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The largest open, dependency-mapped knowledge graph for AI agents.
| 368 Skills | 780+ Connections | MIT Licensed |
|---|---|---|
| Versioned & benchmarked | Dependency-mapped | Community-governed |
▶ Explore Skills → · ▶ Generate Blueprint → · ▶ GitHub Repository →
Response SLA: Issues
<72h· Pull Requests<7 days· Governance Reviews<5 days
🌐 Live Docs · 📦 PyPI · 🗺️ Systems · 🏗️ Blueprints · 📊 Benchmarks · 🤝 Contribute · 🗺 Roadmap
🌐 Read in your language: 🇬🇧 English · 🇸🇦 العربية · 🇨🇳 中文 · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇮🇳 हिन्दी · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇧🇷 Português · 🇷🇺 Русский
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 categoriesOr 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
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
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
┌─────────────────────────────────────────────────────────┐
│ 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
| Feature | Skills Tree | LangChain Hub | Hugging Face Hub | Custom YAML files |
|---|---|---|---|---|
| AI agent skill taxonomy | ✅ 360+ skills | ❌ Model-focused | ❌ None | |
| Versioned skill evolution | ✅ v1→v2→v3 | ❌ | ❌ | ❌ |
| Runnable code examples | ✅ Every skill | ❌ | ||
| Benchmarks included | ✅ Head-to-head | ❌ | ❌ | |
| MCP server integration | ✅ Built-in | ❌ | ❌ | ❌ |
| Multi-agent blueprints | ✅ 7+ blueprints | ❌ | ❌ | |
| CLI + Python API | ✅ Both | ✅ Both | ❌ | |
| Community-governed | ✅ Open PRs | ✅ Open | ✅ (yours only) | |
| Failure modes documented | ✅ Every skill | ❌ | ❌ | ❌ |
| Free & open source (MIT) | ✅ | ✅ | ✅ |
If you're new, read these first. Each ships with runnable code, typed I/O, failure modes, and a model-comparison table.
- 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
- 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
- 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 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.
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
| # | Category | Skills | What It Covers |
|---|---|---|---|
| 01 | 👁️ Perception | 36 | Text, images, PDFs, code, sensors, databases, screens |
| 02 | 🧠 Reasoning | 45 | Planning, deduction, abduction, causal chains, commonsense |
| 03 | 🗄️ Memory | 19 | Working, episodic, semantic, vector, injection, forgetting |
| 04 | ⚡ Action Execution | 21 | File I/O, HTTP, email, shell, database writes |
| 05 | 💻 Code | 28 | Write, run, debug, review, refactor, test, deploy |
| 06 | 💬 Communication | 15 | Summarize, translate, draft, argue, adapt tone |
| 07 | 🔧 Tool Use | 33 | APIs — GitHub, Slack, Stripe, OpenAI, MCP, A2A |
| 08 | 🎭 Multimodal | 14 | Images, audio, video, VQA, 3D, charts |
| 09 | 🤖 Agentic Patterns | 23 | ReAct, CoT, ToT, MCTS, LATS, RAG, Debate |
| 10 | 🖥️ Computer Use | 20 | Click, type, scroll, OCR, terminal, VM, a11y tree |
| 11 | 🌐 Web | 17 | Search, scrape, crawl, login, fill forms, parse RSS |
| 12 | 📊 Data | 18 | ETL, SQL, embeddings, time series, anomaly detection |
| 13 | 🎨 Creative | 14 | Copywriting, image prompts, SVG, music, scripts |
| 14 | 🔒 Security | 13 | Sandboxing, secret scanning, audit logs, rollback |
| 15 | 🎼 Orchestration | 22 | Multi-agent, state machines, retry, consensus |
| 16 | 🏺 Domain-Specific | 28 | Medical, legal, finance, DevOps, education, science |
| 17 | 🛠️ Infrastructure | 1 | Dependency auditing & supply-chain tooling |
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
| System | Skills Used | Use Case |
|---|---|---|
| Research Agent | Web search + RAG + Summarize | Deep research automation |
| Coding Agent | Code reading + Write + Debug | End-to-end code generation |
| Code Reviewer | Code reading + Reasoning + Comment gen | Automated PR reviews |
| Data Pipeline Agent | DB reading + ETL + Anomaly detection | Automated data ops |
| Customer Support Bot | Memory injection + Intent + Response gen | Personalized support |
| Computer Use Agent | Screen reading + OCR + Click | Full GUI automation |
| Blueprint | Description |
|---|---|
| RAG Stack | Embed → store → retrieve → generate, fully wired |
| Multi-Agent Workflow | Sequential orchestration with handoffs |
| Multi-Agent Mesh | N specialists + orchestrator, parallel execution |
| Human-in-the-Loop | Approval gates, escalation, audit trails |
| Self-Healing Agent | Error detection, retry logic, rollback |
| Memory-First Agent | Profile + episodic + vector memory combined |
| Benchmark | Winner | Margin | Link |
|---|---|---|---|
| ReAct vs LATS (HotpotQA) | LATS | +8.3% accuracy | → |
| RAG retrieval strategies | HyDE | +12% recall | → |
| Memory injection methods | Top-K semantic | Best cost/quality | → |
| Function calling comparison | Claude 3.7 | +6% tool accuracy | → |
| Type | What It Is | PR Title Format |
|---|---|---|
| New Skill | A capability not yet indexed | feat: add [skill] to [category] |
| Skill Upgrade | Bump v1→v2 with better content | improve: [skill] — v1→v2 |
| Benchmark | Head-to-head with real numbers | benchmark: [skill-a] vs [skill-b] |
| System / Blueprint | Multi-skill workflow or architecture | system: add [name] |
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 PRFull guide: CONTRIBUTING.md
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
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.
⭐ Star this repo · 📦 Install from PyPI · 🌐 Browse Skills · 🤝 Contribute · 💖 Sponsor
The AI Agent Skill OS — built by the community, for the community.