在本地捕获、存储和检索错误、更正和最佳实践，以持续改进 AI 代理工作流程和知识。
数据来源：ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一：命令行安装（推荐）
推荐（无需提前安装 clawhub）
npx clawhub@latest --dir ~/.claude/skills install adaptive-learning-agents或使用 clawhub CLI（需提前安装）
clawhub --dir ~/.claude/skills install adaptive-learning-agents⚠️ 需要 Node.js 18+，没有 Node？请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二：手动下载安装（无需 Node）
下载 ZIP，解压后将文件夹放到以下路径，重启 Agent 即可：
安装路径
~/.claude/skills/adaptive-learning-agents/💡解压后将文件夹放到上方路径，重启 Agent 即可生效
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
---
Working with Claude or any AI agent means encountering:
But there's no systematic way to learn from these moments and apply the knowledge next time.
Adaptive Learning Agent captures every error, correction, and successful pattern automatically. Then retrieves relevant learnings before tackling similar problems again.
---
1. Record Learnings
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
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.
✅ 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
---
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")---
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---
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"
)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"
)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"
)Export learnings and share with your team or future projects.
agent.export_learnings("team_learnings.json")
# Share this file with teammatesBefore 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"⚠️ {unresolved} unresolved errors—review before proceeding")---
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 |
---
When recording learnings, specify the source:
user-correction - User told you something was wrongerror-discovery - You found the solution to an errorsuccessful-pattern - You discovered something that works welluser-feedback - User suggested an improvement---
record_learning(content, category, source, context)Record a successful pattern or insight.
Parameters:
content (str, required): What was learnedcategory (str): One of the category types abovesource (str): One of the source types abovecontext (str): Optional context about where this appliesReturns: 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 wrongcontext (str, required): What was being attemptedsolution (str): How to fix itprevention_tip (str): How to avoid itReturns: Error object with ID
search_learnings(query)Search learnings by keyword or category.
Parameters:
query (str): Search termReturns: 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")---
.adaptive_learning/ on your machine---
This is MIT Licensed and community-maintained. You're encouraged to:
---
---
---
MIT License - Free and open-source
Use, modify, fork, and redistribute freely. See 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.
安装 所有人的自我提升 后，可以对 AI 说这些话来触发它
Help me get started with Self Improvement For All
Explains what Self Improvement For All does, walks through the setup, and runs a quick demo based on your current project
Use Self Improvement For All to capture, store, and retrieve errors, corrections, and best practice...
Invokes Self Improvement For All with the right parameters and returns the result directly in the conversation
What can I do with Self Improvement For All in my marketing & growth workflow?
Lists the top use cases for Self Improvement For All, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/adaptive-learning-agents/ 目录（个人级，所有项目可用），或 .claude/skills/adaptive-learning-agents/（项目级）。重启 AI 客户端后，用 /adaptive-learning-agents 主动调用，或让 AI 根据上下文自动发现并使用。
所有人的自我提升 支持 Claude、Cursor、OpenClaw，可与这些 AI 平台无缝集成，扩展其能力。
所有人的自我提升 可免费安装使用。请查阅仓库了解许可证信息。
在本地捕获、存储和检索错误、更正和最佳实践，以持续改进 AI 代理工作流程和知识。
所有人的自我提升 属于「Marketing & Growth」分类，该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my marketing & growth tasks using Self Improvement For All
Identifies repetitive steps in your workflow and sets up Self Improvement For All to handle them automatically