Spec Driven Implementation logo

Spec Driven Implementation

Organization
warpdotdev
spec-driven-implementation

Drive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs updated as implementation evolves. Use when starting a significant feature, planning agent-driven implementation, or when the user wants product and tech specs checked into source control.

Overview

Publisherwarpdotdev
Repositorycommon-skills
Skill namespec-driven-implementation
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 Spec Driven Implementation 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/spec-driven-implementation
  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/spec-driven-implementation .claude/skills/spec-driven-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Driven Implementation 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 Driven Implementation 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 Driven Implementation 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-driven-implementation

Drive a spec-first workflow for substantial features in Warp.

Overview

Use this skill for significant features where a written spec will improve implementation quality, reduce ambiguity, or make review easier. Be pragmatic: not every change needs specs.

Specs should usually live in:

  • specs/<linear-ticket-number>/PRODUCT.md
  • specs/<linear-ticket-number>/TECH.md

For example:

  • specs/APP-1234/PRODUCT.md
  • specs/APP-1234/TECH.md

specs/ should contain only ticket-named directories as direct children. Do not create engineer-named subdirectories or feature-slug directories there.

If a relevant Linear issue does not already exist, create one before writing specs. Use the Linear MCP tools directly:

  • list_teams to find the appropriate team
  • list_issue_labels to inspect the expected labels/tags
  • save_issue to create the issue with the appropriate team and labels

If the correct team or labels are not obvious from the request and surrounding context, use ask_user_question to clarify rather than guessing.

These specs should largely be written by agents, not by hand, and should be checked into source control so they can be reviewed and kept current with the code.

When specs are required

Strongly prefer specs when the change is substantial, such as:

  • product or architectural ambiguity
  • expected implementation size around 1k+ LOC
  • deep or cross-cutting stack changes
  • risky behavior changes where regressions would be expensive
  • work where agent quality will improve materially from clearer inputs

Specs are often unnecessary for:

  • small, local bug fixes
  • straightforward refactors
  • narrow UI tweaks with little ambiguity

For pure UI changes, the product spec is often useful while the tech spec may be unnecessary.

Workflow

1. Decide whether the feature needs specs

Evaluate the size, ambiguity, and risk of the feature. If specs will not meaningfully improve execution or review, skip them and focus on verification instead.

2. Write the product spec first

Before implementation, create PRODUCT.md describing the desired user-facing behavior.

Use the write-product-spec skill to produce it. The product spec should define:

  • what problem is being solved
  • the desired user experience
  • invariants and edge cases
  • success criteria
  • how the behavior will be validated

If the feature has UI or interaction design, ask for a Figma mock if one exists. If there is no mock, continue but call that out explicitly in the product spec.

Reference the Linear issue in the spec when one exists. Because specs live under specs/<linear-ticket-number>/..., this should usually be straightforward.

3. Write the tech spec when warranted

Use the write-tech-spec skill for substantial or ambiguous implementation work.

Prefer a tech spec when:

  • the implementation spans multiple subsystems
  • architecture or extensibility matters
  • there are meaningful tradeoffs to document
  • reviewers will benefit more from reviewing the plan than the raw code

It is acceptable to write the tech spec after an e2e prototype if that leads to a more accurate implementation plan. Do not force a premature tech spec when the implementation details are still too uncertain.

4. Implement approved specs

After the specs are approved, use the implement-specs skill to build from the approved PRODUCT.md and TECH.md.

The implementation can often be pushed in the same PR as the product and tech specs. As the engineer iterates, keep PRODUCT.md, TECH.md, code changes, and tests in that same PR so the review reflects the feature that will actually ship.

For large features, the implementer may optionally offer:

  • PROJECT_LOG.md to track explored paths, checkpoints, and current implementation state
  • DECISIONS.md to capture concrete product and technical decisions made during design and implementation

These are optional aids, not required outputs.

5. Keep specs current during implementation

If implementation changes from the spec, update the spec rather than leaving it stale.

Update PRODUCT.md when:

  • user-facing behavior changes
  • success criteria change
  • UX details or edge cases change

Update TECH.md when:

  • the implementation approach changes
  • architectural boundaries move
  • risks, dependencies, or rollout details change
  • the testing or validation plan changes

The checked-in specs should describe the feature that actually ships, not just the initial intent. Keep those spec updates in the same PR as the related code changes whenever practical.

6. Verify behavior against the spec

Before considering the work complete, make sure verification maps back to the specs. Prefer tests and artifacts that validate the product behavior directly:

  • unit tests and regression coverage that follow the repository's local testing conventions
  • integration tests for critical user flows
  • loom walkthroughs or equivalent feature demonstrations when appropriate
  • screenshots or videos when useful for UI-heavy work

Best Practices

  • Be pragmatic above all else.
  • Write specs to improve input quality for agents, not as ceremony.
  • Keep product specs behavior-oriented and implementation-light.
  • Keep tech specs implementation-oriented and grounded in current codebase patterns.
  • When a spec references relevant code chunks, include the inspected commit SHA in the file reference when possible and link the reference to the exact GitHub blob/<sha>/...#Lx-Ly lines.
  • Use review time to validate specs and behavior, not to over-index on code style nits.

Related Skills

  • implement-specs
  • write-product-spec
  • write-tech-spec

Frequently asked questions

What does the Spec Driven Implementation AI skill do?

Drive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs updated as implementation evolves. Use when starting a significant feature, planning agent-driven implementation, or when the user wants product and tech specs checked into source control.

Why use Spec Driven Implementation on TypingMind?

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

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

Which AI models can use Spec Driven Implementation?

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 Driven Implementation?

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

Is the Spec Driven Implementation 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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