skills-tree 1.8.0

Architect — taxonomy-driven skill recommendation engine for AI agent builders

Skills Tree

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

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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

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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

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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

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Comparison vs Alternatives

Feature	Skills Tree	LangChain Hub	Hugging Face Hub	Custom YAML files
AI agent skill taxonomy	✅ 360+ skills	⚠️ Prompt-focused	❌ Model-focused	❌ None
Versioned skill evolution	✅ v1→v2→v3	❌	❌	❌
Runnable code examples	✅ Every skill	⚠️ Some	⚠️ Some	❌
Benchmarks included	✅ Head-to-head	❌	⚠️ Leaderboards	❌
MCP server integration	✅ Built-in	❌	❌	❌
Multi-agent blueprints	✅ 7+ blueprints	⚠️ Templates	❌	❌
CLI + Python API	✅ Both	⚠️ Python only	✅ Both	❌
Community-governed	✅ Open PRs	⚠️ Curated	✅ Open	✅ (yours only)
Failure modes documented	✅ Every skill	❌	❌	❌
Free & open source (MIT)	✅	⚠️ Mixed	✅	✅

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🚀 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.

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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

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🗂️ The 17 Skill Categories

#	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

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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

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

---

🏗️ Blueprints — Production Architectures

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

---

📊 Benchmarks

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	→

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🤝 How to Contribute

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 PR

Full guide: CONTRIBUTING.md

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🗺️ 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

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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.

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⭐ Star this repo · 📦 Install from PyPI · 🌐 Browse Skills · 🤝 Contribute · 💖 Sponsor

The AI Agent Skill OS — built by the community, for the community.