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daily-reflection日常反思

Agent Skill

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。

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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。

GitHub

来源数

2

许可证

MIT-0

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: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

简介

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。

SKILL.md

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

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