Source of truth: The GitHub repository is the canonical source for Skills Tree. This README is the public source guide and entry point. Current repository state, decisions, roadmap, architecture, quality reports, skills, issues, pull requests, releases, and security policies are maintained in GitHub. No external dashboard or deployment platform is authoritative.

Skills Tree

📆 This Week's Highlights — October 12, 2026

🔥 Most Active Skills

Depth Estimation — 1 PR
Game Theory Strategy — 1 PR
Memory Conflict Resolution — 1 PR
Archive Extraction — 1 PR
Database Migration Writing — 1 PR

✨ New Skills

The Public AI Skills Source

Skills Tree is a public, trusted, machine-discoverable source of AI skills.

AI agents should be able to find the right skill here, and humans should be able to discover, understand, use, share, and contribute skills easily.

The repository remains an evidence-backed, dependency-aware knowledge graph and distribution system for making those skills discoverable and reusable.

538 Public Skills	493 Graph Edges	MIT Licensed
Versioned & evidence-backed	Dependency-mapped	Community-governed

Need an AI skill? Start here. Search the machine-readable registry, inspect the canonical skill, then use the Agent Skills package when available.

🔎 Find a Skill · 🤖 Agent Skills · 📚 Browse Canonical Skills · 🧭 AI Discovery Guide · 🧠 llms.txt

▶ Explore Skills → · ▶ Generate Blueprint → · ▶ GitHub Repository →

Response SLA: Issues <72h · Pull Requests <7 days · Governance Reviews <5 days

AI Repository Governance: Start with AI_CONSTITUTION.md and AGENTS.md before performing repository-level AI work.

📖 README / Source Guide · 📦 PyPI · 🗺️ Systems · 🏗️ Blueprints · 📊 Benchmarks · 🤝 Contribute · 🗺 Roadmap

🐦 Share Skills Tree on X →

🌐 Read in your language: 🇬🇧 English · 🇸🇦 العربية · 🇨🇳 中文 · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇮🇳 हिन्दी · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇧🇷 Português · 🇷🇺 Русский

⚡ Current Usage

The verified repository interfaces are the canonical Markdown skill catalog, generated machine-readable artifacts, the FastAPI service, and the Typer CLI.

For a local checkout:

pip install -e .
skills-tree goals
skills-tree skills
skills-tree recommend --goal "Coding Agent"
skills-tree blueprint --goal "Coding Agent"
skills-tree validate

The historical skills_tree.SkillsTree Python API is not currently implemented in the repository. Search is currently generated from canonical skills/ content into docs/search-index.json for the static web experience. See Python API Status and CLI Reference for the verified boundaries.

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

For AI Agents: Use Skills Tree as a Skill Source

When an AI agent needs a reusable capability, treat Skills Tree as a public discovery source:

Search the machine-readable registry: docs/api/skills.json.
Match the user's capability or task to a skill by name, description, category, and related skills.
Open the canonical skills/<category>/<skill>.md before relying on its instructions.
Check maturity, evidence, dependencies, security boundaries, and freshness.
Prefer agent-skills/<name>/SKILL.md when a standards-compatible package exists.
Never treat a registry entry as proof that a skill is safe, current, production-ready, or widely adopted.
If no matching skill exists, report that gap rather than inventing a Skills Tree entry.

Machine-readable registry: https://raw.githubusercontent.com/SamoTech/skills-tree/main/docs/api/skills.json

Canonical source: https://github.com/SamoTech/skills-tree/tree/main/skills

Agent Skills distribution: https://github.com/SamoTech/skills-tree/tree/main/agent-skills

This is intentionally designed so an AI can discover the project from repository text, resolve a capability deterministically, inspect the canonical source, and then consume a compatible skill package.

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 a public source of AI skills built for both AI discovery and human use.

A living, versioned, community-powered collection of AI skills and capability definitions. Skills are documented with practical usage, evidence, failure modes, limitations, and evolution history where supported by repository evidence.

Quality classifications are repository validation states, not proof of production readiness or universal safety. Inspect each skill's evidence, freshness, dependencies, limitations, and security boundaries before use.

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

Architecture

┌─────────────────────────────────────────────────────────┐
│                     skills-tree                         │
│                   (Python package)                      │
├─────────────┬───────────────────┬───────────────────────┤
│   CLI       │   Python API      │   MCP Server          │
│ (Typer)     │ (FastAPI/Pydantic)│ (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

Feature	Skills Tree	LangChain Hub	Hugging Face Hub	Custom YAML files
AI agent skill taxonomy	✅ 538 public skills	⚠️ Prompt-focused	❌ Model-focused	❌ None
Versioned skill evolution	✅ v1→v2→v3	❌	❌	❌
Runnable code examples	✅ Every skill (CI-gated)	⚠️ 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 public skill (CI-gated)	❌	❌	❌
Free & open source (MIT)	✅	⚠️ Mixed	✅	✅

Claim basis (2026-10-12 audit): runnable-example coverage is CI-enforced — the quality gate blocks new stubs and the live report shows 0 stubs / 0 invalid. Failure-modes coverage is corpus-wide and CI-gated: every new or modified skill file must carry a ## Failure Modes section (validate-skills.yml), and the 2026-10-12 backfill added the section to the remaining legacy skill files, so all 538 public skill files carry it (the intentional 00-sandbox fixture is excluded); repository code search verified zero public skills lacking the section after the backfill. Blueprint and benchmark counts are verified against blueprints/ (7 reference blueprints) and benchmarks/ (head-to-head comparison documents).

