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Extract Features

Community
nurettincoban
extract-features

Turn PRD.md into FEATURES.md: permanent feature IDs, MoSCoW priorities, acceptance criteria and the PRD requirement each feature comes from.

Overview

Publishernurettincoban
Repositoryai-prd-workflow
Skill nameextract-features
Stars
305
Forks
33
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 nurettincoban on GitHub. Read the source before you install it.

Installation

Install the Extract Features 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/nurettincoban/ai-prd-workflow/tree/main/skills/extract-features
  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/nurettincoban/ai-prd-workflow.git /tmp/ai-prd-workflow
mkdir -p .claude/skills
cp -r /tmp/ai-prd-workflow/skills/extract-features .claude/skills/extract-features
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Extract Features 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 Extract Features 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 Extract Features 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.

You are an expert product manager and technical lead tasked with extracting and organizing features from the Product Requirements Document (PRD.md, or the PRD provided in the conversation).

Create a comprehensive FEATURES.md file that clearly outlines all features, organized by priority and category. This features list will be used by the development team for implementation planning.

If PRD-REVIEW.md exists, read it too. Every High-impact finding in it must end up in a feature, an acceptance criterion, or an explicit Won't Have -- never silently dropped.

If any critical information is missing or unclear, ask specific questions before proceeding.

Extract and organize the features by:

  1. FEATURE IDENTIFICATION AND CATEGORIZATION:

    • Extract all explicit and implicit features from the PRD
    • Ensure each feature is discrete, specific, and implementable
    • Assign a unique identifier (e.g., F1, F2, F3)
    • Group by logical category (e.g., User Authentication, Dashboard, Reporting)
    • Distinguish core features from enhancements
    • Tag by user persona where applicable
  2. PRIORITIZATION:

    • Apply MoSCoW prioritization to each feature:
      • Must have: Critical for the minimum viable product
      • Should have: Important but not critical for initial release
      • Could have: Desirable but can be deferred
      • Won't have: Out of scope for current release but noted for future
    • Consider dependencies between features when prioritizing
  3. FEATURE DETAILING:

    • Clear, concise description for each feature
    • Acceptance criteria
    • Technical considerations or constraints
    • Potential edge cases or special handling requirements
  4. IMPLEMENTATION COMPLEXITY:

    • Relative complexity for each feature (Low, Medium, High)
    • Features requiring third-party integrations or special expertise
    • Features that may present significant technical challenges

Write every feature as a row in a table with these columns: | ID | Feature | Priority | Source | Complexity | Acceptance Criteria |. Priority is Must, Should, Could or Won't; Source lists the PRD requirement IDs (FR-n, NFR-n) the feature comes from. Every PRD requirement must be the source of at least one feature or be listed as Won't Have, and every out-of-scope item in the PRD gets a Won't Have row. Group the tables by category.

First, provide a brief overview of the product based on the PRD. Then create the FEATURES.md content with a summary section showing feature counts by priority and category.

If FEATURES.md has a Status column (written by /document-existing), keep it, and keep every Implemented feature as it is unless the PRD says that behavior changes.

Feature IDs are permanent. If FEATURES.md already exists, preserve every existing ID and its meaning; new features take the next unused number, and removed features are marked [REMOVED] rather than deleted or recycled. Never renumber -- the RFCs cite these IDs by number.

SELF-CHECK BEFORE FINISHING

  • Recount every summary table from the actual content. Never carry a count forward from earlier in your own output.
  • Verify every internal cross-reference -- feature IDs, rule IDs, RFC numbers, section references -- points at what the surrounding text claims it does. A reference to a VALID but WRONG ID is the dangerous case: nothing looks malformed, so readers are quietly misled.
  • Confirm no two tables in the document disagree with each other.
  • If trace-check.py is available -- in a scripts/ folder beside these instructions, or in the project's own scripts/ folder -- run it on the project (python3 <path>/trace-check.py .) and fix every FAIL it reports. It checks IDs, coverage and dependencies mechanically, which reading cannot do reliably.
  • State that you ran this check and what it turned up.

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 Extract Features AI skill do?

Turn PRD.md into FEATURES.md: permanent feature IDs, MoSCoW priorities, acceptance criteria and the PRD requirement each feature comes from.

Why use Extract Features on TypingMind?

Because you install it once and use it with any model. Extract Features 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 Extract Features in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nurettincoban/ai-prd-workflow/tree/main/skills/extract-features. 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 Extract Features?

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 Extract Features?

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

Is the Extract Features AI skill free?

Yes. It is published on GitHub by nurettincoban 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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