Iván

Self-Evolving

AI 智慧體v1.0.0
作者:Iván更新於 2026-05-11self-evolving@clawhub_ivangdavila/self-evolving
工作流編排記憶增強

據 AlphaMoat 統計,截至 2026-09,Self-Evolving 累計下載 4,255 次、獲 8 個星標,在全部 5,078 個 AI 技能中排名第 2,331 位,在「AI 智慧體」分類(969 個)中排名第 453 位。

累計安裝
130
累計下載
4255
星標
8
全站排名
#2,331
全站前 46% · 「AI 智慧體」前 47%
平台累計安裝累計下載星標評論
ClawHub ↗13039918-
SkillHub ↗12942558—

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

該技能要求從真實摩擦出發,每次只測試一個改動,並用明確證據比較速度、質量或可靠性。只有在至少三次可比使用中持續有效時,才將模式提升為穩定規則;未驗證的想法應保留為實驗。持久儲存的偏好和觸發規則可能超出原有適用範圍,應定期檢查其範圍與有效性。

資料釋出於 2026-10-29

可以用來做什麼

  • 覆盤重複出現的工作流程問題
  • 以小規模實驗比較一種提示或決策規則
  • 整理經多次驗證的穩定做法

什麼時候用

  • 某個工作流反覆出現失敗、糾正或瓶頸時
  • 能夠保留基線並逐次只改一個因素時
  • 可以用具體結果而非直覺評估變化時

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。

請根據最近一次重複出現的問題提出一個最小改動,先記錄基線和實驗結果;不要把規則提升為長期記憶,直到有多次可比證據。

月度資料趨勢

累計下載4255
1584301944532026-03 · 累計下載 17822026-05 · 累計下載 32242026-06 · 累計下載 34182026-07 · 累計下載 37342026-08 · 累計下載 38802026-09 · 累計下載 425526-0326-09
星標8
4682026-03 · 星標 42026-05 · 星標 72026-06 · 星標 82026-07 · 星標 82026-08 · 星標 82026-09 · 星標 826-0326-09

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
2026-094255—8
2026-083880+3.9%8
2026-073734+9.2%8
2026-063418+6.0%8
2026-053224—7
2026-031782—4

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。

4.5 KB · ClawHub v1.0.0
---
name: Self-Evolving
slug: self-evolving
version: 1.0.0
homepage: https://clawic.com/skills/self-evolving
description: Improve reusable agent workflows with reflective experiments, value checks, and local pattern memory.
changelog: Introduces a clearer local evolution loop, setup guidance, and safer local memory boundaries.
metadata: {"clawdbot":{"emoji":"🧬","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-evolving/"]}}
---
展開全部(共 108 行)
## When to Use

User wants the agent to improve a repeated workflow without blind self-rewrites. The skill handles local experiment logs, promotion of proven patterns, and explicit value gates before a new behavior becomes stable.

## Architecture

Memory lives in `~/self-evolving/`. If `~/self-evolving/` does not exist, run `setup.md`. See `memory-template.md`, `memory.md`, `experiments.md`, `evolution-loop.md`, and `boundaries.md` for the operating model.

```text
~/self-evolving/
├── memory.md        # HOT: stable rules, guardrails, activation cues
├── experiments.md   # WARM: tentative mutations and outcomes
└── archive/         # COLD: retired patterns and old experiments
```

## Quick Reference

| Topic | File |
|-------|------|
| Setup guide | `setup.md` |
| Memory template | `memory-template.md` |
| Hot memory baseline | `memory.md` |
| Experiment log format | `experiments.md` |
| Evolution cycle | `evolution-loop.md` |
| Safety boundaries | `boundaries.md` |

## Requirements

- No credentials required
- No extra binaries required
- No network access required

## Core Rules

### 1. Start From Real Friction
- Evolve only after a failed attempt, repeated correction, or measurable bottleneck.
- Do not invent mutations just because a task feels interesting.

### 2. Change One Lever at a Time
- Test one prompt pattern, decision rule, retrieval step, or file habit per experiment.
- Small mutations make the winning variable obvious.

### 3. Gate by Value, Not Novelty
- Promote a pattern only when it improves speed, quality, or reliability across at least three comparable uses.
- Unproven ideas stay tentative in `experiments.md`.

### 4. Keep Local Evidence
- Record the trigger, mutation, outcome, and next action for every experiment.
- Tell the user before the first persistent write that this skill keeps concise local notes for repeat improvement.
- Promote durable rules into `memory.md` only after evidence repeats.

### 5. Prefer Promotion Over Rewrite
- Convert winners into short rules, checklists, or retrieval triggers.
- Stable systems compound by accumulation, not by starting over.

### 6. Respect Hard Boundaries
- Follow `boundaries.md` before storing data or changing behavior.
- Never modify the installed skill files, exfiltrate unrelated data, or run hidden experiments on the user.

## Common Traps

| Trap | Why It Fails | Better Move |
|------|--------------|-------------|
| Rewriting the whole workflow after one mistake | You cannot isolate what actually helped | Test one mutation and compare against the previous baseline |
| Promoting an idea after one good run | Lucky wins become noisy defaults | Wait for three comparable wins before promotion |
| Logging vague lessons like "be smarter" | Future retrieval becomes useless | Write the exact trigger, decision, and expected outcome |
| Optimizing for novelty instead of value | The system churns without compounding | Keep only behaviors that measurably save time or reduce errors |
| Learning from silence | Lack of complaint is not proof | Require explicit feedback or repeated success evidence |

## Security & Privacy

**Data that leaves your machine:**
- None by default

**Data that stays local:**
- Stable rules, guardrails, and activation notes in `~/self-evolving/memory.md`
- Tentative experiments and outcomes in `~/self-evolving/experiments.md`
- First-time local storage should be announced before the first write

**This skill does NOT:**
- Call external APIs
- Read or store credentials
- Modify its own installed instructions
- Read unrelated files outside the active task plus `~/self-evolving/`

## Related Skills
Install with `clawhub install <slug>` if user confirms:
- `self-improving` — learn from corrections and compound execution quality over time
- `memory` — keep durable long-term context and retrieval patterns
- `decide` — compare options and commit to a clear next move
- `learning` — structure deliberate practice and feedback loops
- `proactivity` — follow through on next steps once a better pattern is chosen

## Feedback

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

「AI 智慧體」分類下的熱門技能

Iván 的其他技能

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。