🚀 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/          → 538 public skills (539 quality-corpus files including one sandbox fixture)
├── 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/            → Repository documentation and generated registry artifacts
├── i18n/            → Localized READMEs (10 languages)
├── meta/            → Schema, glossary, frameworks, roadmap, changelog
├── mcp/             → MCP server integration
└── tests/           → pytest test suite

🗂️ The 17 Skill Categories

#	Category	Skills	What It Covers
01	👁️ Perception	43	Text, images, PDFs, code, sensors, databases, screens
02	🧠 Reasoning	53	Planning, deduction, abduction, causal chains, commonsense
03	🗄️ Memory	27	Working, episodic, semantic, vector, injection, forgetting
04	⚡ Action Execution	28	File I/O, HTTP, email, shell, database writes
05	💻 Code	36	Write, run, debug, review, refactor, test, deploy
06	💬 Communication	21	Summarize, translate, draft, argue, adapt tone
07	🔧 Tool Use	40	APIs — GitHub, Slack, Stripe, OpenAI, MCP, A2A
08	🎭 Multimodal	21	Images, audio, video, VQA, 3D, charts
09	🤖 Agentic Patterns	31	ReAct, CoT, ToT, MCTS, LATS, RAG, Debate
10	🖥️ Computer Use	27	Click, type, scroll, OCR, terminal, VM, a11y tree
11	🌐 Web	29	Search, scrape, crawl, login, fill forms, parse RSS
12	📊 Data	30	ETL, SQL, embeddings, time series, anomaly detection
13	🎨 Creative	26	Copywriting, image prompts, SVG, music, scripts
14	🔒 Security	28	Sandboxing, secret scanning, audit logs, rollback
15	🎼 Orchestration	42	Multi-agent, state machines, retry, consensus
16	🏺 Domain-Specific	40	Medical, legal, finance, DevOps, education, science
17	🛠️ Infrastructure	16	Dependency auditing & supply-chain tooling

A Skill in 60 Seconds

Every skill file is self-contained and versioned:

# 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 battle-tested skill is CI-gated to include a real description, runnable Python code, and documentation tables; every new or modified skill must also document failure modes (CI ratchet), and the large majority document frameworks and 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	→

🤝 How to Contribute

Type	What It Is	PR Title Format
New Skill	A reusable procedural skill 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

🗺️ 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

Product Mission

Skills Tree is a public, trusted, machine-discoverable source of AI skills.

AI agents should be able to find the right skill here, and humans should be able to discover, understand, use, share, and contribute skills easily.

The project grows through genuine utility, reuse, sharing, contribution, and GitHub discovery—not fabricated popularity claims.

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

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

Metadata

Release files for skills-tree 1.80.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for skills-tree 1.80.2
File	Size	Uploaded
skills_tree-1.80.2.tar.gz	2.6 MB	Oct 12, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for skills-tree 1.80.2
File	Interpreter	ABI	Platform	Reset
skills_tree-1.80.2-py3-none-any.whl 3.0 MB Oct 12, 2026	Python 3	none	any	Details

Total release size: 5.6 MB

Release files / skills_tree-1.80.2.tar.gz

Download URL	skills_tree-1.80.2.tar.gz
Size	2.6 MB
Tags	Source
SHA-256 checksum How to use checksums	`526c7eaeab4173b3dc3aded3c13e889b69a3b583eaca7df56955d6670110eb1c`
BLAKE2b-256 checksum How to use checksums	`7ac9146d0c8dfbd4c04449c0ef0195805f1db2aaaffa4d2ec6c8050128e2038f`
Upload date	Oct 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/SamoTech/skills-tree
Publishing commit: github.com/SamoTech/skills-tree/tree/9c2645789446e3adc6651da11580fba81e44fefd

Workflow

Publishing configuration: github.com/SamoTech/skills-tree/blob/9c2645789446e3adc6651da11580fba81e44fefd/.github/workflows/zero-touch-release.yml
Publishing logs: github.com/SamoTech/skills-tree/actions/runs/38313649730/attempts/1

Artifact

Subject: skills_tree-1.80.2.tar.gz
SHA-256 checksum: 526c7eaeab4173b3dc3aded3c13e889b69a3b583eaca7df56955d6670110eb1c Verifying attestations

Release files / skills_tree-1.80.2-py3-none-any.whl

Download URL	skills_tree-1.80.2-py3-none-any.whl
Size	3.0 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`d40415c1e49d5512876d51a863d433f81bd36c0775ee521fba7d1a19cd386279`
BLAKE2b-256 checksum How to use checksums	`64058827757ce16e7031ca620eec0e1ec81f16e72c903e442b0a0f94b4eba733`
Upload date	Oct 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/SamoTech/skills-tree
Publishing commit: github.com/SamoTech/skills-tree/tree/9c2645789446e3adc6651da11580fba81e44fefd

Workflow

Publishing configuration: github.com/SamoTech/skills-tree/blob/9c2645789446e3adc6651da11580fba81e44fefd/.github/workflows/zero-touch-release.yml
Publishing logs: github.com/SamoTech/skills-tree/actions/runs/38313649730/attempts/1

Artifact

Subject: skills_tree-1.80.2-py3-none-any.whl
SHA-256 checksum: d40415c1e49d5512876d51a863d433f81bd36c0775ee521fba7d1a19cd386279 Verifying attestations