Keyserkazi1

Self improving

AI 智能体v1.0.0
作者:Keyserkazi1更新于 2026-05-11self-improving-100@clawhub_keyserkazi1/self-improving-100
提示词优化上下文管理记忆增强#Self Improving#Learning

据 AlphaMoat 统计,截至 2026-10,Self improving 累计下载 73,848 次、获 2 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 338 位,在「AI 智能体」分类(1,632 个)中排名第 75 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
7.4万
星标
2
全站下载量排名
#338
全站前 4% · 「AI 智能体」前 5%
平台累计下载星标评论
ClawHub ↗1,176--
SkillHub ↗7.4万2—

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

该技能在用户指出错误、工具失败或完成重要工作后引导智能体自我检查,并使用本地分层记忆记录可复用模式。持久记忆会影响未来行为,也可能保存超出预期的内容;仅在确实需要长期记忆时启用,并定期检查或删除相关文件。初始化时可能建议修改工作区指导文件,应先审阅这些变更。

资料发布于 2026-10-29

可以用来做什么

  • 复盘错误或用户纠正并记录经验
  • 评估重要任务结果并改进后续流程
  • 整理需要长期复用的本地工作模式

什么时候用

  • 用户纠正了智能体或指出输出问题时
  • 工具或操作失败后需要记录教训时
  • 可以接受本地持久记忆并定期审查时

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。

请复盘刚才的错误,提出一条可验证的改进建议;先说明是否要写入持久记忆或修改工作区文件,等我确认后再保存。

月度数据趋势

累计下载7.4万
04.0万8.0万2026-03 · 累计下载 2042026-05 · 累计下载 4912026-06 · 累计下载 6242026-07 · 累计下载 1,0362026-08 · 累计下载 3,2542026-09 · 累计下载 1.6万2026-10 · 累计下载 7.4万26-0326-0926-10
星标2
0122026-03 · 星标 -2026-05 · 星标 -2026-06 · 星标 -2026-07 · 星标 -2026-08 · 星标 -2026-09 · 星标 -2026-10 · 星标 226-0326-0926-10

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
2026-107.4万—2
2026-091.6万—-
2026-083,254+214.1%-
2026-071,036+66.0%-
2026-06624+27.1%-
2026-05491—-
2026-03204—-

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。

8.4 KB · ClawHub v1.0.0
---
name: Self-Improving + Proactive Agent
slug: self-improving
version: 1.2.16
homepage: https://clawic.com/skills/self-improving
description: "Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task."
changelog: "Clarifies the setup flow for proactive follow-through and safer installation behavior."
metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"],"configPaths.optional":["./AGENTS.md","./SOUL.md","./HEARTBEAT.md"]}}
---
展开全部(共 251 行)
## When to Use

User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.

## Architecture

Memory lives in `~/self-improving/` with tiered structure. If `~/self-improving/` does not exist, run `setup.md`.
Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and `HEARTBEAT.md` files, with recurring maintenance routed through `heartbeat-rules.md`.

```
~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
├── projects/          # Per-project learnings
├── domains/           # Domain-specific (code, writing, comms)
├── archive/           # COLD: decayed patterns
└── corrections.md     # Last 50 corrections log
```

## Quick Reference

| Topic | File |
|-------|------|
| Setup guide | `setup.md` |
| Heartbeat state template | `heartbeat-state.md` |
| Memory template | `memory-template.md` |
| Workspace heartbeat snippet | `HEARTBEAT.md` |
| Heartbeat rules | `heartbeat-rules.md` |
| Learning mechanics | `learning.md` |
| Security boundaries | `boundaries.md` |
| Scaling rules | `scaling.md` |
| Memory operations | `operations.md` |
| Self-reflection log | `reflections.md` |
| OpenClaw HEARTBEAT seed | `openclaw-heartbeat.md` |

## Requirements

- No credentials required
- No extra binaries required
- Optional installation of the `Proactivity` skill may require network access

## Learning Signals

Log automatically when you notice these patterns:

**Corrections** → add to `corrections.md`, evaluate for `memory.md`:
- "No, that's not right..."
- "Actually, it should be..."
- "You're wrong about..."
- "I prefer X, not Y"
- "Remember that I always..."
- "I told you before..."
- "Stop doing X"
- "Why do you keep..."

**Preference signals** → add to `memory.md` if explicit:
- "I like when you..."
- "Always do X for me"
- "Never do Y"
- "My style is..."
- "For [project], use..."

**Pattern candidates** → track, promote after 3x:
- Same instruction repeated 3+ times
- Workflow that works well repeatedly
- User praises specific approach

**Ignore** (don't log):
- One-time instructions ("do X now")
- Context-specific ("in this file...")
- Hypotheticals ("what if...")

