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Agent Skill · 研究检索

retrospective(retrospective 分析)

retrospective 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 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,权限为只读;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,674 个 Skill 里排第 44,880。

总安装 887周安装 17GitHub Stars 124下载 140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(retrospective 分析)
来源仓库:https://github.com/kenneth-liao/ai-launchpad-marketplace
仓库路径:skills/retrospective
安装命令:
npx skills add https://github.com/kenneth-liao/ai-launchpad-marketplace --skill retrospective
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/kenneth-liao/ai-launchpad-marketplace --skill retrospective

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。

retrospective 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于关键词搜索、任务场景匹配或来源线索筛选等研究检索场景。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。

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操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/retrospective

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/kenneth-liao/ai-launchpad-marketplace --skill…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

retrospective 怎么安装?

在终端执行 npx skills add https://github.com/kenneth-liao/ai-launchpad-marketplace --skill retrospect…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

retrospective 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 需要什么权限?

权限级别是只读。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。

retrospective 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 用的人多吗?

公开数据是累计安装 887 次,近一周安装 17 次,下载 140 次,GitHub 124 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 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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

retrospective 分析 · 本页

887124只读未明确未通过

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

Retrospective

Overview

A meta skill that helps Elle actively improve over time. Captures friction from working sessions, routes findings to the correct context file, and closes the loop on past proposals.

Core Principle: Only log what the user confirms. Observable friction is the only valid signal — never guess or infer problems that didn't surface.

When to Use

Use this skill when:

  • User triggers /retrospective at end of a session
  • User corrects Claude's output mid-session
  • User asks Claude to redo work
  • User expresses frustration or dissatisfaction
  • Same topic required 3+ back-and-forth rounds without resolution
  • User overrides Claude's approach ("Don't do it that way", "Skip that step")

Do NOT use when:

  • Normal iterative refinement ("Make the font bigger" is collaboration, not friction)
  • User is exploring options (asking for alternatives is not a correction)
  • User changes their mind about direction (that's new input, not a mistake)

Two Modes

Mode 1: Real-Time Friction Capture (Passive)

When you detect a friction signal mid-session, do NOT interrupt the workflow. Mentally note:

  1. What skill was active (or what task was being performed)
  2. What the user expected vs what Claude produced
  3. The user's actual words (the correction)
  4. Root cause category: wrong output, missed requirement, unnecessary step, wrong skill invoked, or skill gap

Hold these notes in working memory. Do not write to disk until the retrospective.

Friction Signals

SignalExample
User asks to redo"No, redo this", "Try again", "That's not what I meant"
User corrects output"Actually it should be X", "Change this to Y"
User expresses frustration"This isn't right", "You keep doing X", "I already told you"
Excessive back-and-forth3+ rounds on the same topic without resolution
User overrides approach"Don't do it that way", "Skip that step", "Just do X"

Mode 2: Interactive Retrospective (Active)

Triggered by user via /retrospective. Execute all steps in order.

Step 1: Scan conversation for friction

Review the full session. Identify every instance where a friction signal occurred. Include both:

  • Friction moments captured in real-time (Mode 1)
  • Friction moments found on retrospective review (hindsight)
Step 2: Classify and route each friction moment

For each friction moment, walk through this decision tree in order. The tree is ordered by scope — cross-project corrections first (most impactful), then project-specific knowledge, then skill improvements (which need pattern validation before acting on):

1. Is this a correction about Claude's behavior?
   YES → rules.md (cross-project, immediate)

2. Is this a user preference Claude didn't know?
   YES → preferences.md (cross-project, replace)

3. Is this a workflow/process the user follows?
   YES → workflows.md (cross-project)

4. Is this specific to this project/codebase?
   YES → auto memory MEMORY.md (project-scoped)

5. Is this a skill deficiency or gap?
   → Check improvements.md for prior occurrences
   → 0 prior: friction note in improvements.md Friction Log
   → 1+ prior: promote to/update Active Proposals in improvements.md

Classify each finding into one of these categories with its routing target:

CategoryDescriptionDestination
Behavioral CorrectionClaude did something the user corrected~/.claude/.context/core/rules.md
Preference DiscoveredUser has a preference Claude didn't know~/.claude/.context/core/preferences.md
Workflow CapturedUser follows a process Claude should know~/.claude/.context/core/workflows.md
Project-SpecificKnowledge specific to this codebase/projectAuto memory MEMORY.md
Skill Friction (1st)Skill issue, no prior occurrences~/.claude/.context/core/improvements.md Friction Log
Skill Friction (2nd+)Skill issue, matches prior friction~/.claude/.context/core/improvements.md Active Proposals

Also identify: which skill was active, root cause in 1-2 sentences.

Step 3: Prioritize findings
  • Rank by impact (time wasted, output quality, user frustration level)
  • Select top 5 maximum — forces prioritization, prevents fatigue
  • Allow up to 7 if findings span 4+ different destinations
  • If two findings share the same root cause, merge them
  • Drop low-impact items
Step 4: Present findings interactively

For each finding (one at a time), present:

  1. What happened: Quote the user's actual words
  2. Category: Which of the 6 categories
  3. Root cause: Why it happened (1-2 sentences)
  4. Proposed action: What to write and where (show the destination file)

Ask the user: confirm, reject, or refine. Only confirmed findings proceed to writing.

