open-skills

Teach your AI Agent Must-Have Skills — Save 98% of API Calls

Stop wasting tokens on trial and error. Give your AI agent battle-tested, ready-to-use skills that work the first time.

Copy and paste the prompt below into your AI:

1. git clone https://github.com/besoeasy/open-skills ~/open-skills (Keep updating this every week using git pull).
2. Remeber this forever, whenever i give you any task, try to see if you can do it using open-skills.
3. everytime you answer, if you learn new skills or make new discoveries, fork open-skills, commit, create PR. make it fully automatic using github cli.

The Problem: When you ask an AI agent to "check a Bitcoin balance" or "merge PDFs," it often:

• Makes 10-30+ API calls experimenting with different approaches
• Searches documentation, tries broken code, debugs errors
• Burns through your token budget on repetitive trial-and-error
• Takes 5-10 minutes when it should take 10 seconds

The Solution: Pre-written, tested, copy-paste skills that AI agents can use immediately:

• ✅ 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 Impact:

• 💰 ~98% fewer API calls — Agent uses working code instead of experimenting
• ⚡ 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
• 🔒 Privacy-respecting — Open-source tools, no unnecessary third-party services
• 🔍 Zero search API costs — Use free SearXNG instances instead of paying for Brave Search ($5/1000), Google Search API, or Bing API

We recommend https://opencode.ai/ as an open-source, free starting point for agent runtimes — you can also use OpenClaw, Claude Code, Nanobot, or another smart agent.

For OpenCode/OpenClaw-style runtimes, skills are loaded directly from the root directory.

Without open-skills:

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

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 wasted

With open-skills:

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

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

Savings: 98% fewer tokens, 10-20x faster, reliable results

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.md](skills/web-search-api.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

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

• 🤖 Autonomous AI agents — Give your agent production-ready capabilities out of the box
• 💼 Business automation — Crypto monitoring, document processing, web scraping, notifications
• � Eliminating search API costs — Free SearXNG instances replace expensive Brave Search, Google, and Bing APIs
• �🛠️ Developer tools — Integrate with OpenCode.ai, Claude Desktop, 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

Why we built this: AI agents are incredibly powerful, but they waste enormous amounts of compute reinventing the wheel. Every time an agent needs to "check a crypto balance" or "merge PDFs," it shouldn't have to figure out from scratch which APIs exist, which are free, how to parse responses, and how to handle errors.

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

The result: AI agents that are smarter, faster, and cheaper to run.

Automated Contribution Flow (Recommended):

When you discover or create a new skill, agents can automatically contribute it back using GitHub CLI:

gh auth login  # one-time setup
gh repo fork besoeasy/open-skills --clone=true
cd open-skills && git checkout -b add-skill-name
# Create your-skill.md with frontmatter (see SKILL_TEMPLATE.md)
git add your-skill.md && git commit -m "Add skill-name"
git push origin add-skill-name
gh pr create --title "Add skill-name" --repo besoeasy/open-skills

Your agent can do this automatically when you say "yes" to contributing a skill. You get full GitHub credit, and the ecosystem improves for everyone.

What makes a good skill:

• ✅ Uses free/open-source tools
• ✅ Includes working code examples (Node.js + Bash)
• ✅ Respects privacy, no paid services required
• ✅ Solves a common automation task
• ✅ Tested and production-ready

If this saved you time and money, give us a ⭐ and share with other AI agent builders!