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。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 130 | 3991 | 8 | - |
| SkillHub ↗ | 129 | 4255 | 8 | — |
各平台的計數口徑不同,統計時間不一致。
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
以下是根據技能文件生成的示例,不代表實際執行結果。
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 |
|---|---|---|---|
| 2026-09 | 4255 | — | 8 |
| 2026-08 | 3880 | +3.9% | 8 |
| 2026-07 | 3734 | +9.2% | 8 |
| 2026-06 | 3418 | +6.0% | 8 |
| 2026-05 | 3224 | — | 7 |
| 2026-03 | 1782 | — | 4 |
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。
---
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`
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在保留公式、格式和活頁簿結構的前提下建立、檢查或編輯 Excel 檔案。
處理 Word 與 DOCX 文件的作業指南,著重維持樣式、編號與版面。
把資料庫、表格與匯出的原始資料變成可決策結論的資料分析技能。
協助編輯 PowerPoint 簡報,並在交付前檢查版面是否正常呈現。
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。