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Implement Spec

Community
FeatherHunter
implement-spec

把 /to-spec 与 /to-tickets 的成果用代码实现出来。

Overview

PublisherFeatherHunter
Repositorydsh-mattpocock-skills-deck
Skill nameimplement-spec
Stars
263
Forks
22
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FeatherHunter on GitHub. Read the source before you install it.

Installation

Install the Implement Spec 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/FeatherHunter/dsh-mattpocock-skills-deck/tree/main/package/bundled-skills-zh/implement-spec
  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/FeatherHunter/dsh-mattpocock-skills-deck.git /tmp/dsh-mattpocock-skills-deck
mkdir -p .claude/skills
cp -r /tmp/dsh-mattpocock-skills-deck/package/bundled-skills-zh/implement-spec .claude/skills/implement-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implement Spec 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 Implement Spec 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 Implement Spec 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.

你已经拿到了一份规格说明。这份规格说明应当配有工单,说明该怎么实现它。

议题跟踪器应当已经提供给你了。如果没有,就让用户去跑 /setup-matt-pocock-skills。

目标是在一条集成分支上把整份规格说明实现完,并且让每一张工单都按议题跟踪器收尾工作的方式解决掉。

工单不是一串步骤,而是一张带阻塞关系的任务图。这意味着任何时候都存在一批随时可以领取的前沿工单。

与子代理之间的往来要少。主要靠上下文指针沟通:指向规格说明、工单、调研笔记和以前的提交。已经能通过指针拿到的信息,不要重复一遍。

实现者子代理应尽可能放到后台运行,以获得最大的并发度。

步骤

  1. 读规格说明和工单,弄清任务图。

  2. (可选)用探查子代理去做工单要求的任何探查——相关的代码库文件或外部文档。要确保探查子代理能保存文件:它应当把 Markdown 笔记保存在仓库之外、所有后续子代理都能访问的某个目录里。这样实现者子代理就能专注在实现上,而不必去做探查。

  3. 建集成分支。如果议题跟踪器是通过 PR 收尾工作的,或者用户要一个,那就在第 5 步第一次合并之后开一个草稿 PR(一个还没有比 main 多出任何提交的分支是开不了 PR 的),并标注它会关闭这份规格说明和这些工单。

  4. 用实现者子代理实现每张工单,每张工单各在自己的工作树、自己的分支上。每个实现者子代理都要:

    • 动手之前先确认自己的工作树是基于集成分支的,如果不是,就重置到集成分支上;
    • 调用 Skill 工具并传入 tdd,把这张工单实现出来;
    • 在报告完成之前,把集成分支的最新提交合并进自己的分支
  5. 一个实现者子代理完成后,用合并子代理把它的成果合并到集成分支。

  6. 如果这一步改变了可领取工单的前沿,就再起几个实现者子代理去做新冒出来的工单。这样能获得最大的并发度。

  7. 等所有工单都完成后,在集成分支上调用 Skill 工具并传入 code-review。用一个实现者子代理把代码评审提出的所有问题一次性修完。

  8. 如果存在草稿 PR,就把它标记为可供评审。否则,按议题跟踪器收尾工作的方式逐张解决工单,并报告集成分支。

  9. 清理掉所有实现者子代理的工作树。

Frequently asked questions

What does the Implement Spec AI skill do?

把 /to-spec 与 /to-tickets 的成果用代码实现出来。

Why use Implement Spec on TypingMind?

Because you install it once and use it with any model. Implement Spec 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 Implement Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FeatherHunter/dsh-mattpocock-skills-deck/tree/main/package/bundled-skills-zh/implement-spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Implement Spec?

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 Implement Spec?

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

Is the Implement Spec AI skill free?

Yes. It is published on GitHub by FeatherHunter 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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