ProductRubric

Judge the work against the same standard.

ProductRubric helps product teams make better decisions about the work in front of them. Whether a design is ready to ship, whether a survey can be trusted, whether findings are strong enough to act on: those calls usually rest on whoever happens to review the work, using whatever criteria they improvise in the moment.

ProductRubric is an open library of evaluation rubrics that replaces the improvisation. Your AI assistant applies a rubric to the work itself, a screen, a design board, a study plan, a questionnaire, or a readout, and returns a structured review, the same shape of review in Claude, ChatGPT, Grok, Cursor, or any other tool, every time.

Connect the MCP server

The whole library is served over MCP. Add this address to your AI tool and it can list the rubrics, fetch the one you ask for, and apply it in your chat. No account and no key.

https://mcp.productrubric.com

Where the address goes

Claude

Settings, Connectors, Add custom connector. Name it ProductRubric and paste the address.

ChatGPT

Settings, Connectors, then add a custom connector and paste the address. On paid plans, developer mode needs to be on, under Advanced settings.

Grok

Go to grok.com/connectors, choose New Connector, then Custom, and paste the address. In Grok Bot, you can also send the bot “Add this MCP server: https://mcp.productrubric.com” in chat.

Cursor

Settings, MCP, Add new global MCP server. Paste the address as the server URL.

Anything else, including Muse and Claude Code

Ask your AI to add this server for you. Agent tools can do this themselves. If yours keeps connectors in its settings instead, it will walk you through the same two steps: add a custom connector, paste the address.

See it run

A rubric, running in a chat

This is the whole product in one exchange, adapted from the library’s worked example: the Survey Review rubric run on the sample questionnaire in the repository.

Your AI assistant, with ProductRubric connected
day-30-survey.pdf
get_rubric · survey-review

Survey Review. 12 questions reviewed: 1 invalidating, 8 biasing, 1 burden, 2 sound.

Q1 (Invalidating): “How much do you love using Ledgerline?” presupposes the feeling it claims to measure, so the survey’s headline number is manufactured. Suggested revision: “How satisfied are you with Ledgerline so far?”, on a balanced scale.

Q6 (Biasing): “Do you agree that our pricing is unfair for freelancers?” plants its conclusion. Suggested revision: “How fair do you think Ledgerline’s pricing is for freelancers?”

Highest impact fix: replace Q1. As written, the result the team will quote most is the one the question invented.

Every flawed question gets a rating, the criterion it breaks, and a rewritten version ready to drop into the survey.

The idea

What is a rubric?

A rubric is a review written down before the work arrives: the criteria to judge against, the scoring to use, and the format the verdict comes back in. Product teams already work from rubrics informally, in critique habits, heuristic evaluation, and survey methodology. Those traditions are frameworks. A rubric takes a framework, an established one or your own, and turns it into instructions an AI can follow consistently, so the standard survives the person who brought it.

Each rubric here is one plain Markdown file, and each one also states plainly what it cannot judge.

Who it is for

Who runs a rubric, and when

Product designers

Run one on a screen or a flow before a crit, before a design review, or at handoff, and walk in already knowing where the work is weak and where it holds up.

UX researchers

Run one on the study plan before recruiting starts, on the questionnaire before it fields, and on the readout before it goes to stakeholders. The cheap moment to catch a leading question is before anyone has answered it.

Product managers and product owners

The ship decision, and the call on whether findings are strong enough to act on, usually land on your desk. Run the same rubric the team ran and you see the design or the readout the way its toughest reviewer would, before the meeting where the decision gets made.

Design and research leads

Your standards stop living in your head and in old feedback threads. Written down as rubrics, they are applied the same way in every review, whoever runs it and whichever AI they use.

Small teams and solo builders

The criteria a staff designer or a research lead would apply, available in the room even when there is no staff designer or research lead in it. If you are also the one building, that covers the Accessibility Review on the interface you shipped and the Copy Review on the error messages and empty states nobody was assigned, because on a small team that person is you.

The library, product design

Product design

Run one before a design ships, before a crit, or at handoff. Select a rubric for its full description.

Simple
One screen and one narrow question. Quick to run, on any current model.
Standard
One screen or flow, judged against structured criteria, with a fuller report.
Deep
Needs more than the artifact itself, such as a full board with its rationale, or context the rubric asks you for before it judges.
Design Principles Score Standard Scores a screen or flow against seven product design principles.

