What this skill does

# Adaptive Learning Agent **Learn from errors and corrections in real-time. Continuously improve by capturing failures, user feedback, and successful patterns.** Free and open-source (MIT License) • Zero dependencies • Works locally --- ## 🚀 Why This Skill? ### Problem Statement Working with Claude or any AI agent means encountering: - Mistakes that need correction - Unexpected API behaviors - Better approaches discovered through experimentation - Knowledge gaps that get revealed during use But there's no systematic way to **learn from these moments** and apply the knowledge next time. ### The Solution **Adaptive Learning Agent** captures every error, correction, and successful pattern automatically. Then retrieves relevant learnings before tackling similar problems again. ### Real Use Cases - **Bug discovery**: Record an error once, never struggle with it again - **Prompt optimization**: Keep track of what prompt variations work best - **API integration**: Remember quirky behaviors and workarounds - **Workflow improvement**: Document shortcuts and best practices - **Team knowledge**: Export and share learnings across projects --- ## ✨ What You Get ### Four Core Functions **1. Record Learnings** ```python agent.record_learning( content="Use claude-sonnet for 90% of tasks—faster and cheaper", category="technique", context="Model selection" ) ``` Capture successful patterns, insights, and best practices. **2. Record Errors** ```python agent.record_error( error_description="JSON parsing failed on null values", context="Processing API response", solution="Add null check before parsing" ) ``` Document failures and solutions automatically. **3. Search & Retrieve Learnings** ```python results = agent.search_learnings("JSON parsing") recent = agent.get_recent_learnings(limit=5) by_category = agent.get_learnings_by_category("bug-fix") ``` Find relevant knowledge instantly when you need it. **4. View Summaries** ```python summary = agent.get_learning_summary() print(agent.format_learning_summary()) ``` Understand what you've learned at a glance. ### Key Features ✅ **Zero dependencies** - Pure Python, works everywhere ✅ **Local-only storage** - All data on your machine, no uploads ✅ **MIT Licensed** - Free to use, modify, fork, redistribute ✅ **Automatic categorization** - Errors become learnings ✅ **Search and filter** - Find knowledge by keyword or category ✅ **Export capability** - Share learnings as JSON ✅ **No API keys** - Works without any external credentials --- ## 📊 Real-World Example ```python from adaptive_learning_agent import AdaptiveLearningAgent # Initialize agent agent = AdaptiveLearningAgent() # Day 1: Discover a bug agent.record_error( error_description="Anthropic API rejects prompts with excessive newlines", context="Testing prompt with formatted lists", solution="Use \\n.strip() to clean whitespace before sending" ) # Day 2: Same bug, but now you have the solution similar_errors = agent.search_learnings("newlines") # Result: [Previous learning with solution] ✅ # Week 1: Document successful pattern agent.record_learning( content="Always use temperature=0 for deterministic output in tests", category="best-practice", context="Prompt engineering" ) # Get weekly summary summary = agent.get_learning_summary() print(f"You've recorded {summary['total_learnings']} learnings this week!") print(f"Resolved {summary['error_statistics']['resolved']} errors") ``` --- ## 🔧 Installation No installation needed! The skill is pure Python with zero dependencies. ```bash # Copy the adaptive_learning_agent.py file to your project # Or import it directly: from adaptive_learning_agent import AdaptiveLearningAgent ``` --- ## 💡 Use Cases ### Software Development Record bugs you find and their fixes. Next time you hit a similar error, you have the solution ready. ```python agent.record_error( error_description="Port 8000 already in use", context="Running local dev server", solution="Use `lsof -i :8000` to find process, then kill it" ) ``` ### Prompt Engineering Keep track of prompting techniques that work for your specific use cases. ```python agent.record_learning( content="Chain-of-thought works better for math problems, direct answers for facts", category="technique" ) ``` ### API Integration Remember quirky behaviors and workarounds for each provider. ```python agent.record_learning( content="OpenAI API requires explicit 