Archon Manager

Archon Manager

Master Archon MCP for strategic project management and knowledge operations.

Archon is the command center for AI coding assistants, providing:

• Strategic Layer: Project management, task tracking, priority-based workflow (WHAT/WHEN)
• Knowledge Layer: RAG queries, web crawling, document processing, code examples
• Integration Layer: Connects Claude Code, Cursor, Windsurf with unified context

The Two-Layer Architecture:

• Archon (this skill) = Strategic (WHAT to build, WHEN)
• Skills = Tactical (HOW to build well)
• Together = Optimal outcomes

---

When to Use This Skill

• Project Setup: Creating hierarchical projects with features and tasks
• Task Management: Priority-based workflow (P0/P1/P2), status tracking
• Knowledge Queries: RAG searches across documentation, code examples, PDFs
• Strategic Planning: Using Archon to decide WHAT to build next
• Two-Layer Workflow: Implementing Archon (strategic) + Skills (tactical) pattern
• Context Preservation: Maintaining project knowledge across sessions
• AI Coordination: Synchronizing multiple AI assistants on same project

---

Core Concepts

1. The Two-Layer Architecture

┌──────────────────────────────────────────────────┐
│           ARCHON MCP SERVER                      │
│           (Strategic Layer)                      │
│                                                  │
│  • Project management & task tracking           │
│  • Priority-based workflow (P0/P1/P2)          │
│  • Knowledge queries (RAG)                      │
│  • Code example search                          │
│  • Progress tracking & metrics                  │
│  • Context preservation                         │
└─────────────────┬────────────────────────────────┘
                  │
                  │ invokes when needed
                  ↓
┌──────────────────────────────────────────────────┐
│      AI-DEV-STANDARDS SKILLS                     │
│      (Tactical Layer)                            │
│                                                  │
│  • Domain-specific expertise                    │
│  • Implementation patterns                      │
│  • Quality standards                            │
│  • Best practices                               │
└──────────────────────────────────────────────────┘

Key Insight: Archon manages WHAT to build and WHEN, Skills guide HOW to build it well.

2. Hierarchical Project Structure

Project
├── Feature 1
│   ├── Task 1.1 (P0)
│   ├── Task 1.2 (P1)
│   └── Task 1.3 (P2)
├── Feature 2
│   ├── Task 2.1 (P0)
│   └── Task 2.2 (P1)
└── Knowledge Base
    ├── Web pages
    ├── PDFs
    ├── Code examples
    └── Documentation

3. Priority-Based Workflow

• P0 (Critical): Must have for core value prop, blocks everything
• P1 (High Value): Important but can wait, high impact
• P2 (Nice to Have): Enhancement, low priority

4. Knowledge Management

• Web Crawling: Automatic sitemap detection, intelligent scraping
• Document Processing: PDFs with intelligent chunking
• Code Examples: Extract from documentation
• Semantic Search: Vector-based RAG with embeddings
• Source Organization: Tags, categories, versions

---

6-Phase Archon Implementation

Phase 1: Setup & Configuration

Goal: Install and configure Archon for your project

1.1 Prerequisites

# Required
- Docker Desktop (running)
- Node.js 18+
- Supabase account (cloud: https://supabase.com or local)
- LLM API key (OpenAI, Gemini, or Ollama)

1.2 Installation

# Clone Archon
git clone https://github.com/coleam00/Archon.git
cd Archon

# Create .env file
cp .env.example .env

# Edit .env with your credentials
SUPABASE_URL=your-supabase-url
SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key
OPENAI_API_KEY=your-openai-key  # or GEMINI_API_KEY, OLLAMA_URL

# Set up database (run SQL migrations in Supabase SQL Editor)
# See: database/migrations/

# Start Archon
docker compose up --build -d

# Verify running
# Frontend: http://localhost:3737
# API: http://localhost:8181
# MCP: http://localhost:8051

1.3 Connect to AI Assistant

For Claude Code (.claude/mcp-settings.json):

{
  "mcpServers": {
    "archon": {
      "command": "node",
      "args": ["/path/to/archon/mcp-server/dist/index.js"],
      "env": {
        "ARCHON_API_URL": "http://localhost:8181"
      }
    }
  }
}

