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Self Improvement

通用型智能体自我改进 Skill:通过命令行收集用户对输出的反馈,提取 +1/-1 评分与改进提示,按任务名统计并生成可执行的改进建议,结果保存到本地 JSON 记忆文件;支持统计查询、改进生成与定时任务触发。

adelpro
adelpro
热度 57.06
最近更新 2026-05-20 03:31:11
分类:知识依赖:代码执行 / 读写文件应用场景:笔记整理 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add agent-self-improvement
Metadata
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