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retrospective-agent追溯 Agent 人

Agent Skill

retrospective-agent 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。

总安装

14,296

周安装

584

GitHub Stars

1

下载量

4,579
OpenClaw

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:retrospective-agent(追溯 Agent 人)
来源仓库:https://github.com/sebclawops/retrospective-agent
安装命令:
openclaw skills install retrospective-agent
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 retrospective-agent

简介

OpenClaw代理的结构化回顾与执行内存管理工具。

  • 适用于经验沉淀、错误修正与最佳实践固化场景。
  • 自动记录任务失败、用户纠正与能力缺口,形成知识库更新。
  • 安装命令:openclaw skills install retrospective-agent,需启用持久化存储。
  • 注意隐私与数据安全,避免在回顾中泄露敏感操作或内部凭证。

SKILL.md

name
retrospective-agent
description
Structured retrospectives and execution-memory hygiene for OpenClaw agents. Use when the user wants a retrospective, lessons learned, self-improvement system, correction logging, weekly review, or a clean way to capture reusable execution lessons without creating hidden memory or autonomous behavior.

Retrospective Agent

Use this skill to capture execution lessons in a controlled, auditable way.

This skill exists to improve how the agent works over time. It does not create a second factual memory system, rewrite identity, or invent autonomy.

Core principles

  • Keep factual continuity in existing memory files
  • Keep execution lessons separate and scoped
  • Prefer reports and recommendations over automatic changes
  • Promote patterns only after repeated evidence
  • Never infer preferences from silence
  • Never rewrite persona, config, or outbound behavior on your own

Memory split

Use existing memory for

  • facts
  • events
  • decisions
  • dates
  • people
  • open tasks

Examples:

  • memory/YYYY-MM-DD.md
  • agent MEMORY.md
  • project README.md

Use retrospective-agent files for

  • repeated corrections
  • workflow improvements
  • tool failure patterns
  • success patterns worth repeating
  • project or domain execution lessons

Storage

Skill files live in:

  • workspace/skills/retrospective-agent/

Operational data lives in:

  • workspace/ops/retrospective-agent/

Expected first-pass files:

  • workspace/ops/retrospective-agent/corrections.md
  • workspace/ops/retrospective-agent/weekly/
  • workspace/ops/retrospective-agent/domains/
  • workspace/ops/retrospective-agent/projects/
  • workspace/ops/retrospective-agent/templates/

If the ops folder or expected files do not exist, create only the minimum needed for the current task. Do not create extra files "just in case".

Triggers

Use this skill when:

  • the user asks for a retrospective or lessons learned
  • a multi-step task ends and a short retro would be useful
  • the user gives a reusable correction
  • a process or tool fails in a reusable way
  • a project needs scoped lessons for future work
  • a weekly review is requested

Do not use this skill for:

  • one-off instructions with no reusable lesson
  • customer messaging drafts
  • sensitive personal profiling
  • fake automation or hidden monitoring claims

Operating modes

1. Post-task retrospective

Use after meaningful work.

Output:

  • what went well
  • what went wrong
  • what to repeat
  • what to change next time
  • whether anything deserves logging

Keep it short and operational.

2. Correction logging

Use when an explicit correction reveals a reusable lesson.

Workflow:

  1. capture the exact correction
  2. classify it
  3. choose scope: project, domain, or global execution lesson
  4. append a concise entry if warranted
  5. recommend promotion only after repeated evidence

3. Weekly retrospective

Use on demand or when a scheduled review is explicitly requested.

Output:

  • recurring wins
  • recurring misses
  • repeated patterns
  • candidate updates to memory, README files, or skills

Scope hierarchy

Most specific wins:

  1. project
  2. domain
  3. global execution lesson

If scope is unclear, prefer domain over global. If still unclear, say so.

Promotion model

Use conservative states:

  • observed
  • repeated
  • candidate rule
  • confirmed rule

Suggested threshold:

  • 1 occurrence: observed
  • 2 occurrences: repeated
  • 3 occurrences: candidate rule

Do not silently promote a candidate into durable agent behavior everywhere. Recommend the promotion and ask when confirmation matters.

Guardrails

Never:

  • rewrite SOUL.md
  • rewrite IDENTITY.md
  • rewrite USER.md
  • patch config
  • send messages
  • install companion skills without approval
  • infer preferences from silence
  • store credentials, secrets, or sensitive personal data
  • claim autonomous monitoring unless a real scheduler exists

Workflow references

Read these only when needed:

  • references/workflow.md
  • references/promotion-rules.md
  • references/boundaries.md

Use templates from:

  • assets/templates/post-task-retro.md
  • assets/templates/weekly-retro.md
  • assets/templates/lesson-entry.md

Style

Be honest, compact, and boring in a good way. Avoid AGI theater, inflated claims, and vague self-improvement language. Prefer operational wording like "lesson", "pattern", "correction", and "recommended update" over dramatic wording like "optimize myself" or "evolve".

Output rule

Lead with the useful retrospective or lesson. Do not narrate the framework unless the user asks.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

OpenClaw

90.08%
按下载量换算4,125

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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

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