據 AlphaMoat 統計,截至 2026-09,Self Reflection 累計下載 3,405 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 3,289 位,在「生活服務」分類(246 個)中排名第 159 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 101 | 3088 | - | - |
| SkillHub ↗ | 97 | 3405 | - | — |
各平台的計數口徑不同,統計時間不一致。
流程會讀取會話記錄片段並可能持續寫入高信任檔案;會話內容可能私密,提煉出的內容也可能不準確或包含敏感資訊。應限制會話範圍、擷取必要片段、進行脫敏,並在寫入 AGENTS.md、TOOLS.md、MEMORY.md 或技能檔案前由負責人稽核批准。
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。 該技能有同名的其他版本,調整前的資料歸屬當前下載量最大的這一個。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 3405 | — | - |
| 2026-08 | 2966 | +5.1% | - |
| 2026-07 | 2821 | — | - |
| 2026-05 | 2315 | — | - |
| 2026-03 | 1471 | — | - |
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-reflection slug: agent-self-reflection version: 1.0.0 description: Periodic self-reflection on recent sessions. Analyzes what went well, what went wrong, and writes concise, actionable insights to the appropriate workspace files. Designed to run as a cron job. --- # Self-Reflection Skill Reflect on recent sessions and extract actionable insights. Runs hourly via cron.
## Step 1: Gather Recent Sessions
```bash
# List sessions active in the last 2 hours
openclaw sessions --active 120 --json
```
Parse the output to get session keys and IDs. Skip subagent sessions (they're task workers, not interesting for reflection). Focus on:
- Telegram group/topic sessions (real user interactions)
- Direct sessions (1:1 with Brenner)
- Cron-triggered sessions (how did automated tasks go?)
## Step 2: Read Session History
For each interesting session from Step 1, read the JSONL transcript:
```bash
# Read the last ~50 lines of each session file (keep it bounded!)
tail -50 ~/.openclaw/agents/main/sessions/<sessionId>.jsonl
```
**⚠️ CRITICAL: Never load full session files. Use `tail -50` or `Read` with offset/limit. Sessions can be 100k+ tokens.**
Parse the JSONL to understand what happened. Look for:
- `type: "user"` or `type: "human"` — what was asked
- `type: "assistant"` — what you responded
- `type: "tool_use"` / `type: "tool_result"` — what tools were called and results
- Error patterns, retries, confusion
## Step 3: Analyze & Extract Insights
For each session, ask yourself:
### What went well?
- Tasks completed smoothly on first try
- Good tool usage patterns worth reinforcing
- Efficient approaches to remember
### What went wrong?
- Errors, retries, wrong approaches
- Misunderstandings of user intent
- Tools that didn't work as expected
- Context that was missing
### Lessons learned?
- "Next time, do X instead of Y"
- "Remember that Z works this way"
- "Tool A needs parameter B or it fails"
- "When user says X, they usually mean Y"
**Quality bar:** Each insight must be:
- **Specific** — not "be more careful" but "check if file exists before editing"
- **Actionable** — something future-you can directly apply
- **Non-obvious** — skip things any competent agent would know
- **New** — don't repeat insights already captured
## Step 4: Route Insights to the Right Files
Each insight belongs somewhere specific. Route them:
### → `AGENTS.md`
- Process improvements (how to handle sessions, memory, etc.)
- New conventions or workflow rules
- Safety lessons
### → `TOOLS.md`
- Tool-specific gotchas ("gog needs --json flag for parsing")
- Environment details (paths, configs, quirks)
- New tool patterns discovered
### → `memory/YYYY-MM-DD.md` (today's date)
- Session-specific context ("Brenner asked about X project")
- Temporary facts that matter today but not forever
- What happened today (events, decisions, requests)
### → `memory/about-user.md`
- New preferences discovered
- Communication style observations
- Project/interest updates
### → `skills/<skill-name>/SKILL.md`
- Improvements to specific skill instructions
- Bug fixes in skill workflows
- New parameters or approaches for a skill
### → `MEMORY.md`
- Updates to the memory index if new memory files are created
## Step 5: Write the Insights
For each insight, append or edit the appropriate file. Use the `Edit` tool for surgical changes to existing content. Use append (write to end) for daily memory files.
**Format for daily memory files:**
```markdown
## Self-Reflection — HH:MM ET
### Insights
- [source: session-key] Lesson learned here
- [source: session-key] Another insight
### Tool Notes
- Discovered: tool X needs Y configuration
### User Context
- Brenner mentioned interest in Z
```
## Step 6: Summary
After writing all insights, produce a brief summary of what you reflected on and what you wrote. This is your output — keep it to 2-4 sentences max.
If there's nothing interesting to reflect on (quiet period, only heartbeats), just say so. Don't manufacture insights.
## Quality Checklist
Before writing any insight:
- [ ] Is this actually new? (Check existing files first)
- [ ] Is this specific and actionable?
- [ ] Am I routing it to the right file?
- [ ] Am I keeping daily memory files concise (not dumping full transcripts)?
- [ ] Did I respect the token budget (no huge file reads)?
## Anti-Patterns (Don't Do These)
- ❌ Don't summarize every session — only extract *lessons*
- ❌ Don't read full JSONL files — tail/limit only
- ❌ Don't write vague insights ("improve response quality")
- ❌ Don't duplicate existing knowledge
- ❌ Don't create new files when appending to existing ones works
- ❌ Don't reflect on your own reflection sessions (skip cron:self-reflection sessions)
用 curl 直接查詢公開天氣服務,取得目前天氣與預報。
在區域網裡發現並控制 Sonos 音箱的命令列工具。
以六爻占卜方式作答的技能。
用本地 JSON 記錄飲水量與睡眠的技能。
在本地同步並查詢 CalDAV 日曆的命令列技能。
在 macOS 上透過 remindctl 管理 Apple 提醒事項的技能。
travel-cn 聚合去哪兒/攜程/飛豬等中國旅行平台的資料查詢,覆蓋機票、酒店、火車票、景點門票與行程規劃。
在終端裡搜尋與控制 Spotify 播放的技能。
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。