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Agent Skill · 研究检索

gstack-openclaw-retro(gstack OpenClaw retro 搜索)

gstack-openclaw-retro 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

gstack-openclaw-retro 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,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 4,301。

总安装 33,746周安装 1,352GitHub Stars 公开资料未说明下载 10,924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:gstack-openclaw-retro(gstack OpenClaw retro 搜索)
来源仓库:https://github.com/garrytan/gstack-openclaw-retro
安装命令:
openclaw skills install gstack-openclaw-retro
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 gstack-openclaw-retro

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。

每周工程回顾分析器,追踪提交历史与代码质量趋势。适用于团队效能度量与技术债务可视化展示。具备团队意识标签,支持多成员贡献归因。使用前请开启 Git 日志整理与静态分析插件。建议固定周期运行以形成持续改进闭环。gstack-openclaw-retro 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 gstack-openclaw-retro;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

    它需要需要联网,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

gstack-openclaw-retro 怎么安装?

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

gstack-openclaw-retro 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。

gstack-openclaw-retro 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

gstack-openclaw-retro 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。

gstack-openclaw-retro 用的人多吗?

公开数据是累计安装 33,746 次,近一周安装 1,352 次,下载 10,924 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。

gstack-openclaw-retro 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

  • 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。

先想清楚的地方

  • 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 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。

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
gstack-openclaw-retro

gstack OpenClaw retro 搜索 · 本页

33,746—需要联网MIT-0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

name
gstack-openclaw-retro
description
Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware with per-person contributions, praise, and growth areas. Use when asked for weekly retro, what shipped this week, or engineering retrospective.
version
1.0.0
metadata
{ "openclaw": { "emoji": "📊" } }

Weekly Engineering Retrospective

Generates a comprehensive engineering retrospective analyzing commit history, work patterns, and code quality metrics. Team-aware: identifies the user running the command, then analyzes every contributor with per-person praise and growth opportunities.

Arguments

  • Default: last 7 days
  • 24h: last 24 hours
  • 14d: last 14 days
  • 30d: last 30 days
  • compare: compare current window vs prior same-length window

Instructions

Parse the argument to determine the time window. Default to 7 days. All times should be reported in the user's local timezone.

Midnight-aligned windows: For day units, compute an absolute start date at local midnight. For example, if today is 2026-03-18 and the window is 7 days, the start date is 2026-03-11. Use --since="2026-03-11T00:00:00" for git log queries. For hour units, use --since="N hours ago".


Step 1: Gather Raw Data

First, fetch origin and identify the current user:

git fetch origin main --quiet
git config user.name
git config user.email

The name returned by git config user.name is "you" ... the person reading this retro. All other authors are teammates.

Run ALL of these git commands (they are independent):

# All commits with timestamps, subject, hash, author, files changed
git log origin/main --since="<window>" --format="%H|%aN|%ae|%ai|%s" --shortstat

# Per-commit test vs total LOC breakdown with author
git log origin/main --since="<window>" --format="COMMIT:%H|%aN" --numstat

# Commit timestamps for session detection and hourly distribution
git log origin/main --since="<window>" --format="%at|%aN|%ai|%s" | sort -n

# Files most frequently changed (hotspot analysis)
git log origin/main --since="<window>" --format="" --name-only | grep -v '^$' | sort | uniq -c | sort -rn

# PR numbers from commit messages
git log origin/main --since="<window>" --format="%s" | grep -oE '[#!][0-9]+' | sort -t'#' -k1 | uniq

# Per-author file hotspots
git log origin/main --since="<window>" --format="AUTHOR:%aN" --name-only

# Per-author commit counts
git shortlog origin/main --since="<window>" -sn --no-merges

# Test file count
find . -name '*.test.*' -o -name '*.spec.*' -o -name '*_test.*' -o -name '*_spec.*' 2>/dev/null | grep -v node_modules | wc -l

# Test files changed in window
git log origin/main --since="<window>" --format="" --name-only | grep -E '\.(test|spec)\.' | sort -u | wc -l

Step 2: Compute Metrics

Calculate and present these metrics in a summary:

  • Commits to main: N
  • Contributors: N
  • PRs merged: N
  • Total insertions: N
  • Total deletions: N
  • Net LOC added: N
  • Test LOC (insertions): N
  • Test LOC ratio: N%
  • Version range: vX.Y.Z → vX.Y.Z
  • Active days: N
  • Detected sessions: N
  • Avg LOC/session-hour: N

Then show a per-author leaderboard immediately below:

Contributor         Commits   +/-          Top area
You (garry)              32   +2400/-300   browse/
alice                    12   +800/-150    app/services/
bob                       3   +120/-40     tests/

Sort by commits descending. The current user always appears first, labeled "You (name)".


Step 3: Commit Time Distribution

Show hourly histogram in local time:

Hour  Commits  ████████████████
 00:    4      ████
 07:    5      █████
 ...

Identify:

  • Peak hours
  • Dead zones
  • Bimodal pattern (morning/evening) vs continuous
  • Late-night coding clusters (after 10pm)

Step 4: Work Session Detection

Detect sessions using 45-minute gap threshold between consecutive commits.

