Iván

Self-Evolving

AI 智能体v1.0.0
作者:Iván更新于 2026-05-11self-evolving@clawhub_ivangdavila/self-evolving
工作流编排记忆增强

据 AlphaMoat 统计,截至 2026-09,Self-Evolving 累计下载 4,255 次、获 8 个星标,在全部 5,078 个 AI 技能中排名第 2,331 位,在「AI 智能体」分类(969 个)中排名第 453 位。

累计安装
130
累计下载
4,255
星标
8
全站排名
#2,331
全站前 46% · 「AI 智能体」前 47%
平台累计安装累计下载星标评论
ClawHub ↗1303,9918-
SkillHub ↗1294,2558—

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

该技能要求从真实摩擦出发,每次只测试一个改动,并用明确证据比较速度、质量或可靠性。只有在至少三次可比使用中持续有效时,才将模式提升为稳定规则;未验证的想法应保留为实验。持久保存的偏好和触发规则可能超出原有适用范围,应定期检查其范围与有效性。

资料发布于 2026-10-29

可以用来做什么

  • 复盘重复出现的工作流程问题
  • 以小规模实验比较一种提示或决策规则
  • 整理经多次验证的稳定做法

什么时候用

  • 某个工作流反复出现失败、纠正或瓶颈时
  • 能够保留基线并逐次只改一个因素时
  • 可以用具体结果而非直觉评估变化时

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。

请根据最近一次重复出现的问题提出一个最小改动,先记录基线和实验结果;不要把规则提升为长期记忆,直到有多次可比证据。

月度数据趋势

累计下载4,255
1,5843,0194,4532026-03 · 累计下载 1,7822026-05 · 累计下载 3,2242026-06 · 累计下载 3,4182026-07 · 累计下载 3,7342026-08 · 累计下载 3,8802026-09 · 累计下载 4,25526-0326-09
星标8
4682026-03 · 星标 42026-05 · 星标 72026-06 · 星标 82026-07 · 星标 82026-08 · 星标 82026-09 · 星标 826-0326-09

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
2026-094,255—8
2026-083,880+3.9%8
2026-073,734+9.2%8
2026-063,418+6.0%8
2026-053,224—7
2026-031,782—4

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。

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/"]}}
---
展开全部(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 智能体」分类下的热门技能

Iván 的其他技能

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。