For Cursor/Windsurf: Similar MCP configuration in settings

1.4 Verify Connection

// Test Archon connection
archon: list_projects()
// Should return empty list or existing projects

---

Phase 2: Project Creation

Goal: Set up hierarchical project structure

2.1 Create Project

// Using Archon MCP tool
archon: create_project({
  name: 'My Application',
  description: 'Full-stack web application for task management',
  status: 'active',
  metadata: {
    tech_stack: ['Next.js', 'Supabase', 'TypeScript'],
    team_size: 1,
    target_launch: '2025-12-01'
  }
})

// Returns: { project_id: "uuid", name: "My Application", ... }

2.2 Add Features

// Create major features
archon: create_feature({
  project_id: 'uuid',
  name: 'User Authentication',
  description: 'Complete auth system with email/OAuth',
  priority: 'P0',
  estimated_effort: '2 days'
})

archon: create_feature({
  project_id: 'uuid',
  name: 'Task Management',
  description: 'CRUD operations for tasks',
  priority: 'P0',
  estimated_effort: '3 days'
})

archon: create_feature({
  project_id: 'uuid',
  name: 'Team Collaboration',
  description: 'Share tasks with team members',
  priority: 'P1',
  estimated_effort: '4 days'
})

2.3 Break Down Features into Tasks

// Use AI-assisted task generation
archon: generate_tasks({
  feature_id: 'auth-feature-uuid',
  instructions: 'Break down authentication into implementation tasks',
  use_ai: true
})

// Or create manually
archon: create_task({
  feature_id: 'auth-feature-uuid',
  title: 'Implement email/password signup',
  description: 'Create signup form, API endpoint, database schema',
  priority: 'P0',
  status: 'todo',
  estimated_hours: 4,
  skills_to_use: ['api-designer', 'security-engineer', 'frontend-builder']
})

---

Phase 3: Knowledge Base Setup

Goal: Build comprehensive knowledge base for AI queries

3.1 Add Web Documentation

// Crawl entire documentation site
archon: crawl_website({
  url: 'https://nextjs.org/docs',
  max_depth: 3,
  follow_sitemap: true,
  tags: ['nextjs', 'documentation']
})

// Archon automatically:
// - Detects sitemap
// - Crawls pages
// - Extracts text
// - Chunks intelligently
// - Generates embeddings
// - Stores in vector database

3.2 Add PDF Documents

// Upload PDFs (design docs, specs, research papers)
archon: add_document({
  file_path: '/path/to/architecture-spec.pdf',
  type: 'pdf',
  tags: ['architecture', 'design'],
  project_id: 'uuid'
})

// Archon automatically:
// - Extracts text from PDF
// - Chunks by sections/pages
// - Generates embeddings
// - Indexes for search

3.3 Add Code Examples

// Extract code examples from repos or docs
archon: extract_code_examples({
  source_url: 'https://github.com/vercel/next.js/tree/canary/examples',
  tags: ['nextjs', 'examples'],
  language_filter: ['typescript', 'javascript']
})

3.4 Organize Knowledge

// Tag and categorize
archon: update_source({
  source_id: 'uuid',
  tags: ['authentication', 'security', 'best-practices'],
  category: 'implementation-guides',
  version: '1.0'
})

---

Phase 4: The Archon+Skills Workflow

Goal: Use two-layer architecture for optimal development

4.1 Phase 1: Strategic Planning (Archon)

Goal: Understand WHAT to build and WHY

// 1. Get next priority task
const task = archon:get_next_task({
  project_id: "uuid",
  filter_by: "status",
  filter_value: "todo",
  sort_by: "priority"  // P0 first
})
// → { id: "P0-3", title: "Implement User Authentication", priority: "P0" }

// 2. Get task details
const details = archon:get_task({
  task_id: "P0-3"
})
// → Complete task info: description, requirements, dependencies

// 3. Research the domain (RAG query)
const research = archon:perform_rag_query({
  query: "JWT authentication Next.js best practices",
  project_id: "uuid",
  match_count: 5
})
// → Top 5 most relevant knowledge base entries with context

// 4. Find code examples
const examples = archon:search_code_examples({
  query: "auth middleware Next.js TypeScript",
  match_count: 3
})
// → Relevant code examples from knowledge base

// 5. Mark as doing
archon:update_task({
  task_id: "P0-3",
  updates: { status: "doing" }
})

