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    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
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    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Adaptive Learning Agent",
        "body": "Learn from errors and corrections in real-time. Continuously improve by capturing failures, user feedback, and successful patterns.\n\nFree and open-source (MIT License) • Zero dependencies • Works locally"
      },
      {
        "title": "Problem Statement",
        "body": "Working with Claude or any AI agent means encountering:\n\nMistakes that need correction\nUnexpected API behaviors\nBetter approaches discovered through experimentation\nKnowledge gaps that get revealed during use\n\nBut there's no systematic way to learn from these moments and apply the knowledge next time."
      },
      {
        "title": "The Solution",
        "body": "Adaptive Learning Agent captures every error, correction, and successful pattern automatically. Then retrieves relevant learnings before tackling similar problems again."
      },
      {
        "title": "Real Use Cases",
        "body": "Bug discovery: Record an error once, never struggle with it again\nPrompt optimization: Keep track of what prompt variations work best\nAPI integration: Remember quirky behaviors and workarounds\nWorkflow improvement: Document shortcuts and best practices\nTeam knowledge: Export and share learnings across projects"
      },
      {
        "title": "Four Core Functions",
        "body": "1. Record Learnings\n\nagent.record_learning(\n    content=\"Use claude-sonnet for 90% of tasks—faster and cheaper\",\n    category=\"technique\",\n    context=\"Model selection\"\n)\n\nCapture successful patterns, insights, and best practices.\n\n2. Record Errors\n\nagent.record_error(\n    error_description=\"JSON parsing failed on null values\",\n    context=\"Processing API response\",\n    solution=\"Add null check before parsing\"\n)\n\nDocument failures and solutions automatically.\n\n3. Search & Retrieve Learnings\n\nresults = agent.search_learnings(\"JSON parsing\")\nrecent = agent.get_recent_learnings(limit=5)\nby_category = agent.get_learnings_by_category(\"bug-fix\")\n\nFind relevant knowledge instantly when you need it.\n\n4. View Summaries\n\nsummary = agent.get_learning_summary()\nprint(agent.format_learning_summary())\n\nUnderstand what you've learned at a glance."
      },
      {
        "title": "Key Features",
        "body": "✅ Zero dependencies - Pure Python, works everywhere\n✅ Local-only storage - All data on your machine, no uploads\n✅ MIT Licensed - Free to use, modify, fork, redistribute\n✅ Automatic categorization - Errors become learnings\n✅ Search and filter - Find knowledge by keyword or category\n✅ Export capability - Share learnings as JSON\n✅ No API keys - Works without any external credentials"
      },
      {
        "title": "📊 Real-World Example",
        "body": "from adaptive_learning_agent import AdaptiveLearningAgent\n\n# Initialize agent\nagent = AdaptiveLearningAgent()\n\n# Day 1: Discover a bug\nagent.record_error(\n    error_description=\"Anthropic API rejects prompts with excessive newlines\",\n    context=\"Testing prompt with formatted lists\",\n    solution=\"Use \\\\n.strip() to clean whitespace before sending\"\n)\n\n# Day 2: Same bug, but now you have the solution\nsimilar_errors = agent.search_learnings(\"newlines\")\n# Result: [Previous learning with solution] ✅\n\n# Week 1: Document successful pattern\nagent.record_learning(\n    content=\"Always use temperature=0 for deterministic output in tests\",\n    category=\"best-practice\",\n    context=\"Prompt engineering\"\n)\n\n# Get weekly summary\nsummary = agent.get_learning_summary()\nprint(f\"You've recorded {summary['total_learnings']} learnings this week!\")\nprint(f\"Resolved {summary['error_statistics']['resolved']} errors\")"
      },
      {
        "title": "🔧 Installation",
        "body": "No installation needed! The skill is pure Python with zero dependencies.\n\n# Copy the adaptive_learning_agent.py file to your project\n# Or import it directly:\n\nfrom adaptive_learning_agent import AdaptiveLearningAgent"
      },
      {
        "title": "Software Development",
        "body": "Record bugs you find and their fixes. Next time you hit a similar error, you have the solution ready.\n\nagent.record_error(\n    error_description=\"Port 8000 already in use\",\n    context=\"Running local dev server\",\n    solution=\"Use `lsof -i :8000` to find process, then kill it\"\n)"
      },
      {
        "title": "Prompt Engineering",
