07242683bf
Complete collection of AI agent skills including: - Frontend Development (Vue, React, Next.js, Three.js) - Backend Development (NestJS, FastAPI, Node.js) - Mobile Development (React Native, Expo) - Testing (E2E, frontend, webapp) - DevOps (GitHub Actions, CI/CD) - Marketing (SEO, copywriting, analytics) - Security (binary analysis, vulnerability scanning) - And many more... Synchronized from: https://skills.sh/ Co-Authored-By: Claude <noreply@anthropic.com>
2.5 KiB
2.5 KiB
Phase 2: Terminal Execution Enhancements - Research Document
Research Summary
Modular Tool System Architecture
Based on research of leading AI agent frameworks (AutoGen, Xaibo, ReAct patterns), here are key architectural patterns:
1. Tool Abstraction Layer
# Base tool interface
class Tool:
name: str
description: str
parameters: dict
async def execute(self, **kwargs) -> ToolResult:
pass
2. Tool Registry Pattern
class ToolRegistry:
def register(self, tool: Tool)
def get(self, name: str) -> Tool
def list_available(self) -> List[Tool]
def execute(self, tool_name: str, **kwargs) -> ToolResult
3. ReAct Pattern Integration
- Thought: Agent reasoning about what to do
- Action: Selecting and executing a tool
- Observation: Result from tool execution
- Iteration: Loop until completion
4. Key Features from Research
- Xaibo: Tool providers make Python functions available as tools
- AutoGen: Built-in
PythonCodeExecutionToolwith custom agent support - ReAct:
agent_loop()controller that parses reasoning and executes tools - Temporal: Durable agents that evaluate available tools
Implementation Plan for Phase 2
Task 2.1: Create Modular Tool System
- Base Tool Interface - Abstract class for all tools
- Concrete Tool Implementations:
ShellTool- Execute shell commandsFileOperationTool- File system operationsWebSearchTool- Web search capabilitiesCodeExecutionTool- Python code execution
Task 2.2: Enhanced Intent Analysis
- Command Classification - Better detection of command types
- Tool Selection - Automatic tool selection based on intent
- Context Awareness - Remember previous commands for suggestions
Task 2.3: Error Handling & Output Formatting
- Structured Error Responses - Clear, actionable error messages
- Output Formatting - Rich output with syntax highlighting
- Telemetry - Track command success rates and patterns