4.2 Phase 2: Tactical Execution (Skills)

Goal: Implement with domain expertise and best practices

// 6. Identify required skills (from task.skills_to_use)
// → ["api-designer", "security-engineer", "frontend-builder"]

// 7. Invoke skills for guidance
// (AI assistant automatically invokes these skills based on context)

// 8. Implement following both:
//    - Archon research (RAG results + code examples)
//    - Skill guidance (best practices + patterns)

// 9. Build the feature

4.3 Phase 3: Quality Validation

Goal: Ensure quality before marking complete

// 10. Apply quality checks
// - Invoke testing-strategist skill
// - Invoke security-engineer skill
// - Run tests, validate security

// 11. Update task to review
archon:update_task({
  task_id: "P0-3",
  updates: { status: "review" }
})

// 12. After user validation → mark done
archon:update_task({
  task_id: "P0-3",
  updates: { status: "done" }
})

// 13. Get next task
const nextTask = archon:get_next_task({
  project_id: "uuid",
  filter_by: "status",
  filter_value: "todo"
})
// → Repeat cycle

---

Phase 5: Advanced Knowledge Operations

Goal: Leverage Archon's RAG capabilities

5.1 Semantic Search Patterns

// Broad research query
archon: perform_rag_query({
  query: 'How to implement real-time features in Next.js',
  match_count: 10,
  similarity_threshold: 0.7
})

// Specific technical query
archon: perform_rag_query({
  query: 'Next.js middleware authentication example code',
  match_count: 3,
  filter_tags: ['nextjs', 'authentication', 'code-example']
})

// Architecture decision query
archon: perform_rag_query({
  query: 'PostgreSQL vs MongoDB for user data',
  match_count: 5,
  filter_tags: ['database', 'architecture']
})

5.2 Context-Aware Queries

// Query within project context
archon: perform_rag_query({
  query: 'How should we structure our authentication?',
  project_id: 'uuid', // Uses project's knowledge base
  match_count: 5
})

// Query specific feature context
archon: perform_rag_query({
  query: 'Best practices for this feature',
  feature_id: 'auth-feature-uuid',
  match_count: 3
})

5.3 Knowledge Base Versioning

// Version project documentation
archon: create_doc_version({
  project_id: 'uuid',
  document_name: 'Architecture Decision Record',
  content: '...',
  version: '1.0.0',
  tags: ['architecture', 'decisions']
})

// Query historical context
archon: get_doc_history({
  project_id: 'uuid',
  document_name: 'Architecture Decision Record'
})

---

Phase 6: Progress Tracking & Metrics

Goal: Monitor project health and velocity

6.1 Project Metrics

// Get project overview
archon: get_project_metrics({
  project_id: 'uuid'
})
// Returns:
// {
//   total_features: 5,
//   total_tasks: 23,
//   p0_tasks: 8,
//   p1_tasks: 10,
//   p2_tasks: 5,
//   tasks_by_status: {
//     todo: 15,
//     doing: 3,
//     review: 2,
//     done: 3
//   },
//   completion_percentage: 13,
//   knowledge_base_entries: 147
// }

6.2 Velocity Tracking

// Tasks completed per week
archon: get_velocity({
  project_id: 'uuid',
  time_period: 'week'
})

// Burndown chart data
archon: get_burndown({
  project_id: 'uuid',
  sprint_id: 'sprint-1'
})

6.3 Real-Time Updates

Archon uses Socket.IO for real-time progress updates:

• Task status changes
• Knowledge base additions
• Project metrics updates
• Team collaboration events

---

Integration Patterns

Pattern 1: Solo Developer Workflow

// Morning routine
const nextTask = archon:get_next_task({ project_id: "uuid" })
const research = archon:perform_rag_query({
  query: nextTask.title + " implementation",
  match_count: 5
})

// Work on task (using Skills for implementation)
// ...

