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Pace Retro

Community
arch-team
pace-retro

Use when user says "回顾", "复盘", "度量", "retro", "总结", "数据分析", "DORA", "质量报告", "交付效率", "度量报告", "趋势", "中期检查", "对比", "预测", "forecast", "能按时交付吗", "交付概率", "瓶颈", "pace-retro", or at iteration end when reviewing progress and metrics. NOT for next-step recommendations (use /pace-next). NOT for current status overview (use /pace-status).

Overview

Publisherarch-team
Repositorydevpace
Skill namepace-retro
Stars
73
Forks
6
Bundled files
11
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by arch-team on GitHub. Read the source before you install it.

Installation

Install the Pace Retro AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/arch-team/devpace/tree/main/skills/pace-retro
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/arch-team/devpace.git /tmp/devpace
mkdir -p .claude/skills
cp -r /tmp/devpace/skills/pace-retro .claude/skills/pace-retro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pace Retro in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pace Retro on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pace Retro is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/pace-retro — 迭代回顾与度量

生成迭代回顾报告，更新度量仪表盘，提供多粒度分析视角。详细执行规则见对应 procedures 文件。

与现有机制的关系

机制职责区别
/pace-status metrics查看当前度量快照只读展示，不做分析
/pace-plan close迭代关闭时轻量回顾3 个基准指标，不含改进建议
/pace-retro完整回顾分析 + 经验沉淀多维度分析 + 趋势对比 + 知识闭环

推荐使用流程

1. /pace-retro mid          → 迭代中期快速检查（轻量，不影响基准线）
2. /pace-retro focus quality → 聚焦某个维度深入分析
3. /pace-retro               → 迭代末尾完整回顾
4. /pace-retro accept        → 确认回顾建议的操作（MoS 更新等）
5. /pace-retro compare       → 对比本迭代与上一迭代的变化
6. /pace-retro history       → 查看 3+ 迭代的趋势总览
7. /pace-retro update        → 仅刷新度量数据（不生成报告）

输入

$ARGUMENTS：

  • （空）→ 当前迭代完整回顾
  • update → 仅刷新度量数据，不生成报告（带变化反馈）
  • focus <维度> → 聚焦回顾：quality | delivery | dora | defects | value | knowledge | epic
  • compare → 对比回顾：当前迭代 vs 上一迭代的关键指标 delta
  • history → 趋势总览：跨 3+ 迭代的趋势线（基于 dashboard.md 历史快照）
  • mid → 中期检查：轻量版回顾，不更新 dashboard 基准线
  • accept → 确认回顾建议：执行 MoS 更新等上一次回顾建议的操作
  • forecast → 交付预测：基于历史模式预测迭代交付概率、识别瓶颈、发出风险预警

执行路由表

固定加载
文件说明
skills/pace-retro/retro-procedures-common.md通用规则：§0 路由索引 · Agent 记忆 · 数据收集详情 · 基准线检测
按子命令加载
参数额外加载文件执行路径
（空）skills/pace-retro/retro-procedures.md + skills/pace-retro/retro-procedures-dimensions.mdStep 1→2→3→4→5→6 完整回顾
updateskills/pace-retro/retro-procedures-update.mdStep 1→2（带变化反馈）
focus <维度>skills/pace-retro/retro-procedures-focus.md单维度深入分析
compareskills/pace-retro/retro-procedures-compare.md（自包含，不加载 common）双迭代对比
historyskills/pace-retro/retro-procedures-history.md（自包含，不加载 common）跨迭代趋势
midskills/pace-retro/retro-procedures-mid.md（自包含，不加载 common）中期轻量检查
acceptskills/pace-retro/retro-procedures-accept.md（自包含，不加载 common）执行建议操作
forecastskills/pace-retro/retro-procedures-forecast.md（自包含，不加载 common）交付预测分析

路由规则：只读取路由表映射的 procedures 文件，不加载其他文件。compare/history/mid/accept 自包含，不加载 common。

流程

Step 1：收集数据 + 基准线检测

从 backlog/project.md/iterations/releases 提取度量数据，判断首次/非首次度量。详见 skills/pace-retro/retro-procedures-common.md。

Step 2：更新 dashboard.md

更新度量表格，旧值追加到"度量趋势"表。update 模式在此步结束并输出变化反馈（详见 skills/pace-retro/retro-procedures-update.md）。

Step 3：生成回顾报告

两层结构：行动摘要（~10 行）+ 维度详情（按角色排序）。详见 skills/pace-retro/retro-procedures.md。

Step 4：经验沉淀

提炼 pattern 交给 pace-learn 管道，输出「本次学习」透明段。详见 skills/pace-retro/retro-procedures.md。

Step 5：迭代传递清单

结构化传递清单写入 iterations/current.md，供 /pace-plan next 消费。详见 skills/pace-retro/retro-procedures.md。

Step 6：报告质量自评

数据充分度、趋势可信度、建议可执行度三维自评。详见 skills/pace-retro/retro-procedures.md。

输出

行动摘要 + 维度详情报告 + 更新后的 dashboard.md + insights.md 经验沉淀（如有新 pattern）+ 迭代传递清单（写入 iterations/current.md）。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pace Retro AI skill do?

Use when user says "回顾", "复盘", "度量", "retro", "总结", "数据分析", "DORA", "质量报告", "交付效率", "度量报告", "趋势", "中期检查", "对比", "预测", "forecast", "能按时交付吗", "交付概率", "瓶颈", "pace-retro", or at iteration end when reviewing progress and metrics. NOT for next-step recommendations (use /pace-next). NOT for current status overview (use /pace-status).

Why use Pace Retro on TypingMind?

Because you install it once and use it with any model. Pace Retro is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pace Retro in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/arch-team/devpace/tree/main/skills/pace-retro. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pace Retro?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pace Retro?

As many as you like. As long as a model supports skills, you can use Pace Retro with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pace Retro AI skill free?

Yes. It is published on GitHub by arch-team under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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