Add 260+ Claude Code skills from skills.sh
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>
This commit is contained in:
admin
357
agent-pipeline-builder/SKILL.md
Normal file
357
agent-pipeline-builder/SKILL.md
Normal file
@@ -0,0 +1,357 @@
|
||||
---
|
||||
name: agent-pipeline-builder
|
||||
description: Build multi-agent pipelines with structured data flow between agents. Use when creating workflows where each agent has a specialized role and passes output to the next agent.
|
||||
allowed-tools: Write, Edit, Read, Bash, WebSearch
|
||||
license: MIT
|
||||
---
|
||||
|
||||
# Agent Pipeline Builder
|
||||
|
||||
Build reliable multi-agent workflows where each agent has a single, focused responsibility and outputs structured data that the next agent consumes.
|
||||
|
||||
## When to Use This Skill
|
||||
|
||||
Use this skill when:
|
||||
- Building complex workflows that need multiple specialized agents
|
||||
- Creating content pipelines (research → analysis → writing)
|
||||
- Designing data processing flows with validation at each stage
|
||||
- Implementing "funnel" patterns where broad input becomes focused output
|
||||
|
||||
## Pipeline Pattern
|
||||
|
||||
A pipeline consists of:
|
||||
1. **Stage 1: Researcher/Gatherer** - Fetches raw data (WebSearch, file reading, API calls)
|
||||
2. **Stage 2: Analyzer/Filter** - Processes and selects best options
|
||||
3. **Stage 3: Creator/Writer** - Produces final output
|
||||
|
||||
Each stage:
|
||||
- Has ONE job
|
||||
- Outputs structured JSON (or YAML)
|
||||
- Wraps output in markers (e.g., `<<<stage>>>...<<<end-stage>>>`)
|
||||
- Passes data to next stage via stdin or file
|
||||
|
||||
## RalphLoop "Tackle Until Solved" Integration
|
||||
|
||||
For complex pipelines (3+ stages or complexity >= 5), agent-pipeline-builder automatically delegates to Ralph Orchestrator for autonomous pipeline construction and testing.
|
||||
|
||||
### When Ralph is Triggered
|
||||
|
||||
Ralph mode activates for pipelines with:
|
||||
- 3 or more stages
|
||||
- Complex stage patterns (external APIs, complex processing, conditional logic)
|
||||
- Parallel stage execution
|
||||
- User opt-in via `RALPH_AUTO=true` or `PIPELINE_USE_RALPH=true`
|
||||
|
||||
### Using Ralph Integration
|
||||
|
||||
When a complex pipeline is detected:
|
||||
|
||||
1. Check for Python integration module:
|
||||
```bash
|
||||
python3 /home/uroma/.claude/skills/agent-pipeline-builder/ralph-pipeline.py --test-complexity
|
||||
```
|
||||
|
||||
2. If complex, delegate to Ralph:
|
||||
```bash
|
||||
/home/uroma/obsidian-web-interface/bin/ralphloop -i .ralph/PIPELINE.md
|
||||
```
|
||||
|
||||
3. Monitor Ralph's progress in `.ralph/state.json`
|
||||
|
||||
4. On completion, use generated pipeline from `.ralph/iterations/pipeline.md`
|
||||
|
||||
### Manual Ralph Invocation
|
||||
|
||||
For explicit Ralph mode on any pipeline:
|
||||
```bash
|
||||
export PIPELINE_USE_RALPH=true
|
||||
# or
|
||||
export RALPH_AUTO=true
|
||||
```
|
||||
|
||||
Then invoke `/agent-pipeline-builder` as normal.
