feat: Add unified agent integration with Prometheus, Every Code, and Dexto
This commit adds comprehensive integration of three major AI agent platforms: ## MCP Servers (3) - Prometheus MCP: Knowledge graph code reasoning with AST analysis - Every Code MCP: Fast terminal-based coding agent with Auto Drive - Dexto MCP: Agent harness with orchestration and session management ## Claude Code Skills (6) - /agent-plan: Generate implementation plans - /agent-fix-bug: Fix bugs end-to-end - /agent-solve: Solve complex problems - /agent-review: Review code quality - /agent-context: Get code context - /agent-orchestrate: Orchestrate workflows ## Ralph Auto-Integration - Pattern-based auto-trigger for all three platforms - Intelligent backend selection - Multi-platform coordination - Configuration in ralph/ralph.yml ## Documentation - Complete integration guides - Ralph auto-integration documentation - Setup scripts - Usage examples Co-Authored-By: Claude <noreply@anthropic.com>
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uroma
95
skills/agents/fix-bug.md
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95
skills/agents/fix-bug.md
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# Agent: Fix Bug
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End-to-end bug fixing with reproduction, patch generation, and verification.
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## Usage
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```
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/agent-fix-bug "Login fails after password reset"
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```
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## Description
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The `/agent-fix-bug` skill fixes bugs through a systematic process:
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1. Classify the issue type (bug/feature/question/doc)
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2. Reproduce the bug in isolated environment
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3. Retrieve relevant code context via knowledge graph
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4. Generate and apply patch
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5. Verify fix with regression tests
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## Examples
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### Fix a reported bug
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```
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/agent-fix-bug "Users with special characters in names cannot sign up"
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```
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### Fix with specific error
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```
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/agent-fix-bug "NullPointerException in UserService.updateProfile
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Error: java.lang.NullPointerException: Cannot invoke \"String.length()\" because the return value of \"User.getName()\" is null"
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```
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### Fix with reproduction steps
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```
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/agent-fix-bug "Shopping cart loses items when user switches tabs
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Steps:
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1. Add item to cart
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2. Open new tab
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3. Cart appears empty
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4. Return to original tab
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5. Cart still shows items"
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```
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## Backends
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- **Primary**: Prometheus (bug pipeline with LangGraph agents)
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- **Verification**: Every Code Auto Review
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- **Testing**: Prometheus (Docker container execution)
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## Workflow
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### 1. Issue Classification
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- Analyze issue description
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- Classify as bug/feature/question/documentation
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- Identify affected components
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### 2. Bug Reproduction
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- Create minimal reproduction case
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- Execute in Docker container
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- Capture error logs and stack traces
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### 3. Context Retrieval
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- Search knowledge graph for related code
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- Analyze AST for function call chains
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- Identify similar bug fixes
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### 4. Patch Generation
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- Generate fix using AI reasoning
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- Apply patch with git
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- Test in isolated environment
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### 5. Verification
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- Run regression tests
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- Verify reproduction case is fixed
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- Generate test coverage report
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## Output
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```
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✓ Issue classified as: bug
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✓ Bug reproduced: UserService.updateProfile throws NPE for null names
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✓ Context retrieved: 5 related files, 12 similar issues
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✓ Patch generated: Added null check in UserService.updateProfile
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✓ Tests passed: 15/15
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✓ Regression verified: No existing tests broken
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Fix applied: prometheus-backend/src/main/java/com/prometheus/service/UserService.java:47
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```
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## Follow-up
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After fixing:
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- `/agent-review` - Review the changes
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- `/agent-test` - Run specific tests
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- `/agent-context` - Understand the fix context
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57
skills/agents/plan.md
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57
skills/agents/plan.md
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# Agent: Plan
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Generate implementation plans using Every Code's planning capabilities with Prometheus context.
