Stop wasting tokens on trial-and-error.

Give your AI agent battle-tested, ready-to-use skills that work the first time — cut token usage by 95–98%, lower model costs, and make smaller models reliable.

MAIN INSTALLATION: USE THE WEBSITE QUICK START

Battle-tested, copy-paste execution playbooks for AI agents.

Two ways to win:

🏠 Go 100% free — Ollama + Llama/Mistral/Qwen + Open Skills = cloud-level practical task execution at $0

💰 Keep cloud quality, slash cloud cost — GPT-4/Claude/Gemini + Open Skills = ~$0.003–$0.005/task instead of ~$0.15–$0.25

• Browse Skills
• Contributing Guide
• Skill Template

The Problem: AI agents are expensive and cloud-dependent:

• Cloud models (GPT-4, Claude, Gemini): Often spend 10–30+ calls discovering and debugging each task → ~$0.15–$0.25 per simple task
• Local models (Llama, Mistral, Qwen): Often know the goal but fail at API/tool details without guidance
• Both burn through tokens on trial-and-error, searching documentation, and debugging

The Solution: Pre-written, tested skills that work with ANY AI model:

• ✅ Working code examples (Node.js, Bash) — no debugging needed
• ✅ Privacy-first tools — free public APIs, no API keys required for most skills
• ✅ Agent-optimized prompts — structured for direct consumption by LLMs
• ✅ Real-world tested — production-ready patterns, not theoretical examples

The New Approach: Separate reasoning from execution knowledge.

• Model handles intent and orchestration
• Open Skills provides tested implementation steps (commands, API patterns, parsing logic)
• Outcome: faster execution, lower token usage, and higher reliability across both cloud and local models

The Game-Changer: 🚀 Make local models as capable as cloud models

Instead of paying models to figure everything out from scratch, give them proven execution playbooks:

• Llama 3.1 / Mistral / Qwen (free, local) + Open Skills → performs like GPT-4/Claude for practical tasks
• Result: $0 cost, 100% self-hostable, complete privacy

The Impact:

• 💰 95–98% cloud cost reduction — Cloud models drop from ~$0.15–$0.25 to ~$0.003–$0.005 per task with skills
• 🏠 $0 local operation — Local models + skills run practical tasks without cloud spend
• 🏠 100% self-hostable — Run Ollama + Open Skills entirely offline
• 🔒 Complete privacy — No data leaves your machine
• ⚡ 10-50x faster execution — No trial-and-error loops
• 🎯 Higher success rate — Proven patterns that work reliably
• 🤖 Automated contributions — Agents can auto-fork, commit, and PR new skills via GitHub CLI
• 🧠 Self-improving ecosystem — Community skills flow back into the repository automatically
• 🏆 Public credit — Contributors get GitHub commit history and recognition
• 🔍 Zero search API costs — Use free SearXNG instances instead of paying for Brave Search ($5/1000), Google Search API, or Bing API

Without open-skills (Cloud models like GPT-4/Claude):

User: "Check the balance of this Bitcoin address: 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa"

Cloud AI Agent → Searches for "bitcoin balance API"
                → Tries blockchain.com (wrong endpoint)
                → Tries blockchain.info (wrong format)
                → Debugs response parsing
                → Realizes satoshis need conversion
                → Finally works after 15-20 API calls

Result: ❌ 2-3 minutes, 50,000+ tokens, $0.15-$0.25 cost

Without open-skills (Local models like Llama/Mistral):

User: "Check the balance of this Bitcoin address: 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa"

Local AI (Llama/Mistral) → Tries to search for API documentation
                         → Gets confused about endpoints
                         → Generates incorrect curl command
                         → Unable to parse response correctly
                         → Gives up or returns error

Result: ❌ Task fails, user frustrated

With open-skills (ANY MODEL - GPT-4, Claude, Llama, Mistral, Gemini):

User: "Check the balance of this Bitcoin address: 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa"

Any AI Agent → Finds check-crypto-address-balance.md
             → Uses working example: curl blockchain.info/q/addressbalance/[address]
             → Converts satoshis to BTC (÷ 1e8)
             → Returns result

Result: ✅ 10 seconds, ~1,000 tokens, works first time
        ✅ Cloud models: $0.003-$0.005 (was $0.15-$0.25) — 95%+ savings
        ✅ Local models: $0.00 (free) — task actually succeeds

Key insight: Open Skills doesn't just make expensive models cheaper — it helps low-powered and free models run tasks reliably with less hallucination.

Example 2: Web Search (API Cost Elimination)

Without open-skills:

User: "Search for recent AI agent news"

Agent → Uses Google Custom Search API ($5/1000 queries)
      → Or Brave Search API ($5/1000 queries)
      → Bing Search API ($3-7/1000 queries)
      → Monthly cost: $50-100+ for 10k searches

Result: ❌ Expensive, requires API keys, tracked searches

With open-skills:

User: "Search for recent AI agent news"

Agent → Uses SearXNG skill (learns from [skills/web-search-api/SKILL.md](skills/web-search-api/SKILL.md))
      → Connects to free SearXNG instance (searx.be)
      → Gets results from 70+ search engines
      → No API key, no rate limits, no tracking

Result: ✅ $0 cost, unlimited queries, privacy-respecting

Savings: $360-$840/year for typical usage, $3,000-$8,000/year for high-volume agents

