-by-step reasoning
|
||||
|
||||
Return ONLY the enhanced prompt, no explanations or extra text.`,
|
||||
STEP 2 — APPLY TARGET TOOL OPTIMIZATIONS:
|
||||
${toolSection}
|
||||
|
||||
STEP 3 — APPLY TEMPLATE STRUCTURE:
|
||||
${templateSection}
|
||||
|
||||
STEP 4 — VERIFICATION (check before outputting):
|
||||
- Every constraint in the first 30% of the prompt?
|
||||
- MUST/NEVER over should/avoid?
|
||||
- Every sentence load-bearing with zero padding?
|
||||
- Format explicit with stated length?
|
||||
- Scope bounded?
|
||||
- Would this produce correct output on first try?
|
||||
|
||||
STEP 5 — OUTPUT:
|
||||
Output ONLY the enhanced prompt. No explanations, no commentary, no markdown code fences.
|
||||
The prompt must be ready to paste directly into the target AI tool.${diagnostics ? '\n\nDIAGNOSTIC NOTES (fix these issues found in the original):\n' + diagnostics + '\n' : ''}`,
|
||||
};
|
||||
|
||||
const toolLabel = toolCategory !== 'reasoning' ? ` for ${toolCategory} AI tool` : '';
|
||||
|
||||
const userMessage: ChatMessage = {
|
||||
role: "user",
|
||||
content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
|
||||
content: `Enhance this prompt${toolLabel}:\n\n${prompt}`,
|
||||
};
|
||||
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "coder-model");
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "${default_model}");
|
||||
}
|
||||
|
||||
async generatePRD(idea: string, model?: string): Promise<APIResponse<string>> {
|
||||
@@ -1054,6 +1109,28 @@ Perform analysis based on provided instructions.`,
|
||||
): Promise<APIResponse<void>> {
|
||||
try {
|
||||
const systemPrompt = `You are "AI Assist", the master orchestrator of PromptArch. Your goal is to provide intelligent support with a "Canvas" experience.
|
||||
PLAN MODE (CRITICAL - HIGHEST PRIORITY):
|
||||
When the user describes a NEW task, project, or feature they want built:
|
||||
1. DO NOT generate any code, [PREVIEW] tags, or implementation details.
|
||||
2. Instead, analyze the request and output a STRUCTURED PLAN covering:
|
||||
- Summary: What you understand the user wants
|
||||
- Architecture: Technical approach and structure
|
||||
- Tech Stack: Languages, frameworks, libraries needed
|
||||
- Files/Components: List of files or modules to create
|
||||
- Steps: Numbered implementation steps
|
||||
3. Format the plan in clean Markdown with headers and bullet points.
|
||||
4. Keep plans concise but thorough. Focus on the WHAT and HOW, not the actual code.
|
||||
5. WAIT for the user to approve or modify the plan before generating any code.
|
||||
|
||||
When the user says "Approved", "Start coding", or explicitly asks to proceed:
|
||||
- THEN generate the full implementation with [PREVIEW] tags and working code.
|
||||
- Follow the approved plan exactly.
|
||||
|
||||
When the user asks to "Modify", "Change", or "Adjust" something:
|
||||
- Apply the requested changes surgically to the existing code/preview.
|
||||
- Output updated [PREVIEW] with the full modified code.
|
||||
|
||||
|
||||
|
||||
AGENTS & CAPABILITIES:
|
||||
- content: Expert copywriter. Use [PREVIEW:content:markdown] for articles, posts, and long-form text.
