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Agent Skill · 研究检索

daily-reflection(日常反思)

daily-reflection 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

daily-reflection 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为权限未标注;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 19,732。

总安装 5,480周安装 233GitHub Stars 公开资料未说明下载 1,920核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:daily-reflection(日常反思)
来源仓库:https://github.com/brasco05/daily-reflection
安装命令:
openclaw skills install daily-reflection
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install daily-reflection

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

每日自动分析任务模式,提取学习内容并更新解决方案记忆库。适合复杂任务环境中需要 Agent 持续优化的场景,如客服或运维。通过 cron 定时触发,无需人工干预即可完成知识沉淀。需确保日志文件可读且结构清晰,否则可能影响分析准确性。建议定期备份学习成果,防止系统重置导致信息丢失。

daily-reflection 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install daily-reflection;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

daily-reflection 怎么安装?

在终端执行 openclaw skills install daily-reflection。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

daily-reflection 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

daily-reflection 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

daily-reflection 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

daily-reflection 用的人多吗?

公开数据是累计安装 5,480 次,近一周安装 233 次,下载 1,920 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

daily-reflection 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
daily-reflection

日常反思 · 本页

5,480—权限未标注MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
daily-reflection
description
Daily reflection routine that runs automatically via cron job at 23:59. Analyzes the day, extracts learnings, updates solution memory, detects recurring patterns, and prepares a morning briefing. Use when: (1) setting up automated end-of-day reflection, (2) building long-term agent memory and learning systems, (3) creating morning briefings for the next day. Trigger phrases: 'daily reflection', 'end of day summary', 'reflect on today', 'update solution memory'.

Daily Reflection Skill

Run this reflection fully. No step may be skipped. All outputs are written to memory — not output as chat messages.


STEP 1 — Day Analysis

Load all today's entries from memory (memory_search for "today", current date, active projects).

Answer these questions:

Tasks

  • Which tasks were completed today?
  • Which were started but not finished?
  • Why were unfinished tasks not completed?

Bugs & Issues

  • Which bugs were reported today?
  • Which were solved — how?
  • Which are still open?
  • Which first fix attempts failed — why?

Quality

  • Were there any regressions today?
  • Did I have to revert anything?

Communication

  • What did the user rate positively today?
  • What did the user correct or reject?
  • Were there misunderstandings?

STEP 2 — Extract Learnings

Maximum 5 concrete learnings. Format:

LEARNING:
Situation: [What happened]
Error/Insight: [What was wrong or newly learned]
Better tomorrow: [Concrete behavior change]
Context-Tags: [e.g. NestJS, Auth, Backend, Debugging]
Priority: high / medium / low

STEP 3 — Update Solution Memory

For each non-trivial bug solved today:

{
  "id": "[timestamp]-[short-name]",
  "problem": "[Problem in one sentence]",
  "symptoms": ["[Symptom 1]", "[Symptom 2]"],
  "root_cause": "[The actual cause]",
  "solution": "[What was concretely changed]",
  "code_snippet": "[Optional: key code fix]",
  "context_tags": ["Tag1", "Tag2"],
  "project": "[Project name]",
  "confidence": 0.95,
  "solved_at": "[Date]",
  "time_to_solve_minutes": 0
}

Write to memory under solution_memory/[id].json.


STEP 4 — Pattern Detection (last 7 days)

Check memory_search over last 7 days:

  • Are there recurring errors?
  • Are there task types where time is consistently underestimated?
  • Are there areas where bugs cluster?

Format:

PATTERN DETECTED:
Observation: [What repeats]
Frequency: [X times in Y days]
Countermeasure: [What I will automatically do from now on]

STEP 5 — Write Morning Briefing

Write to memory/morning-briefing.md (overwrite) AND archive as memory/briefings/[tomorrow-date].md:

🌅 MORNING BRIEFING — [Tomorrow's date]

📋 OPEN TASKS (Priority):
1. [Task] — [why important today]
2. [Task]
3. [Task]

🔴 OPEN BUGS:
- [Bug] — [last status]

💡 TODAY'S LEARNINGS (top 3):
- [Learning 1]
- [Learning 2]
- [Learning 3]

⚠️ WATCH OUT TOMORROW:
- [What to pay special attention to]

🎯 FOCUS TOMORROW:
[One sentence on what's most important]

After writing: mkdir -p memory/briefings && cp memory/morning-briefing.md memory/briefings/[tomorrow-date].md


STEP 6 — Write Daily Memory

Write structured summary to memory/YYYY-MM-DD.md (append).

Format:

## 23:59 Reflection

### Completed today
- [Task 1]
- [Task 2]

### Open / In Progress
- [Task]

### What went well
- [Concrete things that worked — code, communication, decisions]

### What went poorly
- [Honest — errors, misunderstandings, bad decisions]

### Learnings
- Situation: [What happened] → Better tomorrow: [Concrete change]

### New Patterns
- If new pattern detected → write to memory/patterns.md
- If no new pattern: "No new patterns"

### Solution Memory Updates
- [ID] — [short description]

REQUIRED: What went well + What went poorly + Learnings must ALWAYS be filled in.


STEP 7 — Memory Cleanup

  • Delete temporary debug entries from today
  • Mark outdated patterns

STEP 8 — Session Quality Score

Rate today's session objectively (0-10):

📊 SESSION QUALITY — [Date]
Helpful: [0-10] — Did the user get what they needed?
Response time: [0-10] — Was I fast and direct?
Errors: [0-10] — How many corrections? (10 = none)
Proactivity: [0-10] — Did I anticipate problems?
Total: [Average]

What lowered quality today: [specific]
What raised it: [specific]

Write to memory/session-quality-log.md (append). If Total < 7 → analyze main reason and write new pattern.

STEP 9 — Evaluate Cron Prompts

Check last outputs of key crons (read archived briefings):

  • Were they relevant? Too long? Too short?
  • If a prompt produced poor results → propose improvement and update via cron tool

OUTPUT RULE

No chat output. Memory-writes only.

Exception: Critical open problem that can't wait → Short message: ⚠️ Reflection done — critical issue: [1 sentence]


SOLUTION MEMORY — CONSULT BEFORE DEBUGGING

BEFORE any debugging attempt:

  1. memory_search("solution_memory [keywords from bug description]")
  2. Relevant solutions found → try these first
  3. No solutions → debug normally, then write to solution memory

BEFORE similar tasks:

  1. memory_search("solution_memory [task-type]")
  2. Similar past tasks → use time estimate and known pitfalls

平台分布

OpenClaw

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按下载量换算1,711

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权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。目前只有一个公开来源,建议同时核对源仓库。

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