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Spec

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owainlewis
spec

Defines one feature or major change: required behavior, technical design, failure handling, and acceptance checks. Use when consequential decisions must be settled before delivery. The spec is the ticket.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namespec
Stars
419
Forks
73
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by owainlewis 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/owainlewis/blueprint/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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/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

Write one implementation-ready specification for one coherent change. The spec is the ticket: its source may be docs/<feature-slug>/spec.md, a GitHub issue, or another user-supplied document. Update the supplied source; do not create a duplicate ticket or document. Without a supplied source, use the Markdown path above. Publish to a tracker only when asked.

Process

  1. Read the request, repository instructions, requirements, architecture, and relevant implementation. Reuse settled decisions and verify claims about current behavior.
  2. Define the outcome and scope. Ask about unresolved choices that could change behavior, data, interfaces, security, compatibility, operations, cost, or proof. Leave reversible local mechanics to implementation.
  3. Write the spec using the shape below. Explain the change from current behavior; link shared requirements and architecture instead of restating them.
  4. Check that a fresh agent could implement it without inventing a consequential decision. Trace success, denial, invalid input, and partial failure where relevant.
  5. Stop with the spec ready for review. Do not plan tasks or implement it.

Spec shape

Outcome and scope

State the problem, affected user or caller, desired result, constraints, and non-goals.

Required behavior

Describe inputs, actions, observable results, permissions, business rules, and failure recovery. Reference applicable REQ-n IDs. Settle feature-specific product details here.

Technical design

Explain changed responsibilities, interfaces, data, and execution order. Use concrete types, payloads, state tables, or diagrams when they remove ambiguity.

Cover schema changes, indexes, migrations, transaction boundaries, concurrency, retries, cancellation, security, compatibility, observability, rollout, and rollback only where they affect the change. State important choices, their reasons, and their costs.

Name any required change to system architecture. Do not silently override an accepted rule. Record the proposed architecture change for review before delivery.

Acceptance and proof

Pair each observable condition and affected architectural rule with a check.

IDDone whenHow to check
AC-1Concrete resultCommand, automated scenario, or manual check

Preserve existing AC-n, REQ-n, and INV-n references. Never reuse or renumber IDs. Do not invent executable commands for code that does not exist; define the scenario and expected result instead.

Open decisions

State missing answers, recommended defaults when justified, and whether they block delivery. Write None when settled. Do not hide unknowns behind placeholders.

Lifecycle

State the decision status and delivery status separately. After delivery, retain the spec as a record of the feature decisions and acceptance checks. Link delivered PRs and proof without claiming unrun checks passed. Record consequential changes made during delivery; do not erase the reasoning. Keep current product and system rules in requirements and architecture.

Boundaries

One spec may lead to several tasks and pull requests. Use /plan when splitting helps delivery. Small, decided fixes can use their task description and checks without a formal spec. Do not require root documents for a local change that does not need them.

Return

Report the source, main decision and tradeoff, and any blocking question.

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 Spec AI skill do?

Defines one feature or major change: required behavior, technical design, failure handling, and acceptance checks. Use when consequential decisions must be settled before delivery. The spec is the ticket.

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/owainlewis/blueprint/tree/main/skills/spec. 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 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 owainlewis 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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