skills-tree 1.0.0

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

Skills Tree

📆 This Week's Highlights — June 15, 2026

🔥 Most Active Skills

• Readme — 1 PR
• Xquik Api — 1 PR

The AI Agent Skill OS — Build Smarter Agents, Faster

360 skills across 17 categories. Versioned, benchmarked, and openly evolving.
Stop rediscovering. Start building on what the community has already proven.

50 skills are battle-tested today. 308 are stubs waiting for a real example, real I/O, and real failure modes — see meta/QUALITY-REPORT.md for the full list. PRs that turn a stub into a production-ready entry are the highest-impact contribution you can make.

🌐 Browse Live UI · 🗺️ Systems · 🏗️ Blueprints · 📊 Benchmarks · 🔬 Labs · 🤝 Contribute · 🗺 Roadmap

🐦 Share Skills Tree on X / Twitter →

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

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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.

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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.

We don't pretend every entry is finished. Battle-tested skills (badged 🟢 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.

It's not a list. It's infrastructure being built in public.

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🚀 Start Here — Battle-Tested Skills

If you're new, read these first. Each one 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 (the foundation for everything else)

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

Working with text

• Translation — placeholder-safe MT with glossary + tone
• Paraphrasing — simplify / formalize / diversify
• OCR — VLM + classical OCR with confidence-based human-review routing

Code

• Code Generation — spec → AST-validated source with self-repair on failure
• Bug Fixing — agentic loop: read → patch → test → repeat until green
• Code Review — automated critique with severity tiers

Web

• Web Search — Tavily/Serper/Brave with recency + host allowlist + TTL cache
• Web Scraping — trafilatura + BS4 fallback, metadata, redirect-safe

Security

• Input Sanitization — 4-layer defense: structural + boundary + content + isolation

Action execution

• File Write — atomic, crash-safe file writes for agents
• HTTP Request — production HTTP with idempotency, retry-on-idempotent-only, header redaction
• Dependency Auditor — vulnerability + license + freshness audit

The full battle-tested set is auto-listed in meta/QUALITY-REPORT.md. The same report names every stub that needs upgrading — those are the highest-impact PRs you can submit.

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What's Inside

skills-tree/
│
├── skills/          → 360 atomic skill files (50 battle-tested, 308 stubs awaiting upgrade)
│                     run `python3 tools/check_skill_quality.py` for the live count
├── 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)
├── i18n/            → Localized READMEs (Arabic, Chinese, Spanish, German, French, Hindi, Japanese, Korean, Portuguese, Russian)
├── meta/            → Schema, glossary, frameworks, roadmap, changelog
└── requirements.txt → Pinned Python deps for CI workflows

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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	39	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	32	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 (early)

Counts above reflect skill files on disk and are auto-synced by tools/update_readme_counts.py (run nightly via update-skill-count.yml). If you spot a drift, open an issue.

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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}]
)
```

## Benchmarks  → benchmarks/memory/injection-strategies.md
## Related     → working-memory.md · rag.md · vector-store-retrieval.md
## Changelog   → v1 (2025-03) · v2 (2026-04, added retrieval scoring)

Every skill includes:

• ✅ What it does and why it matters
• ✅ Typed inputs/outputs
• ✅ Runnable Python code (claude-opus-4-5 / gpt-4o)
• ✅ Frameworks table (LangChain, LangGraph, CrewAI, mem0...)
• ✅ Failure modes and edge cases
• ✅ Related skills cross-links
• ✅ Version history

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Skill Versioning — How Evolution Works

Skills are not static files. They evolve as the community learns:

v1 — Initial entry: description + minimal example
v2 — Enriched: better example + failure modes + related skills
v3 — Battle-tested: benchmarks + model comparison + production notes

To upgrade a skill:

1. Bump the version in frontmatter
2. Add a changelog entry explaining what improved
3. Open a PR titled improve: skill-name — v1 → v2

The best versions surface naturally — through PR merge frequency and inclusion in Systems + Blueprints.

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🗺️ Systems — Multi-Skill Workflows

See how skills combine into real, working agent pipelines:

System	Skills Used	Use Case
Research Agent	Web search + RAG + Summarize + Cite	Deep research automation
Coding Agent	Code reading + Write + Debug + Test	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 + Type	Full GUI automation
Data Analyst	SQL + Charts + Summarize + Insight gen	Automated data analysis
Voice Agent	Audio transcription + NLU + TTS	Real-time voice interaction

---

🏗️ Blueprints — Production Architectures

Copy-paste architectures for the most common agent patterns:

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
Computer Use Browser	Browser automation via Playwright + vision
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

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📊 Benchmarks — Real Numbers, Reproducible

We test so you don't have to:

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 ratio	→
Function calling comparison	Claude 3.7	+6% on tool accuracy	→

Every benchmark includes methodology, dataset, and reproducible test scripts.

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🏆 This Week's Highlights

Auto-updated weekly · Full leaderboard →

🔥 Most Active Skills

• skills/09-agentic-patterns/react.md — 12 community improvements this month
• skills/03-memory/memory-injection.md — v2 with retrieval scoring
• skills/02-reasoning/causal.md — new benchmark comparison added

⚡ Battle-Tested (used in 10+ public projects) ReAct · Chain of Thought · RAG Pipeline · Memory Injection · Tool Use

🔬 Hot in Labs

• labs/reasoning/tree-of-agents.md — multi-agent tree search
• labs/memory/episodic-compression.md — lossy-but-useful memory compression
• labs/tool-use/adaptive-tool-selection.md — dynamic tool filtering for large registries

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🤝 How to Contribute

Four types of contributions — all valued:

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

Quality Rules

• ❌ No generic prompts or vague descriptions
• ❌ No skills without a working code example
• ✅ Must solve a real, specific problem
• ✅ Must be structured and reusable
• ✅ Must include inputs, outputs, and at least one runnable example

Full guide: CONTRIBUTING.md

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Quick Start

# Clone
git clone https://github.com/SamoTech/skills-tree.git

# Find a skill by keyword
grep -r "memory injection" skills/ --include="*.md" -l

# Read a full system end-to-end
cat systems/research-agent.md

# See benchmark results
cat benchmarks/tool-use/function-calling-comparison.md

Or browse the live UI →

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Who This Is For

🏗️  Agent Builders       → Production skill patterns, ready to use today
🔬  AI Researchers        → Benchmarks, taxonomy, and full capability coverage
📐  System Architects     → Blueprints for multi-agent production systems
🎓  Learners              → Structured path from basic skills → advanced systems
🤝  Contributors          → A community that improves everything together

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🗺️ Roadmap

See the full plan: meta/ROADMAP.md

Near-term (v2.x):

• Skill dependency graph — visual map of how skills relate
• Skill Paths — curated learning tracks (e.g., "Build a Research Agent in 5 skills")
• JSON/YAML export of all skill metadata for programmatic use
• Community skill ratings and upvotes
• Auto-leaderboard: Top Skills This Week, Most Improved, Battle-Tested

Medium-term (v3.0):

• CLI: skills-tree search "memory injection" → returns ranked results
• LangChain Hub / MCP registry integration
• ✅ Localization: Arabic, Chinese, Spanish READMEs — shipped in v2.1
• Automated changelog generation on PR merge

Long-term vision:

• Skills Tree becomes the canonical reference for AI agent capabilities
• Every major agent framework links here as the skill index
• 1000+ skills, all battle-tested, all benchmarked

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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. Every benchmark run here prevents someone else from wasting a week. Every system documented here becomes a launchpad for the next builder.

This is not a repo. It's infrastructure for the AI-native era.

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