据 AlphaMoat 统计,截至 2026-09,Self Reflection 累计下载 3,405 次、获 0 个星标,在全部 5,078 个 AI 技能中排名第 3,289 位,在「生活服务」分类(246 个)中排名第 159 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 101 | 3,088 | - | - |
| SkillHub ↗ | 97 | 3,405 | - | — |
各平台的计数口径不同,统计时间不一致。
流程会读取会话记录片段并可能持续写入高信任文件;会话内容可能私密,提炼出的内容也可能不准确或包含敏感信息。应限制会话范围、截取必要片段、进行脱敏,并在写入 AGENTS.md、TOOLS.md、MEMORY.md 或技能文件前由负责人审核批准。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。 该技能有同名的其他版本,调整前的数据归属当前下载量最大的这一个。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,405 | — | - |
| 2026-08 | 2,966 | +5.1% | - |
| 2026-07 | 2,821 | — | - |
| 2026-05 | 2,315 | — | - |
| 2026-03 | 1,471 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 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)
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在局域网里发现并控制 Sonos 音箱的命令行工具。
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用本地 JSON 记录饮水量与睡眠的技能。
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数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。