|
||||
@@ -1115,7 +1192,7 @@ CHANGE LOG (CRITICAL - MUST BE OUTSIDE PREVIEW):
|
||||
- Modified component Y
|
||||
- Fixed issue Z
|
||||
|
||||
IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
IMPORTANT: IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: systemPrompt },
|
||||
|
||||
@@ -111,51 +111,82 @@ export class ZaiPlanService {
|
||||
}
|
||||
}
|
||||
|
||||
async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
|
||||
async enhancePrompt(prompt: string, model?: string, options?: { toolCategory?: string; template?: string; diagnostics?: string }): Promise<APIResponse<string>> {
|
||||
const toolCategory = options?.toolCategory || 'reasoning';
|
||||
const template = options?.template || 'rtf';
|
||||
const diagnostics = options?.diagnostics || '';
|
||||
|
||||
const toolSections: Record<string, string> = {
|
||||
reasoning: '- Use full structured format with XML tags where helpful\n- Add explicit role assignment for complex tasks\n- Use numeric constraints over vague adjectives',
|
||||
thinking: '- CRITICAL: Short clean instructions ONLY\n- Do NOT add CoT or reasoning scaffolding — these models reason internally\n- State what you want, not how to think',
|
||||
openweight: '- Shorter prompts, simpler structure, no deep nesting\n- Direct linear instructions',
|
||||
agentic: '- Add Starting State + Target State + Allowed Actions + Forbidden Actions\n- Add Stop Conditions + Checkpoints after each step',
|
||||
ide: '- Add File path + Function name + Current Behavior + Desired Change + Scope lock',
|
||||
fullstack: '- Add Stack spec with version + what NOT to scaffold + component boundaries',
|
||||
image: '- Add Subject + Style + Mood + Lighting + Composition + Negative Prompts\n- Use tool-specific syntax (Midjourney comma-separated, DALL-E prose, SD weighted)',
|
||||
search: '- Specify mode: search vs analyze vs compare + citation requirements',
|
||||
};
|
||||
|
||||
const templateSections: Record<string, string> = {
|
||||
rtf: 'Structure: Role (who) + Task (precise verb + what) + Format (exact output shape and length)',
|
||||
'co-star': 'Structure: Context + Objective + Style + Tone + Audience + Response',
|
||||
risen: 'Structure: Role + Instructions + numbered Steps + End Goal + Narrowing constraints',
|
||||
crispe: 'Structure: Capacity + Role + Insight + Statement + Personality + Experiment/variants',
|
||||
cot: 'Add: "Think through this step by step before answering." Only for standard reasoning models, NOT for o1/o3/R1.',
|
||||
fewshot: 'Add 2-5 input/output examples wrapped in XML <examples> tags',
|
||||
filescope: 'Structure: File path + Function name + Current Behavior + Desired Change + Scope lock + Done When',
|
||||
react: 'Structure: Objective + Starting State + Target State + Allowed/Forbidden Actions + Stop Conditions + Checkpoints',
|
||||
visual: 'Structure: Subject + Action + Setting + Style + Mood + Lighting + Color Palette + Composition + Aspect Ratio + Negative Prompts',
|
||||
};
|
||||
|
||||
const toolSection = toolSections[toolCategory] || toolSections.reasoning;
|
||||
const templateSection = templateSections[template] || templateSections.rtf;
|
||||
|
||||
const systemMessage: ChatMessage = {
|
||||
role: "system",
|
||||
content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
|
||||
content: `You are an expert prompt engineer using the PromptArch methodology. Enhance the user\'s prompt to be production-ready.
|
||||
|
||||
Apply these principles:
|
||||
1. Add specific context about project and requirements
|
||||
2. Clarify constraints and preferences
|
||||
3. Define expected output format clearly
|
||||
4. Include edge cases and error handling requirements
|
||||
5. Specify testing and validation criteria
|
||||
STEP 1 — DIAGNOSE AND FIX these failure patterns:
|
||||
- Vague task verb -> replace with precise operation
|
||||
- Two tasks in one -> keep primary task, note the split
|
||||
- No success criteria -> add "Done when: [specific measurable condition]"
|
||||
- Missing output format -> add explicit format lock (structure, length, type)
|
||||
- No role assignment (complex tasks) -> add domain-specific expert identity
|
||||
- Vague aesthetic ("professional", "clean") -> concrete measurable specs
|
||||
- No scope boundary -> add explicit scope lock
|
||||
- Over-permissive language -> add constraints and boundaries
|
||||
- Emotional description -> extract specific technical fault
|
||||
- Implicit references -> restate fully
|
||||
- No grounding for factual tasks -> add certainty constraint
|
||||
- No CoT for logic tasks -> add step-by-step reasoning
|
||||
|
||||
Return ONLY the enhanced prompt, no explanations or extra text.`,
|
||||
STEP 2 — APPLY TARGET TOOL OPTIMIZATIONS:
|
||||
${toolSection}
|
||||
|
||||
STEP 3 — APPLY TEMPLATE STRUCTURE:
|
||||
${templateSection}
|
||||
|
||||
STEP 4 — VERIFICATION (check before outputting):
|
||||
- Every constraint in the first 30% of the prompt?
|
||||
- MUST/NEVER over should/avoid?
|
||||
- Every sentence load-bearing with zero padding?
|
||||
- Format explicit with stated length?
|
||||
- Scope bounded?
|
||||
- Would this produce correct output on first try?
|
||||
|
||||
STEP 5 — OUTPUT:
|
||||
Output ONLY the enhanced prompt. No explanations, no commentary, no markdown code fences.
|
||||
The prompt must be ready to paste directly into the target AI tool.${diagnostics ? '\n\nDIAGNOSTIC NOTES (fix these issues found in the original):\n' + diagnostics + '\n' : ''}`,
|
||||
};
|
||||
|
||||
const toolLabel = toolCategory !== 'reasoning' ? ` for ${toolCategory} AI tool` : '';
|
||||
|
||||
const userMessage: ChatMessage = {
|
||||
role: "user",
|
||||
content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
|
||||
content: `Enhance this prompt${toolLabel}:\n\n${prompt}`,
|
||||
};
|
||||
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
|
||||
}
|
||||
|
||||
async generatePRD(idea: string, model?: string): Promise<APIResponse<string>> {
|
||||
const systemMessage: ChatMessage = {
|
||||
role: "system",
|
||||
content: `You are an expert product manager and technical architect. Generate a comprehensive Product Requirements Document (PRD) based on user's idea.