Classify sessions:

  • Deep sessions (50+ min)
  • Medium sessions (20-50 min)
  • Micro sessions (<20 min, single-commit)

Calculate:

  • Total active coding time
  • Average session length
  • LOC per hour of active time

Step 5: Commit Type Breakdown

Categorize by conventional commit prefix (feat/fix/refactor/test/chore/docs). Show as percentage bar:

feat:     20  (40%)  ████████████████████
fix:      27  (54%)  ███████████████████████████
refactor:  2  ( 4%)  ██

Flag if fix ratio exceeds 50% ... signals a "ship fast, fix fast" pattern that may indicate review gaps.


Step 6: Hotspot Analysis

Show top 10 most-changed files. Flag:

  • Files changed 5+ times (churn hotspots)
  • Test files vs production files in the hotspot list
  • VERSION/CHANGELOG frequency

Step 7: PR Size Distribution

Estimate PR sizes and bucket them:

  • Small (<100 LOC)
  • Medium (100-500 LOC)
  • Large (500-1500 LOC)
  • XL (1500+ LOC)

Step 8: Focus Score + Ship of the Week

Focus score: Percentage of commits touching the single most-changed top-level directory. Higher = deeper focused work. Lower = scattered context-switching.

Ship of the week: The single highest-LOC PR in the window. Highlight PR number, LOC changed, and why it matters.


Step 9: Team Member Analysis

For each contributor (including the current user), compute:

  1. Commits and LOC ... total commits, insertions, deletions, net LOC
  2. Areas of focus ... which directories/files they touched most (top 3)
  3. Commit type mix ... their personal feat/fix/refactor/test breakdown
  4. Session patterns ... when they code (peak hours), session count
  5. Test discipline ... their personal test LOC ratio
  6. Biggest ship ... their single highest-impact commit or PR

For the current user ("You"): Deepest treatment. Include all session analysis, time patterns, focus score. Frame in first person.

For each teammate: 2-3 sentences covering what they shipped and their pattern. Then:

  • Praise (1-2 specific things): Anchor in actual commits. Not "great work" ... say exactly what was good.
  • Opportunity for growth (1 specific thing): Frame as leveling-up, not criticism. Anchor in actual data.

If solo repo: Skip team breakdown.

AI collaboration: If commits have Co-Authored-By AI trailers, track "AI-assisted commits" as a separate metric.


Step 10: Week-over-Week Trends (if window >= 14d)

Split into weekly buckets and show trends:

  • Commits per week (total and per-author)
  • LOC per week
  • Test ratio per week
  • Fix ratio per week
  • Session count per week

Step 11: Streak Tracking

Count consecutive days with at least 1 commit, going back from today:

# Team streak
git log origin/main --format="%ad" --date=format:"%Y-%m-%d" | sort -u

# Personal streak
git log origin/main --author="<user_name>" --format="%ad" --date=format:"%Y-%m-%d" | sort -u

Display both:

  • "Team shipping streak: 47 consecutive days"
  • "Your shipping streak: 32 consecutive days"

Step 12: Load History & Compare

Check for prior retro history in memory/:

If prior retros exist, load the most recent one and calculate deltas:

                    Last        Now         Delta
Test ratio:         22%    →    41%         ↑19pp
Sessions:           10     →    14          ↑4
LOC/hour:           200    →    350         ↑75%
Fix ratio:          54%    →    30%         ↓24pp (improving)

If no prior retros exist, note "First retro recorded, run again next week to see trends."


Step 13: Save Retro History

Save a JSON snapshot to memory/retro-YYYY-MM-DD.json with metrics, authors, version range, streak, and tweetable summary.


Step 14: Write the Narrative

Format for Telegram (bullets, bold, no markdown tables in the final output).

Structure:

Tweetable summary (first line):

Week of Mar 1: 47 commits (3 contributors), 3.2k LOC, 38% tests, 12 PRs, peak: 10pm | Streak: 47d

Then sections:

  • Summary ... key metrics
  • Trends vs Last Retro ... deltas (skip if first retro)
  • Time & Session Patterns ... when the team codes, session lengths, deep vs micro
  • Shipping Velocity ... commit types, PR sizes, fix-chain detection
  • Code Quality Signals ... test ratio, hotspots, churn
  • Focus & Highlights ... focus score, ship of the week
  • Your Week ... personal deep-dive for the current user
  • Team Breakdown ... per-teammate analysis with praise + growth (skip if solo)
  • Top 3 Team Wins ... highest-impact things shipped
  • 3 Things to Improve ... specific, actionable, anchored in commits
  • 3 Habits for Next Week ... small, practical, realistic (<5 min to adopt)

Compare Mode

When the user says "compare":

  • Run the retro for the current window
  • Run the retro for the prior same-length window
  • Present side-by-side metrics with arrows showing improvement/regression
  • Brief narrative on biggest changes

Important Rules

  • All times in local timezone. Never set TZ.
  • Format for Telegram. Use bullets and bold. Avoid markdown tables in the final output.
  • Praise anchored in commits. Never say "great work" without naming what was good.
  • Growth areas anchored in data. Never criticize without evidence.
  • Save history. Every retro saves to memory/ for trend tracking.
  • Completion status:

- DONE ... retro generated, history saved - DONE_WITH_CONCERNS ... generated but missing data (e.g., no prior retros for comparison) - BLOCKED ... not in a git repo or no commits in window

平台分布

OpenClaw

87.08%
按下载量换算9,513

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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