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。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 106 | 3178 | - | - |
| SkillHub ↗ | 233 | 3801 | - | — |
各平台的計數口徑不同,統計時間不一致。
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
以下是根據技能文件生成的示例,不代表實際執行結果。
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 |
|---|---|---|---|
| 2026-09 | 3801 | — | - |
| 2026-08 | 3407 | +7.5% | - |
| 2026-07 | 3170 | +10.4% | - |
| 2026-06 | 2871 | +7.0% | - |
| 2026-05 | 2682 | — | - |
| 2026-03 | 1941 | — | - |
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。
---
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/"]}}
---
## 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`
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
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。