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To Spec

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FeatherHunter
to-spec

把当前对话整理成一份规格说明并发布到项目的议题跟踪器:不做访谈,只把你已经讨论过的内容综合出来。

Overview

PublisherFeatherHunter
Repositorydsh-mattpocock-skills-deck
Skill nameto-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 To 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/to-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/to-spec .claude/skills/to-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable To 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 To 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 To 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.

本技能会拿当前的对话上下文与对代码库的理解,产出一份规格说明。不要访谈用户;只综合你已经掌握的内容。

议题跟踪器与分诊标签词表应该已经提供给你了。如果没有,请告诉用户运行 /setup-matt-pocock-skills。

流程

  1. 探索仓库,弄清代码库的当前状态(如果你还没探索过)。整份规格说明都要使用项目领域词汇表的用词,并遵守你所触碰领域的任何 ADR。

  2. 勾画出你打算用来测试这个功能的接缝。已有接缝应优先于新建接缝。尽量使用最靠上的接缝。如果需要新接缝,就把它放在你能放的最高位置。整个代码库里的接缝越少越好——最理想的数量是一个。

与用户确认这些接缝符合他们的预期。

  1. 用下面的模板写规格说明,然后把它发布到项目的议题跟踪器。打上 ready-for-agent 分诊标签——不需要再做额外的分诊。

问题陈述

用户正在面对的问题,从用户的视角来描述。

解决方案

问题的解决方案,从用户的视角来描述。

用户故事

一份很长、带编号的用户故事清单。每条用户故事都应采用下面的格式:

  1. 作为<某个角色>,我想要<某个功能>,以便<获得某种好处>

这份用户故事清单应当极其详尽,覆盖该功能的方方面面。

实现决策

已做出的实现决策清单。可以包括:

  • 将要构建/修改的模块
  • 这些模块中将被修改的接口
  • 来自开发者的技术澄清
  • 架构决策
  • 数据结构变更
  • API 契约
  • 具体的交互

不要写出具体的文件路径或代码片段。它们可能很快就会过时。

例外:如果某个原型产出的片段比文字叙述更精确地表达了某项决策(状态机、归约器、数据结构、类型形状),就把它内联在相关决策里,并简短说明它来自原型。只保留决策密集的部分,不要整个可运行的演示,只要重要的那几点。

测试决策

已做出的测试决策清单。包括:

  • 说明什么样的测试才算好测试(只测外部行为,不测实现细节)
  • 哪些模块会被测试
  • 测试的先例(即代码库中类似类型的测试)

不在范围内

描述本规格说明不涵盖的内容。

补充说明

关于该功能的任何其他说明。

Frequently asked questions

What does the To Spec AI skill do?

把当前对话整理成一份规格说明并发布到项目的议题跟踪器:不做访谈,只把你已经讨论过的内容综合出来。

Why use To Spec on TypingMind?

Because you install it once and use it with any model. To 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 To 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/to-spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use To 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 To Spec?

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

Is the To 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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