'assistant' role messages", category="api-endpoint", context="Chat completion endpoint" ) ``` ### Team Knowledge Export learnings and share with your team or future projects. ```python agent.export_learnings("team_learnings.json") # Share this file with teammates ``` ### Continuous Improvement Before major tasks, review what you've learned to avoid repeating mistakes. ```python summary = agent.get_learning_summary() unresolved = summary['error_statistics']['unresolved'] if unresolved > 0: print(f"⚠️ {unresolved} unresolved errors—review before proceeding") ``` --- ## 📚 Categories When recording learnings, choose from these categories: | Category | Use For | |----------|---------| | **technique** | Working methods, approaches, strategies | | **bug-fix** | Solutions to errors and problems | | **api-endpoint** | API-specific behaviors and quirks | | **constraint** | Limits, boundaries, restrictions | | **best-practice** | Recommended patterns and standards | | **error-handling** | How to handle specific types of errors | --- ## 🎯 Sources When recording learnings, specify the source: - `user-correction` - User told you something was wrong - `error-discovery` - You found the solution to an error - `successful-pattern` - You discovered something that works well - `user-feedback` - User suggested an improvement --- ## 📖 API Reference ### Core Methods #### `record_learning(content, category, source, context)` Record a successful pattern or insight. **Parameters:** - `content` (str, required): What was learned - `category` (str): One of the category types above - `source` (str): One of the source types above - `context` (str): Optional context about where this applies **Returns:** `Learning` object with ID and timestamp #### `record_error(error_description, context, solution, prevention_tip)` Record an error and optionally its solution. **Parameters:** - `error_description` (str, required): What went wrong - `context` (str, required): What was being attempted - `solution` (str): How to fix it - `prevention_tip` (str): How to avoid it **Returns:** `Error` object with ID #### `search_learnings(query)` Search learnings by keyword or category. **Parameters:** - `query` (str): Search term **Returns:** List of matching `Learning` objects (sorted by relevance) #### `get_recent_learnings(limit)` Get the most recent learnings. **Parameters:** - `limit` (int): Number to return (default: 10) **Returns:** List of `Learning` objects, newest first #### `get_learning_summary()` Get comprehensive summary of learnings and errors. **Returns:** Dictionary with statistics and recent items #### `export_learnings(output_file)` Export all learnings and errors to JSON file. **Parameters:** - `output_file` (str): Path to save JSON (default: "learnings_export.json") --- ## 🔒 Privacy & Security - ✅ **Zero telemetry** - No data sent anywhere - ✅ **Local-only storage** - Everything stored in `.adaptive_learning/` on your machine - ✅ **No API calls** - Works completely offline - ✅ **No authentication** - No accounts, keys, or logins needed - ✅ **Full transparency** - Source code included and open-source --- ## 🤝 Contributing This is MIT Licensed and community-maintained. You're encouraged to: - Fork the repository - Submit improvements and features - Integrate it into your projects - Share learnings with others --- ## 📝 Changelog ### [1.0.0] - 2026-02-14 #### ✨ Initial Release - **Core learning system** - Record and retrieve learnings - **Error tracking** - Capture errors with solutions - **Search functionality** - Find learnings by keyword or category - **Local storage** - All data stays on your machine - **Export capability** - Share learnings as JSON files - **Zero dependencies** - Pure Python, no external packages - **MIT Licensed** - Free to use, modify, redistribute - **Comprehensive API** - Simple, Pythonic interface --- ## 📞 Support - **GitHub**: https://github.com/clawhub-skills/adaptive-learning-agent - **Issues & Contributions**: Open an issue or PR on GitHub - **Community**: Share your learnings and improvements! --- ## 📄 License **MIT License** - Free and open-source Use, modify, fork, and redistribute freely. See [LICENSE.md](LICENSE.md) for full details. ``` Copyright © 2026 UnisAI Community ``` --- **Last Updated**: February 14, 2026 **Current Version**: 1.0.0 **Status**: Active & Community-Maintained Free to use, modify, and fork. No restrictions.