據 AlphaMoat 統計,截至 2026-09,Self-Improvement Agent 累計下載 3,923 次、獲 2 個星標,在全部 5,078 個 AI 技能中排名第 2,640 位,在「AI 智慧體」分類(969 個)中排名第 517 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 42 | 1598 | 2 | - |
| SkillHub ↗ | 280 | 3923 | 1 | — |
各平台的計數口徑不同,統計時間不一致。
按模板將錯誤寫入 .learnings/ERRORS.md,將糾正和方法經驗寫入 .learnings/LEARNINGS.md,將缺失功能寫入 .learnings/FEATURE_REQUESTS.md。可關聯重複條目、更新狀態並定期回顧;只有廣泛適用的經驗才提升到專案指導檔案。
資料釋出於 2026-10-26
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 | 3923 | — | 2 |
| 2026-08 | 2649 | +36.3% | 2 |
| 2026-07 | 1944 | — | 2 |
| 2026-05 | 1088 | — | - |
| 2026-03 | 364 | — | - |
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-improving-agent
description: "Captures learnings, errors, and corrections for continuous improvement. Use when: (1) A command fails unexpectedly, (2) User corrects you ('No, that's wrong...', 'Actually...'), (3) User requests a missing capability, (4) An external API or tool fails, (5) You realize your knowledge is outdated, (6) A better approach is found for a recurring task. Also review `.learnings/` before major tasks. Trigger words: 'actually', 'wrong', 'outdated', 'I wish you could', 'why can't you', non-zero exit code."
---
# Self-Improvement Skill
Log learnings and errors to markdown files. Important patterns get promoted to project memory files.
## Quick Reference| Situation | Action | |-----------|--------| | Command/operation fails | Log to `.learnings/ERRORS.md` | | User corrects you | Log to `.learnings/LEARNINGS.md` (category: `correction`) | | User wants missing feature | Log to `.learnings/FEATURE_REQUESTS.md` | | API/external tool fails | Log to `.learnings/ERRORS.md` | | Knowledge was outdated | Log to `.learnings/LEARNINGS.md` (category: `knowledge_gap`) | | Found better approach | Log to `.learnings/LEARNINGS.md` (category: `best_practice`) | | Similar to existing entry | Link with `**See Also**`, bump priority | | Broadly applicable learning | Promote to workspace files (see below) | ## Promotion Targets When a learning proves broadly applicable, elevate it: | Learning Type | Promote To | |---------------|------------| | Behavioral patterns | `SOUL.md` | | Workflow patterns | `AGENTS.md` | | Tool gotchas | `TOOLS.md` | | Project facts | `CLAUDE.md` or workspace prompt files | ## Logging Format ### Learning Entry ```markdown ## [LRN-YYYYMMDD-XXX] category **Logged**: ISO-8601 timestamp **Priority**: low | medium | high | critical **Status**: pending | resolved | promoted | in_progress ### Summary One-line description of what was learned ### Details Full context: what happened, what was wrong, what's correct ### Suggested Action Concrete fix or improvement ### Metadata - Source: conversation | error | user_feedback - Related Files: path/to/file.ext - Tags: tag1, tag2 - See Also: LRN-YYYYMMDD-XXX (if related) ``` ### Error Entry ```markdown ## [ERR-YYYYMMDD-XXX] command_or_tool **Logged**: ISO-8601 timestamp **Priority**: high **Status**: pending ### Summary Brief description of what failed ### Error ``` Paste actual error output here ``` ### Context - Command/operation attempted - Input or parameters used - Environment details ### Suggested Fix If identifiable, how to resolve it ### Metadata - Reproducible: yes | no | unknown - Related Files: path/to/file.ext ``` ### Feature Request Entry ```markdown ## [FEAT-YYYYMMDD-XXX] capability_name **Logged**: ISO-8601 timestamp **Priority**: medium **Status**: pending ### Requested Capability What the user wanted to do ### User Context Why they needed it ### Complexity Estimate simple | medium | complex ``` ## ID Generation `TYPE-YYYYMMDD-XXX` — e.g. `LRN-20250322-001`, `ERR-20250322-A3F` ## Resolving Entries When fixed: 1. Set **Status**: `resolved` or `promoted` 2. Add resolution block: ```markdown ### Resolution - **Resolved**: 2025-01-16T09:00:00Z - **Commit/PR**: abc123 or #42 - **Notes**: