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Create Product Spec

Organization
warpdotdev
create-product-spec

Create a product spec from a GitHub issue in this repository by applying the shared `write-product-spec` workflow with Oz-specific issue context and output paths. Use when an issue should be turned into a product spec artifact stored under `specs/GH<issue-number>/product.md` and the agent should prepare file changes only, without creating commits or pull requests itself unless a cloud workflow explicitly asks for it.

Overview

Publisherwarpdotdev
Repositoryoz-for-oss
Skill namecreate-product-spec
Stars
313
Forks
43
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 Create Product 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/warpdotdev/oz-for-oss/tree/main/.agents/skills/create-product-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/warpdotdev/oz-for-oss.git /tmp/oz-for-oss
mkdir -p .claude/skills
cp -r /tmp/oz-for-oss/.agents/skills/create-product-spec .claude/skills/create-product-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Product 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 Create Product 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 Create Product 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.

create-product-spec

Create a product spec from a GitHub issue for this repository.

Overview

This skill is a thin Oz wrapper around the shared product-spec workflow:

  • write-product-spec

Use that shared skill as the base behavior and structure unless this wrapper overrides it. Keep the same emphasis on precise user-facing behavior, invariants, edge cases, validation, and open questions.

The Oz-specific differences are:

  • the primary input is a GitHub issue, not a Linear issue
  • the output path is specs/GH<issue-number>/product.md
  • issue_comments.md and triggering-comment context are first-class inputs
  • a workflow may also request a structured PR metadata file in pr-metadata.json
  • do not create or edit Linear issues as part of this workflow

Inputs

Expect issue details in the prompt, including the issue number, title, description, labels, assignees, and optional prior discussion captured in issue_comments.md.

If a triggering comment or other workflow-provided comment context is present, treat it as additional context, not as a silent override of the issue body. Resolved decisions from comments can refine the spec; unresolved disagreements should remain explicit open questions.

Process

  1. Start from the shared write-product-spec guidance and follow its structure and writing standards unless this wrapper says otherwise.
  2. Read the issue details carefully. If issue_comments.md exists, review it for clarifications, prior decisions, and issue-comment nuance that should influence the spec.
  3. Inspect the repository enough to understand the current user workflow and likely scope before writing the spec.
  4. Create or update specs/GH<issue-number>/product.md.
  5. Keep the product spec focused on intended behavior and user-facing requirements. Use the shared skill's sections as the baseline, adapted to this repository and issue format. At minimum, cover:
    • summary
    • problem
    • goals
    • non-goals or scope boundaries
    • concrete user experience and behavior requirements
    • success criteria
    • validation
    • open product questions
  6. If design context such as a Figma link is present in the issue description or comments, include it. If no design context exists, make that absence explicit rather than silently omitting it.
  7. Do not include implementation details, file-level changes, or technical design. Those belong in the tech spec.
  8. Do not implement the feature or modify production code as part of this task. Limit changes to the product spec artifact. Treat temporary context files such as issue_comments.md as scratch input only and do not commit them.
  9. Do not include issue number references (e.g. (#N), Refs #N) in commit messages. The issue is already linked in the PR.
  10. If the prompt asks for it, write pr-metadata.json at the repository root containing a JSON object with the fields branch_name, pr_title, and pr_summary. The pr_summary should summarize the product and technical planning clearly enough that reviewers can use it directly as the PR body. For spec-only PRs, include a non-closing reference to the source issue such as Related issue: #<issue-number> rather than closing keywords like Closes or Fixes.
  11. Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI. If the prompt explicitly instructs you to publish a named branch, you may commit and push exactly the requested spec changes to that branch, but still do not open or update the pull request yourself unless the prompt explicitly asks for it.
  12. In your final response, provide a brief summary of the product spec and call out any assumptions or open questions so the workflow can reuse that summary when creating the PR.

Outputs

  • Leave the repository with the new or updated product spec file ready to be committed by the workflow.
  • When requested by the prompt, leave a ready-to-use pr-metadata.json with branch_name, pr_title, and pr_summary.
  • If the issue is underspecified, still produce the best possible product spec and clearly capture assumptions or open questions in the spec file and final response.

Frequently asked questions

What does the Create Product Spec AI skill do?

Create a product spec from a GitHub issue in this repository by applying the shared `write-product-spec` workflow with Oz-specific issue context and output paths. Use when an issue should be turned into a product spec artifact stored under `specs/GH<issue-number>/product.md` and the agent should prepare file changes only, without creating commits or pull requests itself unless a cloud workflow explicitly asks for it.

Why use Create Product Spec on TypingMind?

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

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

Which AI models can use Create Product 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 Create Product Spec?

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

Is the Create Product Spec 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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