A

Self Improvement

AI 智能体v1.0.0
作者:adelpro更新于 2026-11-05agent-self-improvement@clawhub_adelpro/agent-self-improvement
Agent 框架上下文管理记忆增强

据 AlphaMoat 统计,截至 2026-10,Self Improvement 累计下载 28,242 次、获 1 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 994 位,在「AI 智能体」分类(1,632 个)中排名第 151 位。

列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。

累计下载
2.8万
星标
1
全站下载量排名
#994
全站前 10% · 「AI 智能体」前 10%
平台累计下载星标评论
SkillHub ↗2.8万1—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

把用户对输出的回应存下来,从中抽取正向或纠正性的评分,并归纳指令式提示的共性,据此生成改进建议。方法参考 OpenClaw-RL 的 PRM 式评估思路。反馈质量决定结论质量,源文只描述流程,未给效果数据。

资料发布于 2026-10-27

可以用来做什么

  • 把用户的纠正意见沉淀成改进项
  • 定期回看哪类回答最常被否定

什么时候用

  • 想让 agent 随使用逐步调整时
  • 手上有成批用户反馈要归纳时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

把这些用户反馈整理成改进建议
看看最近被纠正最多的是哪类回答

月度数据趋势

累计下载2.8万
01.5万3.0万2026-03 · 累计下载 1,2772026-05 · 累计下载 3,3242026-07 · 累计下载 5,0322026-08 · 累计下载 6,5572026-09 · 累计下载 1.1万2026-10 · 累计下载 2.8万26-0326-0926-10
星标1
0112026-03 · 星标 -2026-05 · 星标 12026-07 · 星标 12026-08 · 星标 12026-09 · 星标 12026-10 · 星标 126-0326-0926-10

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-102.8万—1
2026-091.1万—1
2026-086,557+30.3%1
2026-075,032—1
2026-053,324—1
2026-031,277—-

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

3.5 KB · SkillHub v1.0.0
---
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
展开全部(共 147 行)

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](https://arxiv.org/abs/2603.10165):

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

### Daily: Generate Improvements (10 AM)
```json
{
  "id": "improvement-suggestions",
  "schedule": "0 10 * * *",
  "message": "SKILL:self-improvement --improve daily-digest → Telegram"
}
```

### Weekly: Summary (Saturday 9 AM)
```json
{
  "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

「AI 智能体」分类下的热门技能

数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。