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Verify Prd

Community
nurettincoban
verify-prd

Review PRD.md for gaps, contradictions and unverifiable claims, write an improved PRD.md and record the findings in PRD-REVIEW.md.

Overview

Publishernurettincoban
Repositoryai-prd-workflow
Skill nameverify-prd
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 Verify Prd 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/verify-prd
  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/verify-prd .claude/skills/verify-prd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Prd 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 Verify Prd 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 Verify Prd 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 tasked with reviewing a Product Requirements Document (PRD). Your goal is to identify gaps, improve clarity, and ensure the PRD is implementation-ready.

Review PRD.md in the current directory and provide actionable feedback. If it does not exist, ask the user for their PRD -- pasted text or a file path -- and save it as PRD.md before proceeding.

Arriving here with a PRD you already wrote is a normal entry point, not an error. Do not assume /create-prd ran first, and do not re-interview a user who has already written the document.

CLASSIFY THE PRODUCT TYPE

Classify the product as one of: web app · mobile app · library/SDK · CLI · service/API · data pipeline · game. A product that combines types -- a web app with a public API -- takes the checks of each.

Then apply only the checks that fit. What each type needs probed, and what usually does not apply:

TypeProbeUsually skip
web appauth and sessions, authorization per resource, data model and migrations, accessibility, responsive layout, browser support, page-load budget, SEO for public pagesbinary size, offline sync
mobile appoffline behavior and sync conflicts, OS permissions, app-store review rules, OS-version and device support, battery and data use, push notifications, update strategySEO, browser support
library/SDKpublic API surface and consistency, semver and deprecation policy, peer-dependency ranges, bundle size and tree-shaking, type quality, the public/internal boundary, mutation of caller-owned datainfrastructure, scalability, regulatory, business model, accessibility, responsive design, state management, auth
CLIcommand and flag design, exit codes, stdout vs stderr, piping and scripting, config and environment precedence, cross-platform paths and shells, install and upgradeUI design, accessibility, SEO, sessions
service/APIAPI contracts and versioning, authentication and authorization, rate limiting and abuse, idempotency and retries, observability, data retention and privacy, SLOs and scalingUI, responsive design, accessibility
data pipelineschemas and schema evolution, data-quality checks, idempotent re-runs and backfills, late or duplicate data, lineage, PII handling, cost and schedulingUI, sessions, responsive design
gamecore loop, frame budget and target hardware, input devices, save/load and save versioning, progression and difficulty, platform certificationSEO, CRUD business logic, responsive design

Record the result in PRD.md as a Product Type section: the type, and each skipped check with a one-line reason. Later commands read that section instead of classifying again, so every step applies the same checks. Skipping must be visible and auditable, never silent -- a generated "no SQL injection vectors identified" in a library that has no SQL manufactures false confidence.

STEP 0: GROUND THE PRD IN REALITY

Before the gap analysis:

  • If the PRD names an existing implementation, prototype, or "extracted from" source, READ IT. Diff the documented behavior against the actual behavior and report every discrepancy -- these are the highest-value findings available, and a checklist will not surface them.
  • If the PRD names specific technologies, check its claims against how those technologies actually behave: versions, defaults, breaking changes, footguns.
  • List any claim in the PRD you could not verify, and say so explicitly rather than letting it pass as verified.

STEP 1: GAP ANALYSIS

Identify critical missing elements in these areas:

  1. PRODUCT FUNDAMENTALS

    • Product vision and problem statement
    • Target users and their needs
    • Success metrics and scope boundaries
  2. TECHNICAL REQUIREMENTS

    • Technology constraints and integrations
    • Security, performance, and scalability needs
    • Infrastructure requirements
  3. BUSINESS CONSIDERATIONS

    • Timeline and budget constraints
    • Regulatory requirements
    • Market factors and business model
  4. IMPLEMENTATION FACTORS

    • Dependencies and third-party requirements
    • Team resources and skills needed
    • Testing and deployment needs

STEP 2: IMPROVEMENT RECOMMENDATIONS

Provide specific recommendations in these areas:

  1. STRUCTURE & CLARITY

    • Ensure all essential sections are included
    • Clarify ambiguous requirements
    • Format user stories properly
  2. COMPLETENESS & FEASIBILITY

    • Fill gaps in user journeys
    • Identify technical challenges
    • Suggest alternatives for problematic requirements
  3. PRIORITIZATION & IMPLEMENTATION

    • Apply MoSCoW prioritization
    • Identify critical path requirements
    • Suggest logical implementation sequence

DELIVERABLES

  1. SUMMARY OF FINDINGS

    • List of critical gaps (High/Medium/Low impact)
    • 2-3 sentence overall assessment
  2. SPECIFIC RECOMMENDATIONS

    • Concrete suggestions for improvement
    • Examples of how to clarify ambiguous requirements
  3. IMPROVED PRD

    • Create an enhanced version addressing the issues found
    • Include the Product Type section (see CLASSIFY THE PRODUCT TYPE)
    • Give every functional and non-functional requirement a permanent ID (FR-1, NFR-1, ...) if it has none, and never renumber existing ones -- features and RFCs cite them
    • Keep the PRD's Decisions section, and add every decision made while resolving these findings -- what was decided, why, and what was rejected. Add the section if the PRD has none
    • Save as "PRD.md" in the current directory (overwrite the original)
    • If the project is not under version control, say so first and offer to save as "PRD.v2.md" instead -- overwriting a hand-written PRD with no way to recover it destroys the diff the user needs in order to review what you changed
  4. QUALITY ASSESSMENT

    • Score the PRD (1-10) on: Completeness, Clarity, Feasibility, and User-Focus
  5. PRD-REVIEW.md

    • Write deliverables 1, 2 and 4 to "PRD-REVIEW.md" alongside the improved PRD: the gap list, the recommendations, the scores, and why each change was made
    • Findings that live only in this conversation are gone the moment it ends. The next reader then sees a decision in PRD.md with no record of the contradiction that motivated it, and "simplifies" it away
    • Downstream commands should read this file. Every High-impact finding must stay traceable into FEATURES.md and the RFCs

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 Verify Prd AI skill do?

Review PRD.md for gaps, contradictions and unverifiable claims, write an improved PRD.md and record the findings in PRD-REVIEW.md.

Why use Verify Prd on TypingMind?

Because you install it once and use it with any model. Verify Prd 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 Verify Prd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nurettincoban/ai-prd-workflow/tree/main/skills/verify-prd. 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 Verify Prd?

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 Verify Prd?

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

Is the Verify Prd 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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