3b128ba3bd
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>
106 lines
2.6 KiB
Markdown
106 lines
2.6 KiB
Markdown
# 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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