bitcjm/workbuddy-skills/agent-self-improvement

Self-Improvement Skill

Enables any agent to learn and improve from user feedback using PRM-style evaluation.

What It Does

• Capture Feedback: Store user responses to agent outputs
• Evaluate: Extract score (+1 positive, -1 correction)
• Analyze: Find patterns in directive hints
• Suggest: Generate actionable improvements

Concept (OpenClaw-RL Inspired)

From OpenClaw-RL paper:

"Next-state signals encode both evaluative and directive information about the preceding action."

Two signal types:

1. Evaluative: Did the output work? (binary score)
2. Directive: How should it be different? (hints)

Usage

Capture Feedback

SKILL:self-improvement --feedback "Great!" --job daily-report
SKILL:self-improvement --feedback "Add more stats" --job daily-report

Get Stats

SKILL:self-improvement --stats daily-report

Generate Improvements

SKILL:self-improvement --improve daily-report

Arguments

Storage

memory/learning/agent-feedback.json:

{
  "jobs": {
    "daily-digest": {
      "evaluations": [
        { "date": "2026-03-14", "score": 1, "hint": null },
        { "date": "2026-03-13", "score": -1, "hint": "add weekly star comparison" }
      ],
      "improvements": [
        { "date": "2026-03-14", "suggestion": "Add weekly star delta", "implemented": false }
      ]
    }
  }
}

Cron Integration

Daily: Capture Feedback (9:30 AM)

{
  "id": "feedback-capture",
  "schedule": "30 9 * * *",
  "message": "SKILL:self-improvement --job daily-digest"
}

Daily: Generate Improvements (10 AM)

{
  "id": "improvement-suggestions",
  "schedule": "0 10 * * *",
  "message": "SKILL:self-improvement --improve daily-digest → Telegram"
}

Weekly: Summary (Saturday 9 AM)

{
  "id": "weekly-summary",
  "schedule": "0 9 * * 6",
  "message": "SKILL:self-improvement --improve all --weekly"
}

Example Workflow

1. User receives daily digest
2. User responds: "Good! But can you show star trends?"
3. Feedback captured: score=1, hint="show star trends"
4. Next day, improvements generated:

• "Add star trend comparison (last 7 days)"
• "User满意度: 75%"

1. Agent auto-updates prompt

Improvement Suggestion Format

📈 Improvement Suggestions - {job}
 
Stats: 8 evaluations, avg score: 0.75
 
Top Hints:
1. "add weekly star changes" (2x)
2. "use table format" (1x)
 
Suggested Actions:
• Add 7-day star delta to GitHub section
• Use table-image-generator for stats
 
Status: 1 improvement pending

Manual Evaluation

If no user feedback available, manually evaluate:

Evaluate yesterday's output as: good/bad

Related Skills

• compound-engineering: Deep session analysis
• agent-evolver: Legacy name, see self-improvement