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Agent Skill · 研究检索

retrospective-artifacts(回顾性文物)

retrospective-artifacts 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

retrospective-artifacts 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 70,834。

总安装 198周安装 8GitHub Stars 19下载 62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
unknown

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-artifacts(回顾性文物)
来源仓库:https://github.com/canonical/copilot-collections
仓库路径:skills/retrospective-artifacts
安装命令:
npx skills add https://github.com/canonical/copilot-collections --skill retrospective-artifacts
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/canonical/copilot-collections --skill retrospective-artifacts

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。

用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装使用。安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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

来源仓库内路径:skills/retrospective-artifacts

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/canonical/copilot-collections --skill retrospe…;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

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

retrospective-artifacts 怎么安装?

在终端执行 npx skills add https://github.com/canonical/copilot-collections --skill retrospective-arti…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

retrospective-artifacts 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

retrospective-artifacts 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

retrospective-artifacts 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

retrospective-artifacts 用的人多吗?

公开数据是累计安装 198 次,近一周安装 8 次,下载 62 次,GitHub 19 星。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。

retrospective-artifacts 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 Codex,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。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
retrospective-artifacts

回顾性文物 · 本页

19819调用外部服务未明确有提醒

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

Retrospective Artifacts

Overview

Create durable, queryable retrospective artifacts under .retrospectives/ and provide deterministic retrieval of key learnings from prior artifacts.

This skill has two modes:

  • CREATE mode: interview, gather context, and build a standardized retrospective folder.
  • PARSE mode: read existing retrospective folders and return only the requested facts.

Routing

Use CREATE mode when the request asks to:

  • run a retrospective
  • capture session learnings
  • build a retro artifact folder
  • summarize current work and preserve context

Use PARSE mode when the request asks to:

  • query previous retrospectives
  • extract specific learnings/code snippets/logs
  • shortlist okb_worthy items
  • find sessions needing context asset improvement

Workflow 1: CREATE mode

Follow these steps in order.

Step 1: Context triage

  1. Inspect current conversation and workspace context.
  2. Identify missing facts: catalyst, impact, investigation, resolution, and downstream signals.
  3. Use references/context-intake-checklist.md to drive coverage.

Step 2: Progressive interview

Ask 1-3 targeted questions to fill missing gaps. Always request relevant external links if available:

  • GitHub issues/PRs/Actions logs
  • Jira tickets
  • Mattermost threads
  • external incident docs

Before extraction, require a focus directive from the user to control what gets exported from large contexts. Accept examples like:

  • "only moments where Copilot iterated 3+ times"
  • "only failed attempts and workarounds"
  • "only final solutions and why they worked"
  • "exclude routine successful steps"

Do not proceed to generation until the required minimum context is present.

Step 3: External context acquisition

For each provided link:

  1. Prefer MCP integrations first (GitHub/Jira/Mattermost MCP servers when available).
  2. If MCP is unavailable for a source, use fallback scripts in scripts/.
  3. If retrieval fails or access is denied, state that clearly and request pasted text.
  4. Do not infer missing external content.

By default, perform bounded recursive gathering:

  • discover first-level referenced URLs in fetched content
  • acquire relevant linked artifacts (GitHub/Jira/Mattermost/docs) when they add incident value
  • stop at one recursive level unless user explicitly asks for deeper traversal
  • respect focus directive to avoid low-signal expansion

Use references/context-intake-checklist.md and references/external-context-acquisition.md as mandatory guidance.

Step 3.1: Markdown summary quality gate

Any acquired context saved as markdown must include an explicit, high-signal summary section near the top.

Minimum requirement:

  • ## Summary

Summary quality expectations:

  • 3-7 concise bullets or short paragraphs
  • include what changed, why it mattered, and key technical signals
  • prioritize incident-relevant facts over low-signal detail

When using MCP for retrieval, enforce this summary section before finalizing the artifact.

Step 4: Artifact generation

Create a timestamped folder under: .retrospectives/YYYY-MM-DD_short-session-name/

Expected structure:

  • retro-summary.md
  • context/ (external threads/docs)
  • logs/ (errors, traces, diagnostics)
  • code-snippets/ (before/after or key snippets)

Use references/retro-summary-template.md as the required output schema. Ensure the generated summary clearly states the selected focus directive and what was intentionally excluded.

Step 5: Quality gate

Before finalizing:

  1. Validate frontmatter is complete and truthful.
  2. Ensure all referenced relative paths exist.
  3. Set downstream flags (okb_worthy, context_asset_improvement_needed) conservatively from evidence.
  4. Never fabricate snippets, logs, or external thread content.

Workflow 2: PARSE mode

When asked to retrieve past information:

  1. Locate matching folders in .retrospectives/.
  2. Read retro-summary.md first.
  3. Use references/parse-query-patterns.md to map request type to extraction strategy.
  4. If requested data points to relative files (for example in context/, logs/, code-snippets/), open only those files.
  5. Return only requested information with concise synthesis.

Do not dump full directories unless explicitly requested.

Output contract

  • For CREATE mode, output:

- artifact path - generated file index - concise summary of captured catalyst, resolution, and downstream signals

  • For PARSE mode, output:

- direct answer to query - supporting artifact path(s) - confidence caveat when data is missing or inaccessible

Constraints

  • Anti-hallucination: never invent context, logs, or links.
  • Path discipline: use explicit relative paths inside artifacts.
  • Evidence-first tagging: set metadata flags from observable evidence.
  • Non-destructive behavior: read/summarize existing retros by default.
  • Integration priority: MCP servers first, local scripts second.
  • Fallback scripts must fail loudly with explicit setup instructions when dependencies/tokens are missing.

References

  • references/context-intake-checklist.md
  • references/retro-summary-template.md
  • references/parse-query-patterns.md
  • references/external-context-acquisition.md

Manual fallback handoff

When acquisition fails after MCP + script fallback, use the baked-in handoff format in references/external-context-acquisition.md and request user-provided raw context.

Script fallback quick reference

Only use these scripts when MCP is unavailable for the source.

  • GitHub URL fetch:

- python3 scripts/fetch_github_context.py --url "<github-url>" --output "<artifact>/context/github-*.md"

  • Jira issue fetch:

- python3 scripts/fetch_jira_context.py --url "<jira-url>" --output "<artifact>/context/jira-*.md"

  • Mattermost thread fetch:

- python3 scripts/fetch_mattermost_thread.py --thread "<mm-thread-url-or-id>" --output "<artifact>/context/mm-*.md"

平台分布

Codex

36.89%
按下载量换算23

Claude

29.42%
按下载量换算18

Cursor

18.72%
按下载量换算12

Gemini CLI

9.33%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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