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name: self-improvement description: Generic agent self-improvement skill built on OpenClaw-RL research (arxiv.org/abs/2603.10165). Captures evaluative signals (+1/-1) and directive hints from any user feedback, generates improvement suggestions. metadata: openclaw: emoji: 🧬 homepage: https://adelpro.us.kg

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

Arg	Description	Example
--job	Task/job name	daily-digest, weekly-recap
--feedback	User response	"Thanks!", "Add more X"
--score	Manual score override	1, 0, -1
--stats	Show feedback stats	daily-digest
--improve	Generate improvements	daily-digest
--weekly	Weekly summary	(flag)

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%"
5. 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