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。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 130 | 3,991 | 8 | - |
| SkillHub ↗ | 129 | 4,255 | 8 | — |
各平台的计数口径不同,统计时间不一致。
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
以下是根据技能文档生成的示例,不代表实际运行结果。
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 |
|---|---|---|---|
| 2026-09 | 4,255 | — | 8 |
| 2026-08 | 3,880 | +3.9% | 8 |
| 2026-07 | 3,734 | +9.2% | 8 |
| 2026-06 | 3,418 | +6.0% | 8 |
| 2026-05 | 3,224 | — | 7 |
| 2026-03 | 1,782 | — | 4 |
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。
---
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/"]}}
---
## 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`
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在保留公式、格式与工作簿结构的前提下创建、检查并修改 Excel 文件的技能。
处理 Word 与 DOCX 文档的作业规范,重点是改完之后样式、编号与版式不跑偏。
把数据库、表格与导出的原始数据变成可决策结论的数据分析技能。
改 PPT 之前先看清结构的一套规范,目标是改完还能干净渲染。
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。