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## Usage
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```
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/agent-plan "Implement user authentication with JWT tokens"
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```
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## Description
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The `/agent-plan` skill generates detailed implementation plans by:
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1. Using Every Code's `/plan` command for structured planning
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2. Augmenting with Prometheus knowledge graph for context
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3. Providing step-by-step implementation guidance
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## Examples
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### Plan a feature
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```
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/agent-plan "Add dark mode with system preference detection"
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```
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### Plan with constraints
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```
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/agent-plan "Refactor the user service to use GraphQL
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Scope: backend only, no frontend changes"
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```
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### Plan database migration
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```
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/agent-plan "Migrate from PostgreSQL to MySQL for the users table"
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```
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## Backends
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- **Primary**: Every Code (/plan command)
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- **Enhancement**: Prometheus (context from similar code)
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## Workflow
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1. Parse your request and identify key requirements
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2. Query Prometheus for similar existing code patterns
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3. Generate implementation plan using Every Code
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4. Present plan with:
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- Architecture overview
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- Step-by-step tasks
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- Files to create/modify
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- Dependencies to add
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- Testing considerations
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## Follow-up
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After planning, use:
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- `/agent-implement` - Execute the plan
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- `/agent-context` - Get more context on specific files
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105
skills/agents/solve.md
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105
skills/agents/solve.md
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# Agent: Solve
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Multi-agent problem solving with orchestration across multiple tools and approaches.
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## Usage
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```
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/agent-solve "Optimize database queries for slow dashboard loading"
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```
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## Description
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The `/agent-solve` skill tackles complex problems by coordinating multiple agents:
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1. Every Code's `/solve` command for orchestration
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2. Prometheus for deep code analysis
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3. Multiple solution attempts with validation
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4. Best solution selection based on metrics
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## Examples
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### Solve performance issue
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```
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/agent-solve "Dashboard takes 10 seconds to load with 1000 items"
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```
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### Solve architecture problem
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```
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/agent-solve "Refactor monolithic payment service into microservices
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Constraints: Must maintain backward compatibility"
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```
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### Solve integration challenge
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```
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/agent-solve "Integrate Stripe webhooks with existing payment system
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Context: Using Express.js, PostgreSQL, Redis"
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```
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## Backends
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- **Primary**: Every Code (/solve multi-agent orchestration)
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- **Analysis**: Prometheus (knowledge graph, AST analysis)
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- **Validation**: Both platforms (testing, review)
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## Workflow
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### 1. Problem Decomposition
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- Break down complex problem into sub-tasks
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- Identify dependencies and constraints
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- Determine success criteria
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### 2. Parallel Solution Attempts
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- Agent 1: Analyze current implementation
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- Agent 2: Research best practices
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- Agent 3: Generate solution candidates
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- Agent 4: Validate solutions
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### 3. Solution Synthesis
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- Compare solution approaches
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- Merge best aspects from each
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- Create unified solution
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### 4. Implementation & Testing
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- Apply selected solution
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- Run comprehensive tests
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- Measure improvement metrics
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## Output
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```
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# Problem: Dashboard loading optimization
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## Analysis
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- Current load time: 10.2s
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- Bottleneck identified: N+1 queries in item fetching
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- Database queries: 1,247 per page load
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## Solution Candidates
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1. Query batching with DataLoader: 2.1s (79% improvement)
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2. Caching layer with Redis: 1.8s (82% improvement)
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3. Combined approach: 0.9s (91% improvement)
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## Selected Solution
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- Implemented DataLoader for batch queries
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- Added Redis caching for hot data
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- Optimized database indexes
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## Results
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✓ Load time: 10.2s → 0.9s (91% improvement)
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✓ Queries: 1,247 → 47 per page load
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✓ All tests passing: 127/127
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✓ No regressions detected
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Files modified:
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- src/services/dashboard.ts
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- src/api/items.ts
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- src/cache/redis.ts
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- prisma/schema.prisma
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```
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## Follow-up
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After solving:
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- `/agent-review` - Review implementation quality
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- `/agent-test` - Validate with tests
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- `/agent-context` - Understand solution architecture
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