// End of day
archon:update_task({ task_id: nextTask.id, status: "review" })

Pattern 2: Team Collaboration

// Team lead creates project structure
archon: create_project({ name: 'Team Project' })
archon: create_feature({ name: 'Backend API', assigned_to: 'developer-1' })
archon: create_feature({ name: 'Frontend UI', assigned_to: 'developer-2' })

// Each team member queries same knowledge base
archon: perform_rag_query({ query: '...', project_id: 'uuid' })

// Real-time sync of task status across team

Pattern 3: Multi-AI-Assistant Setup

// Claude Code for implementation
// Cursor for refactoring
// Windsurf for testing

// All connected to same Archon instance
// Same project, same tasks, same knowledge base
// Coordinated through Archon's unified context

---

Archon MCP Tools Reference

Project Management

// Projects
archon: create_project({ name, description, status, metadata })
archon: list_projects()
archon: get_project({ project_id })
archon: update_project({ project_id, updates })
archon: delete_project({ project_id })

// Features
archon: create_feature({ project_id, name, description, priority })
archon: list_features({ project_id })
archon: update_feature({ feature_id, updates })

// Tasks
archon: create_task({ feature_id, title, description, priority, status })
archon: get_next_task({ project_id, filter_by, sort_by })
archon: get_task({ task_id })
archon: update_task({ task_id, updates })
archon: list_tasks({ project_id, filter_by, filter_value })
archon: generate_tasks({ feature_id, instructions, use_ai })

Knowledge Management

// RAG Queries
archon:perform_rag_query({query, project_id?, match_count, similarity_threshold, filter_tags?})
archon:search_code_examples({query, match_count, language_filter?})

// Content Ingestion
archon:crawl_website({url, max_depth, follow_sitemap, tags})
archon:add_document({file_path, type, tags, project_id})
archon:extract_code_examples({source_url, tags, language_filter})

// Knowledge Base Management
archon:list_sources({project_id, filter_tags?})
archon:update_source({source_id, tags, category, version})
archon:delete_source({source_id})

Metrics & Analytics

archon: get_project_metrics({ project_id })
archon: get_velocity({ project_id, time_period })
archon: get_burndown({ project_id, sprint_id })

---

Best Practices

1. Start with Clear Project Structure

// Good: Hierarchical and organized
Project: "E-commerce Platform"
├── Feature: "User Authentication" (P0)
│   ├── Task: "Implement signup" (P0)
│   ├── Task: "Implement login" (P0)
│   └── Task: "Password reset" (P1)
├── Feature: "Product Catalog" (P0)
│   ├── Task: "Product CRUD API" (P0)
│   ├── Task: "Product listing UI" (P0)
│   └── Task: "Search functionality" (P1)

// Bad: Flat, disorganized
- Task: "Build everything"
- Task: "Make it work"
- Task: "Deploy"

2. Use Priority Effectively

P0 (Critical): Must have for MVP, blocks everything

• Core value proposition features
• Critical bugs
• Security vulnerabilities

P1 (High Value): Important, high impact

• Significant enhancements
• Important optimizations
• Major integrations

P2 (Nice to Have): Can wait

• Polish and refinement
• Minor features
• Non-critical improvements

3. Build Comprehensive Knowledge Base

// Add diverse sources
archon: crawl_website({ url: 'https://docs.framework.com' })
archon: add_document({ file_path: 'architecture-spec.pdf' })
archon: extract_code_examples({ source_url: 'https://github.com/...' })

// Tag consistently
tags: ['category', 'technology', 'type']
// e.g., ["authentication", "nextjs", "tutorial"]

4. Use RAG Queries Strategically

// Before starting task
const research = archon:perform_rag_query({
  query: "How to implement " + task.title,
  match_count: 5
})

// During implementation (specific questions)
const answer = archon:perform_rag_query({
  query: "How to handle edge case X",
  match_count: 3
})

// Architecture decisions
const guidance = archon:perform_rag_query({
  query: "Should I use pattern A or pattern B for ...",
  match_count: 5
})

5. Maintain Task Status Discipline

todo → doing → review → done

• todo: Not started
• doing: Currently working on (limit to 1-3 tasks)
• review: Ready for validation
• done: Completed and validated

Update status immediately when changing.

