yiyi-9

Self Improving 1.2.10

AI 智能体v1.0.0
作者:yiyi-9更新于 2026-05-11self-improving-1-2-10@clawhub_yiyi-9/self-improving-1-2-10
上下文管理记忆增强#Self Improving#Learning

据 AlphaMoat 统计,截至 2026-09,Self Improving 1.2.10 累计下载 3,801 次、获 0 个星标,在全部 5,078 个 AI 技能中排名第 2,775 位,在「AI 智能体」分类(969 个)中排名第 550 位。

累计安装
233
累计下载
3,801
星标
-
全站排名
#2,775
全站前 55% · 「AI 智能体」前 57%
平台累计安装累计下载星标评论
ClawHub ↗1063,178--
SkillHub ↗2333,801-—

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

该技能让智能体具备自我反思、自我批评、自我学习与自组织记忆能力:在 ~/self-improving/ 维护分层记忆(HOT 的 memory.md 始终加载、WARM 的 projects/domains 按需加载、COLD 的 archive 按查询加载),自动记录用户纠正与偏好信号,同一模式出现 3 次晋升为 HOT、30 天未用降级、90 天归档,冲突时按最具体优先解决。支持自省日志(CONTEXT/REFLECTION/LESSON)、记忆统计、导出与遗忘确认,并划定安全边界:不存凭据/健康数据/第三方信息、不联网、不读取记忆目录之外的文件。

资料发布于 2026-10-27

可以用来做什么

  • 把用户的纠正和偏好沉淀为跨会话的分层本地记忆
  • 完成重要任务后自动自省,把教训记入 corrections.md
  • 查询『你知道关于 X 的什么』『我的模式有哪些』或导出/清理记忆

什么时候用

  • 当用户纠正智能体或指出错误、希望后续会话不再重犯时
  • 当希望知识跨会话复利积累且无需手动维护时
  • 当开始重要工作前需要加载相关记忆、完成后需要自省时

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。

记住:我永远希望提交信息用中文写
刚才那个方案哪里可以做得更好?反省一下记下来
看看你最近学到了什么,给我看记忆统计

月度数据趋势

累计下载3,801
1,7922,8713,9502026-03 · 累计下载 1,9412026-05 · 累计下载 2,6822026-06 · 累计下载 2,8712026-07 · 累计下载 3,1702026-08 · 累计下载 3,4072026-09 · 累计下载 3,80126-0326-09
星标-
0112026-03 · 星标 -2026-05 · 星标 -2026-06 · 星标 -2026-07 · 星标 -2026-08 · 星标 -2026-09 · 星标 -26-0326-09

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
2026-093,801—-
2026-083,407+7.5%-
2026-073,170+10.4%-
2026-062,871+7.0%-
2026-052,682—-
2026-031,941—-

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。

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/"]}}
---
展开全部(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 智能体」分类下的热门技能

数据月份:2026-09 · 下载/星标/安装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。