According to AlphaMoat, as of 2026-09, Self Evolve has 3,593 downloads and 3 stars — ranked #3,031 of 5,078 AI skills overall, and #613 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 98 | 3.0K | 1 | - |
| SkillHub ↗ | 323 | 3.6K | 3 | — |
Platforms count differently and update at different times.
A tick synchronizes experiment state, records real telemetry, evaluates due experiments against a baseline and may register a new experiment before exiting. Plans require metrics, deadlines, stop conditions and rollback paths; unchanged observations should not produce fake logs. Use a sandbox and human review before commands, installations, scheduling or persistent instruction, memory or code edits. Automation stays disabled until a reviewed schedule is approved.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 3.6K | — | 3 |
| 2026-08 | 3.0K | +6.4% | 3 |
| 2026-07 | 2.8K | +14.7% | 3 |
| 2026-06 | 2.5K | +10.5% | 1 |
| 2026-05 | 2.2K | — | 1 |
| 2026-03 | 1.2K | — | 1 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: self-evolve-agent description: | Agent 自主进化引擎——让 AI agent 像生物进化一样持续变强。 核心循环:感知差距 → 搜索方案 → 设计实验 → 跑实验 → 选赢家 → 固化 → 下一轮。 使用场景:定期自主进化、能力升级、工作流优化、skill/工具迭代。 关键词:进化, evolution, self-improve, A/B test, 实验。 --- # Self-Evolve — Agent 自主进化引擎
> 生物进化的核心机制:变异 → 选择 → 保留。 > 这个 skill 把同样的机制应用到 agent 自身——不断发现可改进的角度,用实验找到更好的方案,然后永久固化。 ## 这是什么 Self-Evolve 是一个 **meta-skill**——它不解决具体业务问题,而是让 agent 自身变强。 * **和 `self-think` 的边界**:`self-think` 进行反思并发现短板(诊断),`self-evolve` 对短板寻找解法并跑实验验证(治疗)。 * **不是什么**:禁止无目标的空转、禁止为写长篇自省报告而改排版的“伪进化”。(详细约束见: [约束与红线](references/constraints-and-rules.md)) ## 快速使用说明 (Quick Start) ### 触发方式 当被以下情况触发时,请启动 Self-Evolve: 1. **自动触发**:`self-think` 建议进化,或是 cron/HEARTBEAT 定时唤醒。 2. **显式要求**:用户下令“进化一下某个能力”。 ### 执行协议 (The 4-Step Tick) 每次触发此技能,你必须执行一次且仅执行一次“滴答巡航”(Tick Handler),绝不能无视状态机一次性跑完实验全流程。 完整并发状态机制及操作边界,请细读: [执行状态机与四步巡航协议](references/execution-protocol.md) 1. **Step 1: Status Sync** - 扫描 `state.json` 的活跃实验。 2. **Step 2: Record Observations** - 将观察指标降噪后写入相应的 `jsonl` 记录。 3. **Step 3: Evaluate & Solidify** - 将到期限对比基线固化,并清理任务队列。 4. **Step 4: Launch New Experiment** - 如并发额度允许(<10),则寻找新瓶颈,搜索方案,设计实验并在状态机注册 `OBSERVING`。部署要求参考 [模板与输出规范](assets/evolution-report-template.md)。 > 🚨 **注意**:部署新实验并更新状态机锁后,**本轮巡航彻底结束,必须强制停止(Exit对话)**,等待下一次唤醒! ## 参考结构指南 你需要严格遵照以下文件来展开进化,**严禁凭感觉行事**: * [执行状态机与四步巡航协议](references/execution-protocol.md) - How to run a tick (Step 1 to Step 4). * [约束红线与分级自主权](references/constraints-and-rules.md) - What is NOT allowed (Anti-Pseudo Constraint). * [质量自检 Checklist](references/quality-checklist.md) - Validation for the experimental design and solidification. * [模板与文件规范](assets/evolution-report-template.md) - Data storage paths, YAML format, markdown templates.
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.