---

Troubleshooting

Issue: Archon Not Connecting

Symptoms: MCP tools not available in AI assistant

Solutions:

1. Check Docker containers running: docker ps
2. Verify ports not blocked: 3737, 8181, 8051, 8052
3. Check MCP configuration in .claude/mcp-settings.json
4. Restart AI assistant after config changes
5. Check Archon logs: docker logs archon-api

Issue: RAG Queries Return No Results

Symptoms: Empty results from perform_rag_query

Solutions:

1. Verify knowledge base has content: archon:list_sources()
2. Check embeddings generated (wait for processing)
3. Lower similarity_threshold (default 0.7, try 0.5)
4. Broader query terms
5. Check source tags match filter_tags

Issue: Tasks Not Appearing

Symptoms: get_next_task returns empty

Solutions:

1. Verify project exists: archon:list_projects()
2. Check task status filters
3. Ensure tasks created for correct feature/project
4. Verify tasks not all marked "done"

Issue: Slow Performance

Symptoms: RAG queries or task operations slow

Solutions:

1. Check Docker resource allocation
2. Optimize knowledge base (remove duplicates)
3. Use match_count appropriately (5-10, not 100)
4. Consider upgrading Supabase plan (if cloud)
5. Use local Ollama instead of API calls

---

Integration with ai-dev-standards

Using Archon + Skills Together

Archon provides (Strategic):

• WHAT to build (task from priority queue)
• WHEN to build it (P0/P1/P2 ordering)
• Context (RAG queries, project knowledge)

Skills provide (Tactical):

• HOW to build well (best practices, patterns)
• Domain expertise (security, performance, etc.)
• Quality standards (testing, validation)

Example Workflow:

// 1. Strategic (Archon)
const task = archon:get_next_task({project_id: "uuid"})
const research = archon:perform_rag_query({query: task.title})
archon:update_task({task_id: task.id, status: "doing"})

// 2. Tactical (Skills)
// AI automatically invokes: api-designer, security-engineer, frontend-builder
// Based on task.skills_to_use

// 3. Implementation
// Build following: Archon research + Skill guidance

// 4. Quality (Skills)
// AI invokes: testing-strategist, security-engineer

// 5. Complete (Archon)
archon:update_task({task_id: task.id, status: "done"})

Task-to-Skill Mapping

When creating tasks in Archon, specify skills_to_use:

archon: create_task({
  title: 'Implement authentication API',
  skills_to_use: ['api-designer', 'security-engineer']
  // ...
})

This tells AI assistants which skills to invoke during implementation.

---

Success Metrics

You're using Archon effectively when:

1. ✅ Clear project structure: Hierarchical, organized, prioritized
2. ✅ Always know what's next: get_next_task guides daily work
3. ✅ Rich knowledge base: RAG queries return relevant, useful results
4. ✅ Visible progress: Metrics show steady completion
5. ✅ Status discipline: Tasks flow smoothly through workflow
6. ✅ Context preserved: Can resume project after breaks without loss
7. ✅ Quality maintained: Two-layer workflow (Archon + Skills) produces excellent results

---

Quick Reference

Common Commands

// Daily workflow
archon: get_next_task({ project_id })
archon: perform_rag_query({ query, match_count: 5 })
archon: update_task({ task_id, status: 'doing' })
// ... work ...
archon: update_task({ task_id, status: 'done' })

// Project setup
archon: create_project({ name, description })
archon: create_feature({ project_id, name, priority: 'P0' })
archon: create_task({ feature_id, title, priority: 'P0' })

// Knowledge building
archon: crawl_website({ url, tags })
archon: add_document({ file_path, tags })

// Progress tracking
archon: get_project_metrics({ project_id })
archon: list_tasks({ project_id, filter_by: 'status', filter_value: 'done' })

Priority Guidelines

• P0: Core features, critical bugs, security issues
• P1: Important enhancements, optimizations, integrations
• P2: Polish, minor features, nice-to-haves

Status Flow

todo → doing (working) → review (validate) → done (complete)

---

Summary

Archon is the strategic command center that:

• Manages WHAT to build and WHEN (priority queue)
• Provides context through RAG (knowledge base)
• Tracks progress and metrics
• Coordinates AI assistants

Combined with Skills (HOW to build well), Archon enables:

• Strategic coherence (all work aligned with goals)
• Tactical excellence (domain expertise + best practices)
• Context preservation (no lost knowledge)
• Quality assurance (multi-layer validation)

Key Takeaway: Use Archon for strategic planning (WHAT/WHEN), invoke Skills for implementation guidance (HOW). Together they create optimal outcomes.

For detailed integration patterns: See DOCS/ARCHON-INTEGRATION.md For complete example: See EXAMPLES/archon-workflow-example.md