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Spec

Organization
g2i-ai
spec

Transform a product brief into a technical specification.

Overview

Publisherg2i-ai
Repositoryagents
Skill namespec
Stars
53
Forks
2
Bundled files
Instructions only
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 g2i-ai 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/g2i-ai/agents/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/g2i-ai/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/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.

Spec

Invocation

This skill is performed in the Architect persona. If you are not already running as Architect (for example, the user invoked /spec directly), read subagents/architect.md and adopt its role guidance for the rest of this task. Stay in the current chat—do not delegate the skill itself to a subagent. You should, however, aggressively delegate codebase research to research subagents (see Codebase Discovery below) so that only their summarized findings—not raw file contents—land in this conversation.

Objective

Transform a product brief into a technical specification and save it to docs/specs/{project-name-slug}.md.

Instructions

  1. Explain the brief-to-spec transformation.

  2. Ask for the brief path and extract the goal, target users, constraints, and success metrics.

  3. Codebase discovery. Before asking the user technical questions, dispatch research subagents in parallel for each independent area the spec needs to ground itself in. Typical areas:

    • existing modules likely to be touched (describe by feature or area, not by guessed file paths)
    • data model / database schema
    • relevant API routes, server actions, and their auth posture
    • existing tests and fixtures for the affected area
    • cross-cutting concerns already in play (feature flags, telemetry, rate limits)

    Each subagent prompt MUST request a structured summary, not file dumps. Require the subagent to return:

    • file paths and line ranges of the 3-10 most relevant symbols
    • current ownership boundaries and key types or interfaces
    • constraints, footguns, or TODOs already present
    • what is not in the area, to bound scope

    Treat the returned summaries as your working knowledge of the codebase. Only Read a file directly in this chat if a subagent summary is insufficient and you need a specific snippet to ground a decision in the spec. Inline Read/Grep is the exception, not the default.

  4. Ask focused technical discovery questions one at a time as needed, informed by the discovery findings:

    • architecture and ownership boundaries
    • data model or schema changes
    • auth and authorization implications
    • external integrations
    • security and privacy concerns
    • performance budgets or expected scale
    • rollout and deployment considerations
  5. Read templates/tech-spec-template.md and follow its structure. Capture the discovery findings as compact citations (path and line range with a one-line note), not pasted source.

  6. Keep the testing plan at the requirements and assertions level, not the implementation level.

  7. Save the spec to docs/specs/{project-name-slug}.md.

  8. If the brief has a status field, update it from draft to completed.

  9. End with the spec path, the most important technical decisions, and the recommended next step: /tdd or /code.

Frequently asked questions

What does the Spec AI skill do?

Transform a product brief into a technical specification.

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/g2i-ai/agents/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?

It is published on GitHub by g2i-ai. Check the repository for licensing terms. 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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