According to AlphaMoat, as of 2026-10, Self Improvement has 28,242 downloads and 1 stars — ranked by downloads #994 among 10,914 AI skills in this ranking, and #151 of 1,632 in AI Agent.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| SkillHub ↗ | 28.2K | 1 | — |
Platforms count differently and update at different times.
It stores how users respond to outputs, pulls a positive or corrective score from each response, and groups the directive hints to produce improvement suggestions. The method follows the PRM-style evaluation described in OpenClaw-RL. Feedback quality bounds the conclusions, and the source only describes the flow without any effectiveness data.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 28.2K | — | 1 |
| 2026-09 | 10.5K | — | 1 |
| 2026-08 | 6.6K | +30.3% | 1 |
| 2026-07 | 5.0K | — | 1 |
| 2026-05 | 3.3K | — | 1 |
| 2026-03 | 1.3K | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 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
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以李白(701—762,字太白,号青莲居士,世称诗仙)的诗学观、意象系统与口吻,进行唐诗赏析、文学评论、考据问答与拟体创作。触发词:李白、李太白、太白、青莲居士、诗仙、谪仙人、将进酒、静夜思、蜀道难、唐诗、盛唐诗人、浪漫主义诗人、拟李白风格、以李白视角、李白会怎么写、用李白口吻。能力:诗词鉴赏与风格DNA解析、诗学观与文学谱系评述、诗句出处考据与真伪边界标注、以李白口吻答问、拟李白体旧体诗(必显式标注非原作)。适用边界:仅用于唐代文学与古典诗歌相关话题,非此类话题不触发;本 skill 为文学普及与研究辅助,不代表李白本人立场,所拟之作均非李白原作;涉及生平佚事、篇目归属等内容不构成学术定论,皆标存疑或待考。
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.