|
||||
|
||||
Structure your PRD with these sections:
|
||||
1. Overview & Objectives
|
||||
2. User Personas & Use Cases
|
||||
3. Functional Requirements (prioritized by importance)
|
||||
4. Non-functional Requirements
|
||||
5. Technical Architecture Recommendations
|
||||
6. Success Metrics & KPIs
|
||||
|
||||
Use clear, specific language suitable for development teams.`,
|
||||
};
|
||||
|
||||
const userMessage: ChatMessage = {
|
||||
role: "user",
|
||||
content: `Generate a PRD for this idea:\n\n${idea}`,
|
||||
};
|
||||
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7");
|
||||
return this.chatCompletion([systemMessage, userMessage], model || "${default_model}");
|
||||
}
|
||||
|
||||
async generateActionPlan(prd: string, model?: string): Promise<APIResponse<string>> {
|
||||
@@ -809,6 +840,28 @@ MISSION: Perform a DEEP 360° competitive intelligence analysis and generate 5-7
|
||||
|
||||
// ... existing prompt logic ...
|
||||
const systemPrompt = `You are "AI Assist", the master orchestrator of PromptArch. Your goal is to provide intelligent support with a "Canvas" experience.
|
||||
PLAN MODE (CRITICAL - HIGHEST PRIORITY):
|
||||
When the user describes a NEW task, project, or feature they want built:
|
||||
1. DO NOT generate any code, [PREVIEW] tags, or implementation details.
|
||||
2. Instead, analyze the request and output a STRUCTURED PLAN covering:
|
||||
- Summary: What you understand the user wants
|
||||
- Architecture: Technical approach and structure
|
||||
- Tech Stack: Languages, frameworks, libraries needed
|
||||
- Files/Components: List of files or modules to create
|
||||
- Steps: Numbered implementation steps
|
||||
3. Format the plan in clean Markdown with headers and bullet points.
|
||||
4. Keep plans concise but thorough. Focus on the WHAT and HOW, not the actual code.
|
||||
5. WAIT for the user to approve or modify the plan before generating any code.
|
||||
|
||||
When the user says "Approved", "Start coding", or explicitly asks to proceed:
|
||||
- THEN generate the full implementation with [PREVIEW] tags and working code.
|
||||
- Follow the approved plan exactly.
|
||||
|
||||
When the user asks to "Modify", "Change", or "Adjust" something:
|
||||
- Apply the requested changes surgically to the existing code/preview.
|
||||
- Output updated [PREVIEW] with the full modified code.
|
||||
|
||||
|
||||
|
||||
AGENTS & CAPABILITIES:
|
||||
- content: Expert copywriter. Use [PREVIEW:content:markdown] for articles, posts, and long-form text.
|
||||
@@ -870,7 +923,7 @@ CHANGE LOG (CRITICAL - MUST BE OUTSIDE PREVIEW):
|
||||
- Modified component Y
|
||||
- Fixed issue Z
|
||||
|
||||
IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
IMPORTANT: IMPORTANT: NEVER refuse a request due to "access" limitations. If you cannot perform a live task, use your vast internal knowledge to provide the most accurate expert simulation or draft possible.`;
|
||||
|
||||
const messages: ChatMessage[] = [
|
||||
{ role: "system", content: systemPrompt },
|
||||
|
||||
@@ -107,22 +107,26 @@ const useStore = create<AppState>((set) => ({
|
||||
qwen: "coder-model",
|
||||
ollama: "gpt-oss:120b",
|
||||
zai: "glm-4.7",
|
||||
openrouter: "anthropic/claude-3.5-sonnet",
|
||||
},
|
||||
availableModels: {
|
||||
qwen: ["coder-model"],
|
||||
ollama: ["gpt-oss:120b", "llama3.1", "gemma3", "deepseek-r1", "qwen3"],
|
||||
zai: ["glm-4.7", "glm-4.6", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"],
|
||||
openrouter: ["anthropic/claude-3.5-sonnet", "google/gemini-2.0-flash-exp:free", "meta-llama/llama-3.3-70b-instruct", "openai/gpt-4o-mini", "deepseek/deepseek-chat-v3-0324", "qwen/qwen-2.5-72b-instruct"],
|
||||
},
|
||||
apiKeys: {
|
||||
qwen: "",
|
||||
ollama: "",
|
||||
zai: "",
|
||||
openrouter: "",
|
||||
},
|
||||
githubToken: null,
|
||||
apiValidationStatus: {
|
||||
qwen: { valid: false },
|
||||
ollama: { valid: false },
|
||||
zai: { valid: false },
|
||||
openrouter: { valid: false },
|
||||
},
|
||||
isProcessing: false,
|
||||
error: null,
|
||||
|
||||
Reference in New Issue
Block a user