|
||||
|
||||
### Ralph-Generated Pipeline Structure
|
||||
|
||||
When Ralph builds the pipeline autonomously, it creates:
|
||||
|
||||
```
|
||||
.claude/agents/[pipeline-name]/
|
||||
├── researcher.md # Agent definition
|
||||
├── analyzer.md # Agent definition
|
||||
└── writer.md # Agent definition
|
||||
|
||||
scripts/
|
||||
└── run-[pipeline-name].ts # Orchestration script
|
||||
|
||||
.ralph/
|
||||
├── PIPELINE.md # Manifest
|
||||
├── state.json # Progress tracking
|
||||
└── iterations/
|
||||
└── pipeline.md # Final generated pipeline
|
||||
```
|
||||
|
||||
## Creating a Pipeline
|
||||
|
||||
### Step 1: Define Pipeline Manifest
|
||||
|
||||
Create a `pipeline.md` file:
|
||||
|
||||
```markdown
|
||||
# Pipeline: [Name]
|
||||
|
||||
## Stages
|
||||
1. researcher - Finds/fetches raw data
|
||||
2. analyzer - Processes and selects
|
||||
3. writer - Creates final output
|
||||
|
||||
## Data Format
|
||||
All stages use JSON with markers: `<<<stage-name>>>...<<<end-stage-name>>>`
|
||||
```
|
||||
|
||||
### Step 2: Create Agent Definitions
|
||||
|
||||
For each stage, create an agent file `.claude/agents/[pipeline-name]/[stage-name].md`:
|
||||
|
||||
```markdown
|
||||
---
|
||||
name: researcher
|
||||
description: What this agent does
|
||||
model: haiku # or sonnet, opus
|
||||
---
|
||||
|
||||
You are a [role] agent.
|
||||
|
||||
## CRITICAL: NO EXPLANATION - JUST ACTION
|
||||
|
||||
DO NOT explain what you will do. Just USE tools immediately, then output.
|
||||
|
||||
## Instructions
|
||||
|
||||
1. Use [specific tool] to get data
|
||||
2. Output JSON in the exact format below
|
||||
3. Wrap in markers as specified
|
||||
|
||||
## Output Format
|
||||
|
||||
<<<researcher>>>
|
||||
```json
|
||||
{
|
||||
"data": [...]
|
||||
}
|
||||
```
|
||||
<<<end-researcher>>>
|
||||
```
|
||||
|
||||
### Step 3: Implement Pipeline Script
|
||||
|
||||
Create a script that orchestrates the agents:
|
||||
|
||||
```typescript
|
||||
// scripts/run-pipeline.ts
|
||||
import { runAgent } from '@anthropic-ai/claude-agent-sdk';
|
||||
|
||||
async function runPipeline() {
|
||||
// Stage 1: Researcher
|
||||
const research = await runAgent('researcher', {
|
||||
context: { topic: 'AI news' }
|
||||
});
|
||||
|
||||
// Stage 2: Analyzer (uses research output)
|
||||
const analysis = await runAgent('analyzer', {
|
||||
input: research,
|
||||
context: { criteria: 'impact' }
|
||||
});
|
||||
|
||||
// Stage 3: Writer (uses analysis output)
|
||||
const final = await runAgent('writer', {
|
||||
input: analysis,
|
||||
context: { format: 'tweet' }
|
||||
});
|
||||
|
||||
return final;
|
||||
}
|
||||
```
|
||||
|
||||
## Pipeline Best Practices
|
||||
|
||||
### 1. Single Responsibility
|
||||
Each agent does ONE thing:
|
||||
- ✓ researcher: Fetches data
|
||||
- ✓ analyzer: Filters and ranks
|
||||
- ✗ researcher-analyzer: Does both (too complex)
|
||||
|
||||
### 2. Structured Data Flow
|
||||
- Use JSON or YAML for all inter-agent communication
|
||||
- Define schemas upfront
|
||||
- Validate output before passing to next stage
|
||||
|
||||
### 3. Error Handling
|
||||
- Each agent should fail gracefully
|
||||
- Use fallback outputs
|
||||
- Log errors for debugging
|
||||
|
||||
### 4. Deterministic Patterns
|
||||
- Constrain agents with specific tools
|
||||
- Use detailed system prompts
|
||||
- Avoid open-ended requests
|
||||
|
||||
## Example Pipeline: AI News Tweet
|
||||
|
||||
### Manifest
|
||||
```yaml
|
||||
name: ai-news-tweet
|
||||
stages:
|
||||
- researcher: Gets today's AI news
|
||||
- analyzer: Picks most impactful story
|
||||
- writer: Crafts engaging tweet
|
||||
```
|
||||
|
||||
### Researcher Agent
|
||||
```markdown
|
||||
---
|
||||
name: researcher
|
||||
description: Finds recent AI news using WebSearch
|
||||
model: haiku
|
||||
---
|
||||
|
||||
Use WebSearch to find AI news from TODAY ONLY.