        "body": "Keep track of prompting techniques that work for your specific use cases.\n\nagent.record_learning(\n    content=\"Chain-of-thought works better for math problems, direct answers for facts\",\n    category=\"technique\"\n)"
      },
      {
        "title": "API Integration",
        "body": "Remember quirky behaviors and workarounds for each provider.\n\nagent.record_learning(\n    content=\"OpenAI API requires explicit 'assistant' role messages\",\n    category=\"api-endpoint\",\n    context=\"Chat completion endpoint\"\n)"
      },
      {
        "title": "Team Knowledge",
        "body": "Export learnings and share with your team or future projects.\n\nagent.export_learnings(\"team_learnings.json\")\n# Share this file with teammates"
      },
      {
        "title": "Continuous Improvement",
        "body": "Before major tasks, review what you've learned to avoid repeating mistakes.\n\nsummary = agent.get_learning_summary()\nunresolved = summary['error_statistics']['unresolved']\nif unresolved > 0:\n    print(f\"⚠️ {unresolved} unresolved errors—review before proceeding\")"
      },
      {
        "title": "📚 Categories",
        "body": "When recording learnings, choose from these categories:\n\nCategoryUse FortechniqueWorking methods, approaches, strategiesbug-fixSolutions to errors and problemsapi-endpointAPI-specific behaviors and quirksconstraintLimits, boundaries, restrictionsbest-practiceRecommended patterns and standardserror-handlingHow to handle specific types of errors"
      },
      {
        "title": "🎯 Sources",
        "body": "When recording learnings, specify the source:\n\nuser-correction - User told you something was wrong\nerror-discovery - You found the solution to an error\nsuccessful-pattern - You discovered something that works well\nuser-feedback - User suggested an improvement"
      },
      {
        "title": "Core Methods",
        "body": "record_learning(content, category, source, context)\n\nRecord a successful pattern or insight.\n\nParameters:\n\ncontent (str, required): What was learned\ncategory (str): One of the category types above\nsource (str): One of the source types above\ncontext (str): Optional context about where this applies\n\nReturns: Learning object with ID and timestamp\n\nrecord_error(error_description, context, solution, prevention_tip)\n\nRecord an error and optionally its solution.\n\nParameters:\n\nerror_description (str, required): What went wrong\ncontext (str, required): What was being attempted\nsolution (str): How to fix it\nprevention_tip (str): How to avoid it\n\nReturns: Error object with ID\n\nsearch_learnings(query)\n\nSearch learnings by keyword or category.\n\nParameters:\n\nquery (str): Search term\n\nReturns: List of matching Learning objects (sorted by relevance)\n\nget_recent_learnings(limit)\n\nGet the most recent learnings.\n\nParameters:\n\nlimit (int): Number to return (default: 10)\n\nReturns: List of Learning objects, newest first\n\nget_learning_summary()\n\nGet comprehensive summary of learnings and errors.\n\nReturns: Dictionary with statistics and recent items\n\nexport_learnings(output_file)\n\nExport all learnings and errors to JSON file.\n\nParameters:\n\noutput_file (str): Path to save JSON (default: \"learnings_export.json\")"
      },
      {
        "title": "🔒 Privacy & Security",
        "body": "✅ Zero telemetry - No data sent anywhere\n✅ Local-only storage - Everything stored in .adaptive_learning/ on your machine\n✅ No API calls - Works completely offline\n✅ No authentication - No accounts, keys, or logins needed\n✅ Full transparency - Source code included and open-source"
      },
      {
        "title": "🤝 Contributing",
        "body": "This is MIT Licensed and community-maintained. You're encouraged to:\n\nFork the repository\nSubmit improvements and features\nIntegrate it into your projects\nShare learnings with others"
      },
      {
        "title": "[1.0.0] - 2026-02-14",
        "body": "✨ Initial Release\n\nCore learning system - Record and retrieve learnings\nError tracking - Capture errors with solutions\nSearch functionality - Find learnings by keyword or category\nLocal storage - All data stays on your machine\nExport capability - Share learnings as JSON files\nZero dependencies - Pure Python, no external packages\nMIT Licensed - Free to use, modify, redistribute\nComprehensive API - Simple, Pythonic interface"
      },
      {
        "title": "📞 Support",
        "body": "GitHub: https://github.com/clawhub-skills/adaptive-learning-agent\nIssues & Contributions: Open an issue or PR on GitHub\nCommunity: Share your learnings and improvements!"
