Skills Tree

📆 This Week's Highlights — June 22, 2026

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The AI Agent Skill OS — Build Smarter Agents, Faster

367 skills across 17 categories. Versioned, benchmarked, and openly evolving.
The shared operating system for AI agent capabilities — stop rediscovering, start building on what the community has already proven.

🌐 Live Docs · 📦 PyPI · 🗺️ Systems · 🏗️ Blueprints · 📊 Benchmarks · 🤝 Contribute · 🗺 Roadmap

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⚡ Quick Install

pip install skills-tree

# Query the skills taxonomy programmatically
from skills_tree import SkillsTree

st = SkillsTree()
skill = st.get("rag")           # fetch a skill by ID
results = st.search("memory")   # full-text search across 360+ skills
cats = st.categories()          # list all 17 categories

Or use the CLI:

skills-tree search "memory injection"
skills-tree show rag
skills-tree list --category reasoning

→ Full install guide: docs/installation.md · Quick start: docs/quickstart.md

The Problem

Every AI agent builder rediscovers the same skills from scratch.

Someone learns RAG the hard way. Someone else figures out memory injection at 2am. A third person spends a week benchmarking ReAct vs LATS — and never shares the results. A fourth discovers the same failure modes you already hit last month.

That collective knowledge is disappearing into Slack threads, private repos, and Twitter bookmarks.

Skills Tree fixes that. → Read the full problem statement

What This Is

Skills Tree is the shared operating system for AI agent capabilities.

A living, versioned, community-powered index of everything an agent can do — at its best, documented with working code, real benchmarks, failure modes, and evolution history.

Battle-tested skills (🟢 verified) are production-ready and copy-paste safe. Yellow/unscanned skills are the community's TODO list — open files, real problem space, and the clearest signal of where contributions are most useful.

→ Real-world use cases · Why Skills Tree vs alternatives

Architecture

┌─────────────────────────────────────────────────────────┐
│                     skills-tree                         │
│                   (Python package)                      │
├─────────────┬───────────────────┬───────────────────────┤
│   CLI       │   Python API      │   MCP Server          │
│ (Typer)     │ (SkillsTree class)│ (tools/mcp/)          │
├─────────────┴───────────────────┴───────────────────────┤
│              Skills Data Layer (Markdown + YAML)        │
│  skills/  │  systems/  │  blueprints/  │  benchmarks/   │
├─────────────────────────────────────────────────────────┤
│  Validation Engine  │  Search Index  │  Quality Reports │
│  (tools/)           │  (Lunr.js)     │  (meta/)         │
└─────────────────────────────────────────────────────────┘

→ Full architecture deep-dive: docs/architecture.md

Comparison vs Alternatives

🚀 Start Here — Battle-Tested Skills

If you're new, read these first. Each ships with runnable code, typed I/O, failure modes, and a model-comparison table.

Agent reasoning loops

• ReAct — Thought → Action → Observation, the foundation of tool-using agents
• Chain of Thought — explicit step-by-step reasoning + self-consistency
• Tree of Thought — branched reasoning with scoring + beam search
• Reflection / Reflexion — critique → revise loop on top of any output
• Self-Consistency — sample N chains, majority-vote
• Planning — typed, DAG-validated plans your executor can run
• Task Decomposition — break a goal into atomic, runnable subtasks

Retrieval & memory

• RAG — chunk → embed → retrieve → cite, end-to-end with confidence + threshold
• Vector Store Retrieval — typed top-k cosine search with metadata filtering
• Embedding Generation — batched, content-hash-cached, Matryoshka-truncatable
• Memory Injection — top-K user memories per turn
• Short-Term Memory — token-budgeted rolling window

Calling LLMs in production

• Function / Tool Calling — the primitive that turns an LLM into an agent
• OpenAI API — chat, structured outputs, tools, embeddings, streaming, retry
• Anthropic API — Claude with tool loop, prompt caching, streaming

Code, Web & Security

• Code Generation — spec → AST-validated source with self-repair
• Web Search — Tavily/Serper/Brave with recency + TTL cache
• Input Sanitization — 4-layer defense: structural + boundary + content + isolation

The full battle-tested set is auto-listed in meta/QUALITY-REPORT.md.

What's Inside

skills-tree/
│
├── skills/          → 360 atomic skill files (50 battle-tested, 308 stubs)
├── systems/         → Multi-skill workflows (research agent, code reviewer...)
├── blueprints/      → Copy-paste production architectures
├── benchmarks/      → Head-to-head, reproducible skill comparisons
├── labs/            → Experimental & bleeding-edge capabilities
│
├── docs/            → Interactive web UI (GitHub Pages) + MkDocs docs site
├── i18n/            → Localized READMEs (10 languages)
├── meta/            → Schema, glossary, frameworks, roadmap, changelog
├── mcp/             → MCP server integration
└── tests/           → pytest test suite

🗂️ The 17 Skill Categories

A Skill in 60 Seconds

Every skill file is self-contained and production-ready:

# Memory Injection
Category: memory | Level: intermediate | Stability: stable | Version: v2

## Description
Dynamically inject relevant past memories into an agent's system prompt
before each turn — giving the model user context without filling the window.

## Example
```python
client.messages.create(
    system=f"{base_system}\n\n## Memory\n{top_k_memories}",
    messages=[{"role": "user", "content": user_message}]
)
```

Every skill includes: ✅ typed inputs/outputs · ✅ runnable Python code · ✅ frameworks table · ✅ failure modes · ✅ version history

🗺️ Systems — Multi-Skill Workflows

🏗️ Blueprints — Production Architectures

📊 Benchmarks

🤝 How to Contribute

git clone https://github.com/SamoTech/skills-tree.git
cp meta/skill-template.md skills/05-code/my-new-skill.md
# Fill in every section → open a PR

Full guide: CONTRIBUTING.md

🗺️ Roadmap

See the full plan: meta/ROADMAP.md

Near-term (v2.x): Skill dependency graph · Skill Paths · JSON/YAML export · Community ratings
Medium-term (v3.0): LangChain Hub / MCP registry integration · 500+ skills
Long-term: Skills Tree becomes the canonical reference for AI agent capabilities

Vision

AI agents are becoming teammates, not tools.

Skills Tree is the shared foundation they run on — a living OS of capabilities that the community builds, tests, and evolves together.

Every skill added here saves every agent builder who comes after you.