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。
| 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。
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
以下是根据技能文档生成的示例,不代表实际运行结果。
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 |
|---|---|---|---|
| 2026-10 | 2.8万 | — | 1 |
| 2026-09 | 1.1万 | — | 1 |
| 2026-08 | 6,557 | +30.3% | 1 |
| 2026-07 | 5,032 | — | 1 |
| 2026-05 | 3,324 | — | 1 |
| 2026-03 | 1,277 | — | - |
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。
---
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](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
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把盛唐诗人李白的诗学观、意象系统与口吻,蒸馏成 AI 可调用的角色包。
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。