✨ v1.0.0 — Sprint C-12

# The AI Agent Skill
Navigator

Taxonomy-driven skill recommendations, calibrated learning paths, and architecture blueprints for every AI agent goal. Install in seconds. Works with Claude Desktop, Cursor, and VS Code.

Get Started → ⭐ Star on GitHub
11
Goal Clusters
5
CLI Commands
4
MCP Tools
5
REST Endpoints
100%
Layer Parity

## What is Skills Tree?

Skills Tree is an open-source engine that answers one question: “What do I need to learn to build a [goal]?”

It combines a hand-curated goal taxonomy, a calibrated skill graph, and a recommendation engine to produce ranked, confidence-scored learning paths for AI agent builders.

🎯

### Goal Taxonomy

11 goal clusters mapping every major AI agent archetype to its required skill set.

🕷️

### Skill Graph

Weighted, directed graph of skills with learning-time estimates and dependency edges.

🧠

### Recommendation Engine

C-08 calibrated ranker achieving 81.2% precision@5 and 99.6% recall@10.

📍

### Blueprint Generator

Full architecture blueprints with deployment type, risk analysis, and ordered learning path.

## Quick Start

From zero to your first recommendation in under 60 seconds.

    # 1. Install
    pip install skills-tree

    # 2. Validate stack
    skills-tree validate

    # 3. Get recommendations
    skills-tree recommend --goal "Coding Agent"

    # 4. Generate a blueprint
    skills-tree blueprint --goal "Coding Agent"

Or launch a ready-to-use environment instantly:

☁️ Open in Codespaces

## Install CLI

The `skills-tree` command exposes all engine capabilities from your terminal.

    skills-tree recommend --goal "Coding Agent"
    skills-tree recommend --goal "RAG Assistant" --experience intermediate --time-budget 80
    skills-tree recommend --goal "Coding Agent" --format table

    skills-tree blueprint --goal "Coding Agent"
    skills-tree blueprint --goal "RAG Assistant" --format pretty

    skills-tree goals
    skills-tree goals --format table

    skills-tree skills
    skills-tree skills --format table

    skills-tree validate
    skills-tree validate --goal "Coding Agent"

## MCP Integration

Skills Tree ships an MCP server exposing 4 tools so Claude Desktop, Cursor, and VS Code can call live Architect recommendations.

    {
      "mcpServers": {
        "skills-tree-architect": {
          "command": "python",
          "args": ["-m", "mcp.server"],
          "cwd": "/path/to/skills-tree"
        }
      }
    }

    {
      "mcpServers": {
        "skills-tree-architect": {
          "command": "python",
          "args": ["-m", "mcp.server"],
          "cwd": "/path/to/skills-tree"
        }
      }
    }

    {
      "servers": {
        "skills-tree-architect": {
          "type": "stdio",
          "command": "python",
          "args": ["-m", "mcp.server"],
          "cwd": "/path/to/skills-tree"
        }
      }
    }

See MCP Quickstart for full setup instructions.

## API Documentation

The Architect REST API (FastAPI) serves all engine capabilities over HTTP. Swagger UI and ReDoc are built-in.

    # Start the API server
    uvicorn api.main:app --reload --port 8000

    # Swagger UI:  http://localhost:8000/docs
    # ReDoc:       http://localhost:8000/redoc
    # OpenAPI:     http://localhost:8000/openapi.json

`GET /health`

Liveness probe

`GET /goals`

All taxonomy goals

`GET /skills`

All graph skills

`POST /recommend`

Calibrated recommendations

`POST /blueprint`

Full architecture blueprint

## Example Recommendations

Recommendations for Coding Agent (intermediate, no budget):

    {
      "goal": "Coding Agent",
      "goal_id": "G01",
      "confidence_score": 0.86,
      "required_skills": [
        { "id": "skill:prompt-engineering",  "name": "Prompt Engineering",  "rank": 1 },
        { "id": "skill:code-generation",      "name": "Code Generation",      "rank": 2 },
        { "id": "skill:function-calling",     "name": "Function Calling",     "rank": 3 },
        { "id": "skill:error-recovery",       "name": "Error Recovery",       "rank": 4 },
        { "id": "skill:context-management",   "name": "Context Management",   "rank": 5 }
      ],
      "calibration_applied": true
    }

## Example Blueprint

    {
      "id": "blueprint-20260615143000",
      "title": "Coding Agent",
      "goal_id": "G01",
      "architecture_type": "Single-Agent",
      "confidence_score": 0.86,
      "required_skills": [...],
      "learning_path": ["Prompt Engineering", "Code Generation", "Function Calling"],
      "risks": ["LLM hallucination in code output", "Tool call error handling"]
    }

## Architecture Diagram

All four layers share a single execution path — no duplicated logic.

💻 CLI (skills-tree)
↓
🤖 MCP Server (stdio)
↓
🌐 FastAPI Service (/recommend, /blueprint, /goals, /skills)
↓
⚙️ Engine (GoalTaxonomyParser · SkillsGraph · RecommendationEngine · RankingCalibrator · BlueprintGenerator)

## GitHub Repository & Community

🐛

### Issues

Report bugs or request features.

💬

### Discussions

Ask questions and share use cases.

🔀

### Pull Requests

Contribute to the engine or taxonomy.

☁️

### Codespaces

Launch a working dev environment instantly.