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Spec

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poshan0126
spec

Turn a fuzzy feature idea into a self-contained SPEC.md by interviewing you first. Claude asks about scope, behavior, edge cases, and verification, then writes a spec a fresh implementation session can execute. Use before building anything non-trivial.

Overview

Publisherposhan0126
Repositorydotclaude
Skill namespec
Stars
870
Forks
204
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 poshan0126 on GitHub. Read the source before you install it.

Installation

Install the 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/poshan0126/dotclaude/tree/main/skills/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/poshan0126/dotclaude.git /tmp/dotclaude
mkdir -p .claude/skills
cp -r /tmp/dotclaude/skills/spec .claude/skills/spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Separate deciding WHAT to build from building it. Interview the user, ground the answers in the actual codebase, then write a self-contained SPEC.md a fresh session can execute without you in the room. $ARGUMENTS is an optional starting description.

Run this in a session that already has the repo open, so the questions are informed by real code.

Step 1: Explore enough to ask good questions

Skim the codebase for what the feature will touch: the relevant modules, existing patterns to match, the test setup, and any similar feature to model after. Read only — you're gathering context to interview well, not building.

Step 2: Interview with AskUserQuestion

Ask only what you can't answer from the code, in focused rounds (use AskUserQuestion; batch related questions). Cover:

  • Scope & outcome — what does "done" look like from the user's side, and what's the smallest version worth shipping?
  • Behavior — the happy path, then the edge cases (empty, error, concurrent, permission-denied) that actually matter here.
  • Boundaries — what is explicitly OUT of scope for this iteration.
  • Constraints — compatibility, performance, data/migration concerns, security-sensitive surfaces.
  • Verification — how will we confirm it works end to end (a command, a test, an observable behavior)?

Where the code implies an answer, propose it as the default in the question rather than asking cold.

Step 3: Write SPEC.md

Write SPEC.md at the project root (NOT in .claude/). Make it self-contained — a fresh session with no memory of this conversation should be able to execute it:

markdown
# Spec: <feature>

## Goal
<what and why, 2-3 sentences>

## Scope
- In: <what this iteration delivers>
- Out: <explicitly deferred>

## Behavior
- <happy path>
- <edge cases and how each should behave>

## Touch points
- <exact files / modules / interfaces involved, as file:path — named, not vague>

## Constraints
- <compat, performance, security, data/migration>

## Verification
- <the exact end-to-end check: command to run, test to pass, or behavior to observe>

## Open questions
- <anything still undecided — none is ideal>

Show the draft and confirm before writing. Then tell the user to run the implementation in a fresh session (/clear first) so it starts with a clean context and the spec as its brief.

Rules

  • Interview before writing — do not skip Step 2 and guess the spec.
  • Name real files and interfaces from the codebase; a spec full of placeholders isn't self-contained.
  • Every spec states its verification step. If you can't name one, say so and ask the user how they'll check.
  • One feature per spec. If the idea is really three features, say so and spec the one that was asked.

Frequently asked questions

What does the Spec AI skill do?

Turn a fuzzy feature idea into a self-contained SPEC.md by interviewing you first. Claude asks about scope, behavior, edge cases, and verification, then writes a spec a fresh implementation session can execute. Use before building anything non-trivial.

Why use Spec on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/poshan0126/dotclaude/tree/main/skills/spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Spec?

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

Is the Spec AI skill free?

Yes. It is published on GitHub by poshan0126 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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