yiyi-9

Self Improving 1.2.10

AI 智慧體v1.0.0
作者:yiyi-9更新於 2026-05-11self-improving-1-2-10@clawhub_yiyi-9/self-improving-1-2-10
上下文管理記憶增強#Self Improving#Learning

據 AlphaMoat 統計,截至 2026-09,Self Improving 1.2.10 累計下載 3,801 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 2,775 位,在「AI 智慧體」分類(969 個)中排名第 550 位。

累計安裝
233
累計下載
3801
星標
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全站排名
#2,775
全站前 55% · 「AI 智慧體」前 57%
平台累計安裝累計下載星標評論
ClawHub ↗1063178--
SkillHub ↗2333801-—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該技能讓智慧體具備自我反思、自我批評、自我學習與自組織記憶能力:在 ~/self-improving/ 維護分層記憶(HOT 的 memory.md 始終載入、WARM 的 projects/domains 按需載入、COLD 的 archive 按查詢載入),自動記錄使用者糾正與偏好訊號,同一模式出現 3 次晉升為 HOT、30 天未用降級、90 天歸檔,衝突時按最具體優先解決。支援自省日誌(CONTEXT/REFLECTION/LESSON)、記憶統計、匯出與遺忘確認,並劃定安全邊界:不存憑據/健康資料/第三方資訊、不聯網、不讀取記憶目錄之外的檔案。

資料釋出於 2026-10-27

可以用來做什麼

  • 把使用者的糾正和偏好沉澱為跨會話的分層本地記憶
  • 完成重要任務後自動自省,把教訓記入 corrections.md
  • 查詢『你知道關於 X 的什麼』『我的模式有哪些』或匯出/清理記憶

什麼時候用

  • 當使用者糾正智慧體或指出錯誤、希望後續會話不再重犯時
  • 當希望知識跨會話複利積累且無需手動維護時
  • 當開始重要工作前需要載入相關記憶、完成後需要自省時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

記住:我永遠希望提交資訊用中文寫
剛才那個方案哪裡可以做得更好?反省一下記下來
看看你最近學到了什麼,給我看記憶統計

月度資料趨勢

累計下載3801
1792287139502026-03 · 累計下載 19412026-05 · 累計下載 26822026-06 · 累計下載 28712026-07 · 累計下載 31702026-08 · 累計下載 34072026-09 · 累計下載 380126-0326-09
星標-
0112026-03 · 星標 -2026-05 · 星標 -2026-06 · 星標 -2026-07 · 星標 -2026-08 · 星標 -2026-09 · 星標 -26-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-093801—-
2026-083407+7.5%-
2026-073170+10.4%-
2026-062871+7.0%-
2026-052682—-
2026-031941—-

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。

6.6 KB · ClawHub v1.0.0
---
name: Self-Improving Agent (Proactive Self-Reflection)
slug: self-improving
version: 1.2.10
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 before starting work and after responding to the user.
changelog: "Sharper setup now lists relevant memory before non-trivial work, with a title that highlights proactive self-reflection."
metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"]}}
---
展開全部(共 218 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`.

```
~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── 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` |
| Memory template | `memory-template.md` |
| Learning mechanics | `learning.md` |
| Security boundaries | `boundaries.md` |
| Scaling rules | `scaling.md` |
| Memory operations | `operations.md` |
| Self-reflection log | `reflections.md` |

## Detection Triggers

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
```

## 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/`)
- 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
- Modifies its own SKILL.md

## 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 智慧體」分類下的熱門技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。