|
||||
|
||||
Output:
|
||||
<<<researcher>>>
|
||||
```json
|
||||
{
|
||||
"items": [
|
||||
{
|
||||
"title": "...",
|
||||
"summary": "...",
|
||||
"url": "...",
|
||||
"published_at": "YYYY-MM-DD"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
<<<end-researcher>>>
|
||||
```
|
||||
|
||||
### Analyzer Agent
|
||||
```markdown
|
||||
---
|
||||
name: analyzer
|
||||
description: Analyzes news and selects best story
|
||||
model: sonnet
|
||||
---
|
||||
|
||||
Input: Researcher output (stdin)
|
||||
|
||||
Select the most impactful story based on:
|
||||
- Technical significance
|
||||
- Broad interest
|
||||
- Credibility of source
|
||||
|
||||
Output:
|
||||
<<<analyzer>>>
|
||||
```json
|
||||
{
|
||||
"selected": {
|
||||
"title": "...",
|
||||
"summary": "...",
|
||||
"reasoning": "..."
|
||||
}
|
||||
}
|
||||
```
|
||||
<<<end-analyzer>>>
|
||||
```
|
||||
|
||||
### Writer Agent
|
||||
```markdown
|
||||
---
|
||||
name: writer
|
||||
description: Writes engaging tweet
|
||||
model: sonnet
|
||||
---
|
||||
|
||||
Input: Analyzer output (stdin)
|
||||
|
||||
Write a tweet that:
|
||||
- Hooks attention
|
||||
- Conveys key insight
|
||||
- Fits 280 characters
|
||||
- Includes relevant hashtags
|
||||
|
||||
Output:
|
||||
<<<writer>>>
|
||||
```json
|
||||
{
|
||||
"tweet": "...",
|
||||
"hashtags": ["..."]
|
||||
}
|
||||
```
|
||||
<<<end-writer>>>
|
||||
```
|
||||
|
||||
## Running the Pipeline
|
||||
|
||||
### Method 1: Sequential Script
|
||||
```bash
|
||||
./scripts/run-pipeline.ts
|
||||
```
|
||||
|
||||
### Method 2: Using Task Tool
|
||||
```typescript
|
||||
// Launch each stage as a separate agent task
|
||||
await Task('Research stage', researchPrompt, 'haiku');
|
||||
await Task('Analysis stage', analysisPrompt, 'sonnet');
|
||||
await Task('Writing stage', writingPrompt, 'sonnet');
|
||||
```
|
||||
|
||||
### Method 3: Using Claude Code Skills
|
||||
Create a skill that orchestrates the pipeline with proper error handling.
|
||||
|
||||
## Testing Pipelines
|
||||
|
||||
### Unit Tests
|
||||
Test each agent independently:
|
||||
```bash
|
||||
# Test researcher
|
||||
npm run test:researcher
|
||||
|
||||
# Test analyzer with mock data
|
||||
npm run test:analyzer
|
||||
|
||||
# Test writer with mock analysis
|
||||
npm run test:writer
|
||||
```
|
||||
|
||||
### Integration Tests
|
||||
Test full pipeline:
|
||||
```bash
|
||||
npm run test:pipeline
|
||||
```
|
||||
|
||||
## Debugging Tips
|
||||
|
||||
1. **Enable verbose logging** - See what each agent outputs
|
||||
2. **Validate JSON schemas** - Catch malformed data early
|
||||
3. **Use mock inputs** - Test downstream agents independently
|
||||
4. **Check marker format** - Agents must use exact markers
|
||||
|
||||
## Common Patterns
|
||||
|
||||
### Funnel Pattern
|
||||
```
|
||||
Many inputs → Filter → Select One → Output
|
||||
```
|
||||
Example: News aggregator → analyzer → best story
|
||||
|
||||
### Transformation Pattern
|
||||
```
|
||||
Input → Transform → Validate → Output
|
||||
```
|
||||
Example: Raw data → clean → validate → structured data
|
||||
|
||||
### Assembly Pattern
|
||||
```
|
||||
Part A + Part B → Assemble → Complete
|
||||
```
|
||||
Example: Research + style guide → formatted article
|
||||
Reference in New Issue
Block a user