Example 3: Trading Indicators (Quant Analysis in Seconds)

Without open-skills:

User: "Calculate RSI, MACD, and top indicators from this OHLCV dataset"

Agent → Searches for indicator formulas one by one
      → Implements RSI, then debugs MACD math
      → Repeats for Bollinger, Stochastic, ATR, ADX, etc.
      → Fixes column mapping/warmup NaN issues
      → Ends up with inconsistent outputs after many iterations

Result: ❌ Slow, error-prone, heavy token/API usage

With open-skills:

User: "Calculate RSI, MACD, and top indicators from this OHLCV dataset"

Agent → Finds trading-indicators-from-price-data.md
      → Runs the ready Python workflow with pandas + pandas-ta
      → Computes 20 indicators (RSI, MACD, SMA/EMA, BB, Stoch, ATR, ADX, CCI, OBV, MFI, ROC)
      → Returns clean, structured output immediately

Result: ✅ Fast, consistent, production-ready calculations

Savings: Massive reduction in trial-and-error, faster indicator pipelines, and more reliable strategy signals

Example 4: Hosted Report Website (Tailwind + Originless)

Without open-skills:

User: "Create a beautiful white-themed report website from this content and host it instantly"

Agent → Experiments with random HTML/CSS templates
      → Tries multiple hosting providers and auth flows
      → Debugs upload endpoints and response formats
      → Rewrites password logic several times
      → Finally ships a fragile page after many retries

Result: ❌ Slow delivery, inconsistent styling, avoidable token/API waste

With open-skills:

User: "Create a beautiful white-themed report website from this content and host it instantly"

Agent → Finds generate-report-originless-site.md
      → Generates index.html with Tailwind CDN + subtle animations
      → Applies clean white-background report layout
      → Uploads to Originless (local/public endpoint)
      → Returns hosted URL/CID immediately
      → If requested, adds client-side password unlock for encrypted content

Result: ✅ Fast static site generation, instant decentralized hosting, predictable output

Savings: Fewer retries, faster publish time, and consistent website quality with account-free hosting

Typical AI agent task without pre-built skills: 20-50 API calls (trial and error)
Same task with open-skills: 1-3 API calls (direct execution)

Over 100 tasks/month:

• GPT-4: Save ~$24.50/month
• Claude: Save ~$14.70/month
• For teams running 1,000+ agent tasks: Save $240-$1,470/month

The Real Game-Changer: Open Skills makes local models competitive with GPT-4 for practical tasks.

The 100% Free, Self-Hostable AI Agent Stack:

# Install Ollama (free, local)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1:8b

# Clone Open Skills (free, open-source)
git clone https://github.com/besoeasy/open-skills ~/open-skills

# Result: GPT-4-level task execution at $0 cost
# - No API keys needed
# - No cloud dependency
# - Complete privacy
# - 100% self-hostable

Monthly cost comparison:

• Cloud models (GPT-4/Claude) without skills: $150-$1,470/month (1,000 tasks)
• Cloud models with skills: $3-$15/month (95%+ savings)
• Local models (Llama/Mistral) + Open Skills: $0/month (100% free, actually works)

Plus: Eliminate search API costs entirely by using free SearXNG instances instead of:

• Google Custom Search API ($5/1000 queries) → $0 with SearXNG
• Brave Search API ($5/1000 queries) → $0 with SearXNG
• Bing Search API ($3-7/1000 queries) → $0 with SearXNG

Total potential savings: $600-$2,300/month for active AI agents
Or go 100% free with local models + Open Skills: $0/month forever

• 🏠 Self-hosted AI enthusiasts — Run Llama/Mistral with Ollama + Open Skills for GPT-4-level capabilities at $0 cost
• 🤖 Autonomous AI agents — Give your agent production-ready capabilities out of the box
• 💼 Business automation — Crypto monitoring, document processing, web scraping, notifications
• 🔍 Eliminating API costs — Replace expensive search, translation, geocoding, and weather APIs with free alternatives
• 🛠️ Developer tools — Integrate with OpenCode.ai, Claude Desktop, Ollama, custom MCP servers
• 📚 AI learning — Study working examples instead of guessing API patterns
• 🔐 Privacy-conscious projects — All skills use open-source tools and public APIs, run entirely offline
• 💰 Cost-sensitive teams — Reduce AI agent costs by 98% or go completely free with local models

Why we built this:

AI agents are incredibly powerful, but there's a massive gap:

• Expensive cloud models (GPT-4, Claude, Gemini): Smart enough to figure things out, but cost $0.15-$0.25+ per task
• Free local models (Llama, Mistral, Qwen): Can't figure things out reliably, so they fail or give up

Open Skills bridges this gap by providing the "figuring out" part:

• Instead of making models search, experiment, and debug → Give them working code
• Instead of requiring high intelligence → Provide pre-tested patterns
• Result: Cheap models execute like expensive models

Our approach:

• ✅ Tested code, not theory — Every example is production-ready
• ✅ Privacy-first — Open-source tools, minimal tracking, no vendor lock-in
• ✅ Agent-optimized — Written for LLM consumption (clear structure, copy-paste ready)
• ✅ Free to use — MIT licensed, no API keys required for core functionality
• ✅ Model-agnostic — Works with GPT-4, Claude, Gemini, Llama, Mistral, Qwen, any LLM

The result: AI agents that are smarter, faster, and cheaper to run — or completely free with local models.