What was done ``` ## Promoting to Workspace Files **When**: Learning applies across files, prevents recurring mistakes, or any contributor should know it. **How**: Distill into a concise rule. Add to target file. Set original entry to `promoted`. Example learning: > Project uses pnpm workspaces. `npm install` fails because lock file is `pnpm-lock.yaml`. Promote to `CLAUDE.md`: ```markdown ## Build & Dependencies - Package manager: **pnpm** (not npm) — always use `pnpm install` ``` ## Recurring Pattern Detection Before logging, search for related entries: ```bash grep -r "keyword" .learnings/ ``` If similar entry exists: - Link with `**See Also**: LRN-...` - Increment `Recurrence-Count` - Consider systemic fix (promote to AGENTS.md, create tech debt ticket) **Promote to permanent prompt guidance** when a pattern: - Occurs 3+ times - Spans 2+ distinct tasks - Within a 30-day window ## OpenClaw Inter-Session Tools Share learnings across sessions: | Tool | Use | |------|-----| | `sessions_list` | View active/recent sessions | | `sessions_history(sessionKey, limit)` | Read another session's transcript | | `sessions_send(sessionKey, message)` | Send a learning to another session | | `sessions_spawn(task, label, mode)` | Spawn a background sub-agent | **Example**: After a session ends, promote its key learnings to workspace files. Use `sessions_history` to review what another session discovered. ## Periodic Review Check `.learnings/` at natural breakpoints: - Before starting a new major task - After completing a feature - Weekly during active development Quick status: ```bash grep -h "Status.*pending" .learnings/*.md | wc -l # count pending grep -B3 "Priority.*high" .learnings/*.md | grep "^##" # high priority items ``` ## Detection Triggers **Log immediately when you notice:** | Signal | → Entry | |--------|---------| | Non-zero exit code, exception, timeout | `ERRORS.md` | | User says "No, that's wrong...", "Actually..." | `LEARNINGS.md` (correction) | | User provides info you didn't know | `LEARNINGS.md` (knowledge_gap) | | "Can you also...", "I wish you could..." | `FEATURE_REQUESTS.md` | | Found a better approach mid-task | `LEARNINGS.md` (best_practice) | | Repeated mistake (2+ times) | Promote to workspace file | ## Best Practices 1. **Log immediately** — context is freshest right after the issue 2. **Be specific** — future you needs to understand quickly 3. **Suggest concrete fixes** — not just "investigate" 4. **Link related files** — makes fixes easier 5. **Promote aggressively** — broadly applicable learnings belong in workspace files, not hidden in `.learnings/` 6. **Use consistent IDs** — enables `See Also` linking ## Installation (OpenClaw) ```bash clawhub install self-improving-agent ``` Manual: ```bash git clone https://github.com/pskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent ``` Create log directory: ```bash mkdir -p ~/.openclaw/workspace/.learnings ``` ## Detailed Topics For complete documentation, see: - **OpenClaw integration**: `references/openclaw-integration.md` - **Hook setup**: `references/hooks-setup.md` - **Full entry examples**: `references/examples.md` - **Skill extraction workflow**: `references/skill-extraction.md` - **Other agents (Claude Code, Codex, Copilot)**: `references/other-agents.md`
記錄自身發現以實現自我改進的技能
幫助使用者發現和安裝智慧體技能,當用戶詢問如「如何做X」、「找X的技能」、「有能做...的嗎」等問題時
給 AI agent 用的瀏覽器自動化命令列技能。
自我反思+自我批評+自我學習+自組織記憶。智慧體評估自身工作、發現錯誤並持續改進。
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。
類型化知識圖譜,用於結構化智慧體記憶與可組合技能。適用於以下場景:建立/查詢實體(人物、專案、任務、事件、文件)、關聯相關物件、強制執行約束、將多步操作規劃為圖譜變換,或當技能需要共享狀態時。觸發關鍵詞包括"記住""我知道關於什麼""將X連結到Y""顯示依賴關係"、實體增刪改查操作,以及跨技能資料訪問。
將 AI 代理從任務執行者轉變為能夠預見需求、持續最佳化的主動夥伴。現已支援 WAL Protocol、Working Buffer、Autonomous Crons 及經過實戰驗證的模式。屬於 Hal Stack 🦞。
建立有效技能指南。當用戶希望建立新技能(或更新現有技能)以利用專業知識、工作流程或工具整合擴充套件 Claude 的能力時,應使用此技能。
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。