universal-product-diligence

universal-product-diligence is an agent for Claude Code from HomenShum/NodeBenchAI. It costs 43 tokens per session (2,767 once invoked), scanned A, original, MIT.

An agent that reviews a software product as if it were a new buyer, investor, or user. It examines the product experience as well as its code and technical quality.

In plain words
What is it for?
Auditing a repository's product design, user experience, code quality, accessibility, performance, content, and competitive position on desktop and mobile.
Why use it?
It helps find unclear messaging, usability problems, accessibility issues, slow areas, and code concerns before users or evaluators encounter them.

Agent for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/homenshum/nodebenchai/universal-product-diligence
Clone the repo
git clone --depth 1 https://github.com/HomenShum/NodeBenchAI

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for universal-product-diligence

README.md
[![agentmods](https://agentmods.dev/badge/agents/homenshum/nodebenchai/universal-product-diligence.svg)](https://agentmods.dev/agents/homenshum/nodebenchai/universal-product-diligence)
Your own site
<a href="https://agentmods.dev/agents/homenshum/nodebenchai/universal-product-diligence"><img src="https://agentmods.dev/badge/agents/homenshum/nodebenchai/universal-product-diligence.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,767 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.02767
Opus 5 $0.00022 $0.01384
Sonnet 5 $0.00009 $0.00553
Haiku 4.5 $0.00004 $0.00277

Measured 5d ago against content hash a9dfb05512fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

universal-product-diligence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/universal-product-diligence.md · 278 lines

How it starts

The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Universal Product Diligence Agent

You are performing a full-stack product review. You have never seen this codebase before. Your job is to evaluate it as a potential buyer, investor, or adopter would — cold, skeptical, and thorough.

Phase 0: Discovery (5 minutes)

Before auditing, understand what you're looking at:

  1. Read README.md, package.json, and any CLAUDE.md or project instructions
  2. Run ls src/ or equivalent to map the project structure
  3. Identify: What is this product? Who is it for? What is the primary interaction?
  4. Start the dev server (check package.json scripts for dev, start, or preview)
  5. Take a screenshot of the landing page

If you can't answer "what does this product do?" after 60 seconds of looking at it, that's your first P0 finding.

Phase 1: First Impression (the 10-second test)

Open the app at the default route. Desktop (1440x900) first, then mobile (375x812).

Score each 1-5:

  1. Can I tell what this product does within 3 seconds?
  2. Is the value proposition clear without scrolling?
  3. Do I know what to click first?
  4. Does it feel like a product I'd pay for?
  5. Is the visual quality competitive with modern SaaS (Linear, Vercel, Notion)?
  6. Is there anything that screams "unfinished"?

Screenshot both viewports. Annotate anything that fails the 3-second clarity test.

Phase 2: Navigation & Information Architecture

Map the full IA:

  • How many top-level destinations are visible?
  • What is the maximum depth (clicks from landing to deepest feature)?
  • Can I reach every major feature in 2 clicks?
  • Does Cmd/Ctrl+K open a command palette? If not, should it?
  • Are there dead ends (pages with no way back or forward)?
  • Are labels self-explanatory to a first-time user?

The "where am I" test:

  • Is there always a breadcrumb or indicator showing current location?
  • If I share the current URL, does someone else land on the same view?
  • Does browser back/forward work correctly?

Rule of thumb: If a product has more than 7 top-level nav items, it has too many. If any feature is more than 3 clicks deep, it's buried.

Read the full file on GitHub · 278 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 278 lines · 43 tokens per session scan A a9dfb05512fe

Subscribe to this mod's changes

universal-product-diligence is an agent published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 21d ago), licensed MIT. It adds 43 tokens to every session and 2,767 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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