Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Self Improvement For All
Capture, store, and retrieve errors, corrections, and best practices locally to continuously improve AI agent workflows and knowledge.
skill openclawclawhub Free
Capture, store, and retrieve errors, corrections, and best practices locally to continuously improve AI agent workflows and knowledge.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- adaptive_learning_agent.py, LICENSE.md, manifest.yaml, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- vedantsingh60
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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) β’ Zero dependencies β’ Works locally
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
Four Core Functions
1. Record Learnings agent.record_learning( content="Use claude-sonnet for 90% of tasksβfaster and cheaper", category="technique", context="Model selection" ) Capture successful patterns, insights, and best practices. 2. Record Errors 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 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 summary = agent.get_learning_summary() print(agent.format_learning_summary()) Understand what you've learned at a glance.
Key Features
β Zero dependencies - Pure Python, works everywhere β Local-only storage - All data on your machine, no uploads β MIT Licensed - Free to use, modify, fork, redistribute β Automatic categorization - Errors become learnings β Search and filter - Find knowledge by keyword or category β Export capability - Share learnings as JSON β No API keys - Works without any external credentials
π Real-World Example
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] β # 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")
π§ Installation
No installation needed! The skill is pure Python with zero dependencies. # Copy the adaptive_learning_agent.py file to your project # Or import it directly: from adaptive_learning_agent import AdaptiveLearningAgent
Software Development
Record bugs you find and their fixes. Next time you hit a similar error, you have the solution ready. 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. 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. 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. 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. summary = agent.get_learning_summary() unresolved = summary['error_statistics']['unresolved'] if unresolved > 0: print(f"β οΈ {unresolved} unresolved errorsβreview before proceeding")
π Categories
When recording learnings, choose from these categories: CategoryUse 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
π― 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
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")
π Privacy & Security
β Zero telemetry - No data sent anywhere β Local-only storage - Everything stored in .adaptive_learning/ on your machine β No API calls - Works completely offline β No authentication - No accounts, keys, or logins needed β Full transparency - Source code included and open-source
π€ 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
[1.0.0] - 2026-02-14
β¨ 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
π 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!
π License
MIT License - Free and open-source Use, modify, fork, and redistribute freely. See LICENSE.md for full details. Copyright Β© 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts1 Config
- SKILL.md
- LICENSE.md
- adaptive_learning_agent.py
- manifest.yaml