## Self-Reflection

After completing significant work, pause and evaluate:

1. **Did it meet expectations?** — Compare outcome vs intent
2. **What could be better?** — Identify improvements for next time
3. **Is this a pattern?** — If yes, log to `corrections.md`

**When to self-reflect:**
- After completing a multi-step task
- After receiving feedback (positive or negative)
- After fixing a bug or mistake
- When you notice your output could be better

**Log format:**
```
CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]
```

**Example:**
```
CONTEXT: Building Flutter UI
REFLECTION: Spacing looked off, had to redo
LESSON: Check visual spacing before showing user
```

Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.

## Quick Queries

| User says | Action |
|-----------|--------|
| "What do you know about X?" | Search all tiers for X |
| "What have you learned?" | Show last 10 from `corrections.md` |
| "Show my patterns" | List `memory.md` (HOT) |
| "Show [project] patterns" | Load `projects/{name}.md` |
| "What's in warm storage?" | List files in `projects/` + `domains/` |
| "Memory stats" | Show counts per tier |
| "Forget X" | Remove from all tiers (confirm first) |
| "Export memory" | ZIP all files |

## Memory Stats

On "memory stats" request, report:

```
📊 Self-Improving Memory

HOT (always loaded):
  memory.md: X entries

WARM (load on demand):
  projects/: X files
  domains/: X files

COLD (archived):
  archive/: X files

Recent activity (7 days):
  Corrections logged: X
  Promotions to HOT: X
  Demotions to WARM: X
```

## Common Traps

| Trap | Why It Fails | Better Move |
|------|--------------|-------------|
| Learning from silence | Creates false rules | Wait for explicit correction or repeated evidence |
| Promoting too fast | Pollutes HOT memory | Keep new lessons tentative until repeated |
| Reading every namespace | Wastes context | Load only HOT plus the smallest matching files |
| Compaction by deletion | Loses trust and history | Merge, summarize, or demote instead |

## Core Rules

### 1. Learn from Corrections and Self-Reflection
- Log when user explicitly corrects you
- Log when you identify improvements in your own work
- Never infer from silence alone
- After 3 identical lessons → ask to confirm as rule

### 2. Tiered Storage
| Tier | Location | Size Limit | Behavior |
|------|----------|------------|----------|
| HOT | memory.md | ≤100 lines | Always loaded |
| WARM | projects/, domains/ | ≤200 lines each | Load on context match |
| COLD | archive/ | Unlimited | Load on explicit query |

### 3. Automatic Promotion/Demotion
- Pattern used 3x in 7 days → promote to HOT
- Pattern unused 30 days → demote to WARM
- Pattern unused 90 days → archive to COLD
- Never delete without asking

### 4. Namespace Isolation
- Project patterns stay in `projects/{name}.md`
- Global preferences in HOT tier (memory.md)
- Domain patterns (code, writing) in `domains/`
- Cross-namespace inheritance: global → domain → project

### 5. Conflict Resolution
When patterns contradict:
1. Most specific wins (project > domain > global)
2. Most recent wins (same level)
3. If ambiguous → ask user

### 6. Compaction
When file exceeds limit:
1. Merge similar corrections into single rule
2. Archive unused patterns
3. Summarize verbose entries
4. Never lose confirmed preferences

### 7. Transparency
- Every action from memory → cite source: "Using X (from projects/foo.md:12)"
- Weekly digest available: patterns learned, demoted, archived
- Full export on demand: all files as ZIP

### 8. Security Boundaries
See `boundaries.md` — never store credentials, health data, third-party info.

### 9. Graceful Degradation
If context limit hit:
1. Load only memory.md (HOT)
2. Load relevant namespace on demand
3. Never fail silently — tell user what's not loaded

## Scope

This skill ONLY:
- Learns from user corrections and self-reflection
- Stores preferences in local files (`~/self-improving/`)
- Maintains heartbeat state in `~/self-improving/heartbeat-state.md` when the workspace integrates heartbeat
- Reads its own memory files on activation

This skill NEVER:
- Accesses calendar, email, or contacts
- Makes network requests
- Reads files outside `~/self-improving/`
- Infers preferences from silence or observation
- Deletes or blindly rewrites self-improving memory during heartbeat cleanup
- Modifies its own SKILL.md

## Data Storage

Local state lives in `~/self-improving/`:

- `memory.md` for HOT rules and confirmed preferences
- `corrections.md` for explicit corrections and reusable lessons
- `projects/` and `domains/` for scoped patterns
- `archive/` for decayed or inactive patterns
- `heartbeat-state.md` for recurring maintenance markers

## Related Skills
Install with `clawhub install <slug>` if user confirms:

- `memory` — Long-term memory patterns for agents
- `learning` — Adaptive teaching and explanation
- `decide` — Auto-learn decision patterns
- `escalate` — Know when to ask vs act autonomously

## Feedback

- If useful: `clawhub star self-improving`
- Stay updated: `clawhub sync`

「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。