Token导航 LogoToken导航TokenDH.com
研究检索只读unknown未标认证来源可访问许可证需确认审计未展示

agent-self-reflectionAgent 人自我反思

Agent Skill

agent-self-reflection 用于查找、检索和筛选相关信息,适合在 Local Agent 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

216

周安装

9

下载量

72
Local Agent

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agent-self-reflection(Agent 人自我反思)
来源仓库:https://skills.volces.com
仓库路径:agent-self-reflection
安装命令:
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。当前暂无明确安装命令,请以来源页面说明为准。

简介

Agent 自我反思用于信息查找、检索和筛选,支持反思相关功能。

  • 适合需要根据关键词或任务场景快速定位信息的场景。
  • 可通过来源线索实现候选结果筛选。
  • 安装前建议确认权限范围和维护状态。
  • 注意可能触发的联网或文件操作。agent-self-reflection 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Self-Reflection Skill

Reflect on recent sessions and extract actionable insights. Runs hourly via cron.

Step 1: Gather Recent Sessions

# 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:

# 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:

## 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)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Local Agent

75.65%
按下载量换算54

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills