Skill Experience Layer for OpenClaw#

A failure-driven learning mechanism that systematically accumulates experience from tool calls, mistakes, and successes. Each tool/skill category has its own experience file with:

• Lessons learned from failures
• Common mistakes pattern matching
• Preventive best practices
• Statistics tracking (total executions, success/failure counts)

Core Philosophy#

"The man who repeats the same mistake and expects different results is a fool." - This mechanism eliminates that.

Every failure is a one-time tuition payment for permanent knowledge.

How It Works#

1. Pre-execution Experience Loading#

Before any tool call, the agent must:

1. Identify the experience category
2. Read the compact experience file
3. Apply learned best practices
4. Avoid known mistakes

2. Post-execution Experience Update#

If the tool call fails:

1. Stop immediately and analyze root cause
2. Add/Update the experience entry
3. Record the mistake and prevention
4. Continue only after updating

If the tool call succeeds:

1. Optionally add high-value best practices
2. Keep experience lean and actionable

3. Weekly/Monthly Maintenance#

• Weekly: Remove outdated experiences
• Monthly: Clean up duplicates, consolidate patterns

Experience Category Structure#

json

{
  "type": "skill",
  "name": "tool-name",
  "lastUpdated": "ISO timestamp",
  "totalExecutions": N,
  "successCount": N,
  "failureCount": N,
  "experiences": [
    {
      "mistake": "Description of what went wrong",
      "when": "Context/scenario where it happens",
      "avoidance": "How to avoid this mistake",
      "count": 1
    }
  ],
  "patterns": {
    "commonMistakes": [
      "List of frequent mistakes"
    ],
    "bestPractices": [
      "List of proven best practices"
    ]
  }
}

Built-in Categories#

CategoryDescriptionreadFile reading, web fetchingwriteFile writing, editingeditExact text replacement in filesexecShell command executionbrowserBrowser automationmessageMessage sending, media uploadsearchWeb search toolsfeishuAll Feishu/Lark API toolsfeishu-*Fine-grained Feishu sub-skill experiencesmemoryMemory search and retrievalskillSkill management operationscronCron job scheduling and delivery modesvideo-framesVideo frame extraction with ffmpeg

Installation#

bash

clawhub install experience-layer

Or manually:

bash

mkdir -p ~/.openclaw/workspace/memory/experiences
# Copy the empty category template to start
cp templates/empty-category.json ~/.openclaw/workspace/memory/experiences/your-category.json
# Edit the JSON and add your first experience

What's Included#

• SKILL.md - Complete documentation

• templates/empty-category.json - Starter template for new categories

• templates/example.json - Real-world example from production usage

• examples/ - Full production-ready experience files:

  • examples/edit.json - Experience for file editing
  • examples/exec.json - Experience for shell command execution
  • examples/feishu.json - Experience for Feishu/Lark API
  • examples/message.json - Experience for message sending
  • examples/cron.json - Experience for cron job scheduling

Usage in OpenClaw Workflow#

1. Before calling a tool:

javascript

read memory/experiences/{category}.json
apply the best practices and avoid the common mistakes

1. After failure:

javascript

analyze root cause
update the corresponding experience file
record the lesson learned
then retry

Benefits#

• No repeated mistakes - Same mistake only pays tuition once
• Progressive improvement - System gets better over time
• Compact and searchable - JSON format for easy semantic search
• Low overhead - Small files, quick to load
• Open extension - Add new categories as needed

Changelog#

1.0.2 (2026-03-18)#

• Add examples/ directory with full production experience files
• Include 5 complete real-world experience JSON: edit, exec, feishu, message, cron
• Users can drop these directly into their memory/experiences/ to start

1.0.1 (2026-03-18)#

• Add template directory with empty category template
• Add complete example JSON from production
• Improve installation documentation

1.0.0 (2026-03-18)#

• Initial public release
• 25+ experience categories with real-world lessons from production usage
• Complete JSON schema
• Documentation

Author#

jilanfang

License#

MIT