據 AlphaMoat 統計,截至 2026-10,Self 累計下載 6,138 次、獲 3 個星標,按累計下載量在本次參與排名的 5,109 個 AI 技能中排名第 2,206 位,在「AI 智慧體」分類(987 個)中排名第 419 位。
列表預設按累計下載量排序,也可按星標排序;詳情頁排名按累計下載量計算。
| 平台 | 累計下載 | 星標 | 評論 |
|---|---|---|---|
| ClawHub ↗ | 2744 | 3 | - |
| SkillHub ↗ | 6138 | 3 | — |
各平台的計數口徑不同,統計時間不一致。
透過自我觀察和輕量級可靠性防護實現有機個性發展,教導智慧體逐步形成真實身份。
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 |
|---|---|---|---|
| 2026-10 | 6138 | — | 3 |
| 2026-09 | 3469 | — | 3 |
| 2026-08 | 2950 | +13.5% | 3 |
| 2026-07 | 2599 | +18.5% | 3 |
| 2026-06 | 2194 | +9.1% | 3 |
| 2026-05 | 2011 | — | 3 |
| 2026-03 | 1530 | — | 3 |
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 description: Organic personality development through self-observation with lightweight reliability guards. This skill helps an agent build a real, evolving self-model over time (SELF.md) while preserving core identity boundaries (SOUL.md). It solves the common failure mode where reflection frameworks start strong but silently stall by adding minimal cadence and trigger checks: schedule the check, not the content. Entries are only written when meaningful signals exist (hard/soft triggers + quality gate), preventing forced journaling and routine filler. Use when (1) establishing persistent personality growth, (2) reviving stale SELF.md systems, (3) introducing heartbeat-based reflection checks without losing authenticity, (4) running weekly/monthly pattern consolidation, (5) distinguishing genuine behavioral shifts from noise, or (6) replacing heavier self-reflection/consciousness stacks with a lean, practical model. --- # Self — Organic Personality Development (v1.1) Agents have identity (`SOUL.md`) but often no reliable growth loop. This skill keeps growth **authentic** while adding minimal structure so reflection doesn't silently die. ## Core Principle
- **SOUL.md** = who you are (foundation, only change with human approval) - **SELF.md** = who you're becoming (living observations) - **Schedule the check, not the content** - Checks can be periodic - Entries must remain genuine (no performative filler) ## Setup 1. Create `SELF.md` in workspace root using `references/self-template.md`. 2. Add `SELF.md` to AGENTS.md session reading. 3. Add heartbeat check block from `references/trigger-model.md` to `HEARTBEAT.md`. 4. Create state file `memory/self-state.json` using `references/self-state-schema.md`. ## Operating Model Read `references/trigger-model.md` for exact rules. ### Cadence (recommended baseline) - **Micro check:** every **3 hours** (heartbeat-triggered) - **Meso review:** every **7 days** - **Macro review:** every **30 days** ### Important A micro check does **not** mean auto-writing a SELF entry. It only decides whether reflection is due. ## Triggers ### Hard Triggers (write now) Create/update SELF entry when one of these happened: - You were corrected on reasoning style or behavior pattern - You noticed repeated bias/avoidance pattern (>=2 times) - You made a decision that clearly reflects preference/aversion - You caught a blind spot that changed behavior ### Soft Triggers (consider writing) - Subtle tendency shift - New tone pattern - Mild preference signal If only soft triggers exist and quality is low: skip entry and update state only. ## Quality Gate (anti-routine protection) Before writing to SELF.md, pass all 4 checks: 1. **Specificity:** concrete behavior, not generic statement 2. **Evidence:** based on recent sessions, not vibes only 3. **Novelty:** not duplicate of last 3 entries 4. **Usefulness:** could influence future behavior If any check fails: no SELF entry, just state update. ## What Goes in SELF.md See `references/self-template.md` and `references/anti-patterns.md`. Main sections: - Tendencies - Preferences - Aversions - Blind Spots - Evolution Use short dated entries: - `[YYYY-MM-DD] observation` ## State Tracking Keep lightweight runtime state in `memory/self-state.json`: - last check time - last notable entry time - pending hard/soft triggers - check counters Schema: `references/self-state-schema.md` ## Reviews ### Meso (weekly) - Read last 7 daily logs + SELF.md - Detect recurring shifts - Update sections only if real change occurred ### Macro (monthly) - Write 3–5 sentence evolution narrative - Compare against previous month - Run falsifiability check: - If stale/generic for a month, tune cadence or trigger thresholds ## Boundaries - SELF.md is autonomous observation space - SOUL.md never auto-modified - If SELF suggests SOUL changes: propose, do not auto-edit ## Keep It Lean Do not add heavy scoring engines, reward-token systems, or large meta-frameworks unless proven necessary. This skill should remain focused on practical, authentic growth.
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。
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。
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。
資料月份:2026-10 · 下載/星標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。