Scores a screen or flow against seven product design principles, with findings tied to visible elements.

When to use it: A general design critique of a screen or flow, when you want a scored review rather than open-ended comments.

What you provide: A screenshot of the screen or flow, or a description that names the visible elements and their text.

How it reports: 1-5 per principle; overall 0-100

Inclusive Design Audit Standard Audits a screen or flow against five inclusive design principles for cultural fit.

Audits a screen or flow against five inclusive design principles for cultural fit, checking who the design includes and who it unintentionally leaves out.

When to use it: An inclusion review of a screen or flow: representation, cultural fit, access, language, and user control, judged from what is visible in the design.

What you provide: A screenshot of the screen or flow, or a description that names the visible elements, imagery, and text.

How it reports: 0-2 per principle; overall 0-10

Accessibility Review Standard Reviews a screen’s design for accessibility problems visible in a screenshot or description.

Reviews a screen’s design for accessibility problems that are visible in a screenshot or description, judged against criteria derived from WCAG 2.2.

When to use it: At design time, before a screen is built, to catch the accessibility problems a picture of the design can already show, so they are fixed in the design instead of discovered in the code.

What you provide: A screenshot of the screen at a realistic size, or a description that names the text, its colors, and the controls. State the platform (web, iOS, Android, desktop) when it is known, because target and text expectations differ by platform.

How it reports: No overall score; each finding rated Blocker, Serious, Moderate, or Minor; criteria that cannot be judged from the input are reported as Cannot judge

Copy Review Standard Reviews every piece of text on a screen and suggests a better revision for each one.

Reviews every piece of text on a screen and returns a revision for each one that fits the same space and does the same job better.

When to use it: Before design handoff, when a screen’s words need a dedicated pass: headlines, buttons, labels, empty states, and error text that were written in the layout and never reviewed as writing.

What you provide: A screenshot of the screen, or the screen’s text supplied directly (a copy list, a transcript of the design, or the strings from the product). If text is supplied directly, say so in the result.

How it reports: None

Five Second Test Simple Predicts what a first-time visitor remembers after five seconds, and what they miss.

Predicts what a first-time visitor will remember after seeing a screen for five seconds, and which notable elements they will miss.

When to use it: A first-impression check on a landing page, a hero section, an app screen, or any single screen whose job is to land its message and its main action within seconds.

What you provide: A screenshot of one screen at its full bounds, or a description that names the visible elements and their text. One screen per run.

How it reports: Memorability 0-10; simulated panel of 5; 3-of-5 rule

Attention Prediction Simple Predicts the three places a viewer’s eye lands first on a screen.

Predicts the three places a viewer’s eye will land first on a screen, in order, with positions a tool can draw as an overlay.

When to use it: To check whether a screen’s visual hierarchy sends attention where the design intends, especially when something decorative might be out-pulling the headline or the main action.

What you provide: A screenshot of the screen (required for coordinates). Optionally, the intended attention path: the elements the design wants seen first, second, and third. A text description alone can support the ranked prediction but not the coordinates; say so and omit them.

How it reports: Exactly 3 hotspots; intensity High 70-100, Medium 40-69, Low 20-39

Design Quality Deep Reviews a design board with its rationale against seven dimensions of design quality.

Reviews a design submission, a multi-frame board with its rationale, against seven dimensions of professional design quality, the way a design leader would in a critique.

When to use it: Before a design critique, a handoff, or a portfolio review, when the question is not only whether the screens work but whether the work itself is good: well aimed, well grounded, well built, and well explained.

What you provide: The design as a multi-frame board (a board overview plus the frames in reading order), with whatever rationale the designer has written on the board or alongside it. A single screen can be submitted, but the dimensions that judge intent and evidence will usually be unjudgeable from a screen alone.

How it reports: 1-5 per dimension; overall 0-100 across the dimensions that could be judged

The library, product research

Product research

Run one before a study is fielded, or before findings are presented to stakeholders. Select a rubric for its full description.

Study Plan Review Deep Reviews a study plan before it is fielded and judges whether it is ready.

Reviews a research study plan before it is fielded, judging whether the questions are answerable, the method fits them, and the participants and materials will produce evidence the team can act on.