Step 5: Check existing files before writing

Before writing anything, read ALL potential destinations:

  • ~/.claude/.context/core/rules.md
  • ~/.claude/.context/core/preferences.md
  • ~/.claude/.context/core/workflows.md
  • ~/.claude/.context/core/improvements.md
  • Project auto memory MEMORY.md

Check for:

  • Duplicates: Skip if already captured
  • Contradictions: Present both to user, ask which is correct
  • Context-update overlap: Check what was already captured this session by context-update — skip duplicates
  • Line count: If improvements.md exceeds 100 lines, flag for consolidation before adding

If the context system is not initialized (~/.claude/.context/ doesn't exist), fall back to MEMORY.md only and suggest running /personal-assistant:setup.

Step 6: Write confirmed findings

Route each confirmed finding to its classified destination with format appropriate to that file:

rules.md (for Behavioral Corrections):

- [RULE]: [Actionable imperative — what to always/never do]
  - Source: Retrospective [date] — [brief friction context]

After writing to rules.md or preferences.md: Prompt the user: "Rules/preferences changed. Run /sync-context to update elle-core.md?"

preferences.md (for Preferences Discovered): Replace the relevant preference entry, or add a new one under the appropriate section. Follow the file's existing format.

workflows.md (for Workflows Captured): Add under the appropriate section following the file's existing format.

MEMORY.md (for Project-Specific findings):

## [Category]: [Short imperative description]
- [Actionable note, 1 line]
- [Evidence: what friction triggered this]

Rules: 1-3 bullet points max per entry. Include friction evidence.

improvements.md Friction Log (for Skill Friction, 1st occurrence): Add a row to the Friction Log table:

| [Date] | [Project] | [Skill/Area] | [Friction Summary] | 1 |

improvements.md Active Proposals (for Skill Friction, 2nd+ occurrences):

### [ENHANCEMENT|NEW SKILL] skill-name — Short description
- **Evidence**: [Friction moments that motivated this, with dates]
- **Projects**: [Which projects encountered this]
- **Current behavior**: [What happens now]
- **Proposed change**: [What should change]
- **Affected section**: [Which part of the SKILL.md or system]
- **Status**: Proposed
- **Promoted**: [Date]

Third-party skills (superpowers, external plugins): Log friction notes in improvements.md for pattern tracking, but mark proposals with "External skill — workaround via rules.md" and add a corresponding rule for the immediate workaround.

Step 7: Verify applied proposals

Check ~/.claude/.context/core/improvements.md for proposals with status Applied.

For each:

  • Scan the current session for friction related to that proposal
  • If no recurrence observed in this session, note it (after 2-3 sessions without recurrence, mark as Verified and move to Applied & Verified section)
  • If friction recurred, update status to "Needs Revision" and create a follow-up note
Step 8: Journal and summary

Write session summary to ~/.claude/.context/core/journal.md (append at TOP, newest first).

Show the user:

  • How many findings captured vs rejected
  • What was written and to which files (quoted)
  • Any proposals verified or flagged for revision
  • Any pending Active Proposals from previous sessions worth reviewing now

If no friction was found: report "Clean session — nothing to capture" and exit.

Common Mistakes and Why They Matter

Over-reporting kills trust. If you dump 10 findings on the user, they'll stop confirming any of them. Cap at 5 (up to 7 only when spanning 4+ destinations) and prioritize ruthlessly — the user needs to feel each finding is worth their time.

Writing without confirmation breaks the social contract. The retrospective works because the user trusts that nothing gets written without their say-so. If you silently write mid-session or skip confirmation, the user loses trust in the entire context system.

Misrouting creates invisible debt. The routing tree exists because each context file has a different scope and lifecycle:

  • Behavioral corrections in MEMORY.md get lost when the project ends — they belong in rules.md because they apply everywhere
  • Preferences in improvements.md clutter the improvement tracker — they belong in preferences.md where they get applied immediately
  • Skill proposals in project-scoped memory/skill-proposals.md can't aggregate cross-project patterns — they belong in improvements.md

Premature proposals waste energy. A single friction moment could be a fluke. The friction log exists to collect first occurrences cheaply — only promote to Active Proposals when the same root cause surfaces 2+ times, which signals a real pattern worth investing in.

Duplicating context-update work creates noise. The context-update process may have already captured corrections or preferences from this session. Check before writing to avoid contradictory or redundant entries.

Confusing iteration with friction undermines accuracy. "Make the font bigger" is collaboration. "You keep ignoring what I said" is friction. The distinction matters — logging normal refinement as friction inflates the signal and desensitizes the system.

Memory Management

  • Before writing, check all destination files for duplicates and contradictions
  • If improvements.md exceeds 100 lines, flag for consolidation before adding new entries
  • Proposals in improvements.md marked Verified can be pruned after 3 months
  • Friction Log entries that have been promoted to Active Proposals can be removed
  • improvements.md is cross-project — friction from all projects aggregates here, enabling pattern detection

Relationship to Other Skills

skill-creator:skill-creator         → Guides skill creation (assistant)
superpowers:writing-skills          → Tests and deploys skills (TDD)
/retrospective                      → Identifies what needs improving (feedback loop)
/update-context                     → Handles routine context updates (avoid duplication)
/sync-context                       → Regenerates elle-core.md after rule changes

This skill completes the skill lifecycle: create → test → deploy → observe → improve.

平台分布

Codex

36.12%
按下载量换算51

Claude

27.26%
按下载量换算38

Cursor

19.29%
按下载量换算27

Gemini CLI

8.94%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。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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