      },
      {
        "title": "📄 License",
        "body": "MIT License - Free and open-source\n\nUse, modify, fork, and redistribute freely. See LICENSE.md for full details.\n\nCopyright © 2026 UnisAI Community\n\nLast Updated: February 14, 2026\nCurrent Version: 1.0.0\nStatus: Active & Community-Maintained\n\nFree to use, modify, and fork. No restrictions."
      }
    ],
    "body": "Adaptive Learning Agent\n\nLearn from errors and corrections in real-time. Continuously improve by capturing failures, user feedback, and successful patterns.\n\nFree and open-source (MIT License) • Zero dependencies • Works locally\n\n🚀 Why This Skill?\nProblem Statement\n\nWorking with Claude or any AI agent means encountering:\n\nMistakes that need correction\nUnexpected API behaviors\nBetter approaches discovered through experimentation\nKnowledge gaps that get revealed during use\n\nBut there's no systematic way to learn from these moments and apply the knowledge next time.\n\nThe Solution\n\nAdaptive Learning Agent captures every error, correction, and successful pattern automatically. Then retrieves relevant learnings before tackling similar problems again.\n\nReal Use Cases\nBug discovery: Record an error once, never struggle with it again\nPrompt optimization: Keep track of what prompt variations work best\nAPI integration: Remember quirky behaviors and workarounds\nWorkflow improvement: Document shortcuts and best practices\nTeam knowledge: Export and share learnings across projects\n✨ What You Get\nFour Core Functions\n\n1. Record Learnings\n\nagent.record_learning(\n    content=\"Use claude-sonnet for 90% of tasks—faster and cheaper\",\n    category=\"technique\",\n    context=\"Model selection\"\n)\n\n\nCapture successful patterns, insights, and best practices.\n\n2. Record Errors\n\nagent.record_error(\n    error_description=\"JSON parsing failed on null values\",\n    context=\"Processing API response\",\n    solution=\"Add null check before parsing\"\n)\n\n\nDocument failures and solutions automatically.\n\n3. Search & Retrieve Learnings\n\nresults = agent.search_learnings(\"JSON parsing\")\nrecent = agent.get_recent_learnings(limit=5)\nby_category = agent.get_learnings_by_category(\"bug-fix\")\n\n\nFind relevant knowledge instantly when you need it.\n\n4. View Summaries\n\nsummary = agent.get_learning_summary()\nprint(agent.format_learning_summary())\n\n\nUnderstand what you've learned at a glance.\n\nKey Features\n\n✅ 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\n\n📊 Real-World Example\nfrom adaptive_learning_agent import AdaptiveLearningAgent\n\n# Initialize agent\nagent = AdaptiveLearningAgent()\n\n# Day 1: Discover a bug\nagent.record_error(\n    error_description=\"Anthropic API rejects prompts with excessive newlines\",\n    context=\"Testing prompt with formatted lists\",\n    solution=\"Use \\\\n.strip() to clean whitespace before sending\"\n)\n\n# Day 2: Same bug, but now you have the solution\nsimilar_errors = agent.search_learnings(\"newlines\")\n# Result: [Previous learning with solution] ✅\n\n# Week 1: Document successful pattern\nagent.record_learning(\n    content=\"Always use temperature=0 for deterministic output in tests\",\n    category=\"best-practice\",\n    context=\"Prompt engineering\"\n)\n\n# Get weekly summary\nsummary = agent.get_learning_summary()\nprint(f\"You've recorded {summary['total_learnings']} learnings this week!\")\nprint(f\"Resolved {summary['error_statistics']['resolved']} errors\")\n\n🔧 Installation\n\nNo installation needed! The skill is pure Python with zero dependencies.\n\n# Copy the adaptive_learning_agent.py file to your project\n# Or import it directly:\n\nfrom adaptive_learning_agent import AdaptiveLearningAgent\n\n💡 Use Cases\nSoftware Development\n\nRecord bugs you find and their fixes. Next time you hit a similar error, you have the solution ready.\n\nagent.record_error(\n    error_description=\"Port 8000 already in use\",\n    context=\"Running local dev server\",\n    solution=\"Use `lsof -i :8000` to find process, then kill it\"\n)\n\nPrompt Engineering\n\nKeep track of prompting techniques that work for your specific use cases.\n\nagent.record_learning(\n    content=\"Chain-of-thought works better for math problems, direct answers for facts\",\n    category=\"technique\"\n)\n\nAPI Integration\n\nRemember quirky behaviors and workarounds for each provider.