When to use it: Before recruiting begins for an interview study, a usability study, a diary study, or any planned piece of product research, when the cost of fixing the plan is still low and the cost of fielding a flawed one is not yet paid.

What you provide: The study plan as written: its goals or research questions, the method, the participant definition and screening approach, and whatever session structure, tasks, or materials the plan specifies. A short informal plan is a valid input; the review judges what is there against what the study needs.

How it reports: No numeric score; each criterion judged Ready, Needs work, or Missing, with an overall readiness verdict

Survey Review Standard Reviews a questionnaire question by question and rewrites the flawed ones.

Reviews a survey questionnaire question by question, finding the wording, scale, and ordering flaws that bias answers or make them impossible to give, and suggesting a corrected version of each flawed question.

When to use it: Before a survey is sent, while the questionnaire can still be changed: for satisfaction surveys, screening surveys, product feedback surveys, and any questionnaire whose results will be quoted as evidence.

What you provide: The full questionnaire text: the introduction shown to respondents, every question with its answer options and scales in order, and any skip logic described. The survey’s purpose and audience, when known, sharpen the review; without them the review judges craft alone and says so.

How it reports: No overall score; every flaw rated Invalidating, Biasing, or Burden, with a suggested revision for each flawed question

Findings Review Standard Reviews a readout and checks whether every claim traces to evidence.

Reviews a research readout, the report or summary stakeholders will read, judging whether every claim traces to evidence, whether observation and interpretation are kept apart, and whether recommendations stay proportional to what the study can support.

When to use it: Before a research readout is circulated: the findings deck, the summary document, or the report a team will use to decide something, when the question is whether a reader can trust what it claims.

What you provide: The readout itself, in full: claims, quotes, numbers, and recommendations as the audience will see them. Access to the underlying evidence (session notes, recordings, response data) strengthens the review when available; without it, the review judges the readout’s internal support and labels every claim check accordingly.

How it reports: No numeric score; each criterion judged Holds, Needs work, or Missing, plus a claim-by-claim audit and a reliance verdict

The project

Open, and growing

ProductRubric is an open project. The library is created and maintained by David Nguyen, and it grows through contributions. If your team has a review it runs again and again, you can write it as a rubric in the shared format and propose it to the library. Rubrics based on a published framework stay credited to that framework’s author, and contributed rubrics carry their writer’s name.

Getting started

Three ways to use the library

Use the files

Open any rubric from the repository, give it to your AI with the work, and ask for the evaluation. Works today, with no setup.

Get the files on GitHub

Install the Skills

Each rubric also ships as an installable Skill, so your assistant can pick the right evaluation on its own.

Get the Skill pack

Connect the MCP server

Point your AI tool at the hosted server above, or run the same server locally against your own folder of rubrics.

Get the server address

DESIGN.md tells an AI how to create the design. PRODUCTRUBRIC.md tells an AI how to judge it.

A rubric standardizes the questions, the criteria, and the shape of the report. It cannot standardize the judgment inside the answers, which depends on the model you run and the material you give it. Treat a result as a structured review by a capable reader, not as a measurement.

FAQ

Questions

Is ProductRubric an AI tool that reviews my work for me?

No. ProductRubric contains no AI. It is a library of written standards. Your own AI assistant does the reviewing, applying the rubric you choose, and the material you evaluate never passes through ProductRubric.

Which AI assistants does it work with?

Any assistant that can read text or connect to an MCP server, including Claude, ChatGPT, Grok, and Cursor. The product design rubrics need an assistant that can see images. The product research rubrics judge documents, so any text assistant can run them.

Do I need an account or an API key?

No. The hosted MCP server is open, the files are on GitHub, and there is nothing to sign up for.

Does ProductRubric see my designs or my data?

No. The server only sends rubric text out. Your screenshots, boards, and documents stay in your own chat with your own AI provider.

How is this different from asking an AI for a critique directly?

A direct ask improvises the criteria in the moment, so the same work gets a different review every time. A rubric fixes the criteria, the scoring, and the report format in writing, so reviews are consistent, comparable across tools and across time, and honest about what they could not judge.

What does it cost?

Nothing. The rubric files are licensed CC BY 4.0 with credit to their authors, and the server code is Apache 2.0.

Can I write a rubric for my own team?

Yes. The format is documented in the repository. You can run your own rubrics locally through the same MCP server, install them as Skills, or propose them to this library.