Implement Specs logo

Implement Specs

Organization
warpdotdev
implement-specs

Implement an approved feature from PRODUCT.md and TECH.md, keeping specs and code aligned in the same PR as implementation evolves. Use after the product and tech specs are approved and the next step is building the feature.

Overview

Publisherwarpdotdev
Repositorycommon-skills
Skill nameimplement-specs
Stars
597
Forks
33
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 warpdotdev on GitHub. Read the source before you install it.

Installation

Install the Implement Specs 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/warpdotdev/common-skills/tree/main/.agents/skills/implement-specs
  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/warpdotdev/common-skills.git /tmp/common-skills
mkdir -p .claude/skills
cp -r /tmp/common-skills/.agents/skills/implement-specs .claude/skills/implement-specs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implement Specs 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 Specs 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 Specs 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.

implement-specs

Implement an approved feature from PRODUCT.md and TECH.md.

Overview

Use this skill after the product and tech specs are approved. The goal is to build the feature described by the specs while keeping the checked-in specs and the implementation aligned as the work evolves.

Approved specs should live directly under a ticket-named directory in specs/, for example specs/APP-1234/PRODUCT.md and specs/APP-1234/TECH.md.

In many cases, the implementation should be pushed in the same PR as the product and tech specs. As the engineer iterates, changes to PRODUCT.md, TECH.md, and the code should all be pushed in that same PR so review stays anchored to the feature that will actually ship.

Prerequisites

Before using this skill:

  • confirm that PRODUCT.md exists
  • confirm that TECH.md exists when the feature warranted one
  • confirm that the relevant specs have been reviewed and approved enough to start implementation

Workflow

1. Read the approved specs first

Treat:

  • PRODUCT.md as the source of truth for user-facing behavior
  • TECH.md as the source of truth for architecture, sequencing, and implementation shape

Make sure you understand the expected behavior, constraints, risks, and validation plan before writing code.

2. Offer optional implementation aids for large features

For large or long-running features, optionally offer one of these aids to the user before implementation begins:

  • PROJECT_LOG.md to track checkpoints, explored paths, partial findings, and current implementation state
  • DECISIONS.md to capture concrete product and technical decisions made during the PRD and tech design process

These are optional aids, not required deliverables. Offer them when they would reduce confusion or help future agents avoid re-exploring the same paths.

3. Plan and implement against the specs

Break the work into concrete implementation steps, then implement the feature against the approved specs.

During implementation:

  • keep behavior aligned with PRODUCT.md
  • keep architecture and sequencing aligned with TECH.md
  • add or update tests and verification artifacts as the work lands

Use the same PR for the specs and implementation when practical so the full feature evolution is reviewable in one place.

4. Update specs as the implementation evolves

If implementation reveals that the intended behavior or design should change, update the checked-in specs rather than letting them go stale.

In particular:

  • update PRODUCT.md when user-facing behavior, UX, edge cases, or success criteria change
  • update TECH.md when architecture, sequencing, module boundaries, or validation strategy change
  • keep those updates in the same PR as the corresponding code changes

The PR should describe the feature that actually ships, not just the initial draft of the specs.

5. Verify against the specs

Before considering the work complete, verify that the code matches the current specs.

Prefer:

  • unit tests and regression coverage that follow the repository's local testing conventions
  • integration or end-to-end tests for important user flows

Best Practices

  • Keep specs and code synchronized throughout implementation.
  • Prefer updating the spec immediately when decisions change rather than batching spec cleanup until the end.
  • Use optional tracking documents only when they add real value for a complex feature.
  • Keep the same PR coherent: spec updates, code changes, tests, and optional tracking docs should all support the same feature narrative.

Related Skills

  • spec-driven-implementation
  • write-product-spec
  • write-tech-spec

Frequently asked questions

What does the Implement Specs AI skill do?

Implement an approved feature from PRODUCT.md and TECH.md, keeping specs and code aligned in the same PR as implementation evolves. Use after the product and tech specs are approved and the next step is building the feature.

Why use Implement Specs on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/warpdotdev/common-skills/tree/main/.agents/skills/implement-specs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Implement Specs?

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 Specs?

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

Is the Implement Specs AI skill free?

Yes. It is published on GitHub by warpdotdev 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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