\n\nagent.record_learning(\n    content=\"OpenAI API requires explicit 'assistant' role messages\",\n    category=\"api-endpoint\",\n    context=\"Chat completion endpoint\"\n)\n\nTeam Knowledge\n\nExport learnings and share with your team or future projects.\n\nagent.export_learnings(\"team_learnings.json\")\n# Share this file with teammates\n\nContinuous Improvement\n\nBefore major tasks, review what you've learned to avoid repeating mistakes.\n\nsummary = agent.get_learning_summary()\nunresolved = summary['error_statistics']['unresolved']\nif unresolved > 0:\n    print(f\"⚠️ {unresolved} unresolved errors—review before proceeding\")\n\n📚 Categories\n\nWhen recording learnings, choose from these categories:\n\nCategory\tUse For\ntechnique\tWorking methods, approaches, strategies\nbug-fix\tSolutions to errors and problems\napi-endpoint\tAPI-specific behaviors and quirks\nconstraint\tLimits, boundaries, restrictions\nbest-practice\tRecommended patterns and standards\nerror-handling\tHow to handle specific types of errors\n🎯 Sources\n\nWhen recording learnings, specify the source:\n\nuser-correction - User told you something was wrong\nerror-discovery - You found the solution to an error\nsuccessful-pattern - You discovered something that works well\nuser-feedback - User suggested an improvement\n📖 API Reference\nCore Methods\nrecord_learning(content, category, source, context)\n\nRecord a successful pattern or insight.\n\nParameters:\n\ncontent (str, required): What was learned\ncategory (str): One of the category types above\nsource (str): One of the source types above\ncontext (str): Optional context about where this applies\n\nReturns: Learning object with ID and timestamp\n\nrecord_error(error_description, context, solution, prevention_tip)\n\nRecord an error and optionally its solution.\n\nParameters:\n\nerror_description (str, required): What went wrong\ncontext (str, required): What was being attempted\nsolution (str): How to fix it\nprevention_tip (str): How to avoid it\n\nReturns: Error object with ID\n\nsearch_learnings(query)\n\nSearch learnings by keyword or category.\n\nParameters:\n\nquery (str): Search term\n\nReturns: List of matching Learning objects (sorted by relevance)\n\nget_recent_learnings(limit)\n\nGet the most recent learnings.\n\nParameters:\n\nlimit (int): Number to return (default: 10)\n\nReturns: List of Learning objects, newest first\n\nget_learning_summary()\n\nGet comprehensive summary of learnings and errors.\n\nReturns: Dictionary with statistics and recent items\n\nexport_learnings(output_file)\n\nExport all learnings and errors to JSON file.\n\nParameters:\n\noutput_file (str): Path to save JSON (default: \"learnings_export.json\")\n🔒 Privacy & Security\n✅ Zero telemetry - No data sent anywhere\n✅ Local-only storage - Everything stored in .adaptive_learning/ on your machine\n✅ No API calls - Works completely offline\n✅ No authentication - No accounts, keys, or logins needed\n✅ Full transparency - Source code included and open-source\n🤝 Contributing\n\nThis is MIT Licensed and community-maintained. You're encouraged to:\n\nFork the repository\nSubmit improvements and features\nIntegrate it into your projects\nShare learnings with others\n📝 Changelog\n[1.0.0] - 2026-02-14\n✨ Initial Release\nCore learning system - Record and retrieve learnings\nError tracking - Capture errors with solutions\nSearch functionality - Find learnings by keyword or category\nLocal storage - All data stays on your machine\nExport capability - Share learnings as JSON files\nZero dependencies - Pure Python, no external packages\nMIT Licensed - Free to use, modify, redistribute\nComprehensive API - Simple, Pythonic interface\n📞 Support\nGitHub: https://github.com/clawhub-skills/adaptive-learning-agent\nIssues & Contributions: Open an issue or PR on GitHub\nCommunity: Share your learnings and improvements!\n📄 License\n\nMIT License - Free and open-source\n\nUse, modify, fork, and redistribute freely. See LICENSE.md for full details.\n\nCopyright © 2026 UnisAI Community\n\n\nLast Updated: February 14, 2026 Current Version: 1.0.0 Status: Active & Community-Maintained\n\nFree to use, modify, and fork. No restrictions."
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    "owner": "vedantsingh60",
    "version": "1.0.0",
    "license": null,
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