Ai Product Audit

AI Product Audit

Use this skill to diagnose whether an AI-built product follows proven product patterns and converts inspiration into action.

product category + scenario -> pattern matching -> scenario audit -> conversion surface audit -> business reality check -> issue cards -> regression check -> lessons

This skill must not judge a product by visual polish alone. It asks whether the page prepares the user for a believable next step and provides a path to reach it.

When To Use

1. The target is a lifestyle, service, commerce, creator, SaaS, portfolio, directory, or dashboard product.
2. /audit or /visual-qa flags a pattern mismatch or vague value proposition.
3. The user wants to know whether the page converts inspiration into action.
4. Batch-auditing multiple sites for product-pattern fitness.
5. Before declaring a page "conversion-ready" or "shippable."

Core Rules

1. Separate product-pattern evidence from visual evidence — a polished page can still have a broken scenario.
2. Compare against the proven pattern for the product category, not personal taste.
3. Every finding needs three things: expected pattern, observed gap, and business risk.
4. Use S0-S4 severity mapped to delivery and conversion risk, not subjective preference.
5. Preserve the shared output shape: Scope, Evidence, Findings, Severity, Reproduction, Fix Suggestion, Regression Check, Lessons.
6. For batch audits, emit a summary table first, then progressive per-site details.
7. Mark payment, irreversible submission, and production mutation as SKIPPED-SAFE unless explicitly allowed.
8. Never claim a product "understands its user" without citing a specific page element and its failure.

Workflow

1. Intake and scope: identify product category, intended scenario, business outcome, conversion surfaces, and audit depth.

• Use references/product-pattern-rubric.md for the full dimension list.
• Use references/category-pattern-catalog.md for category-specific pattern expectations.

1. Surface and pattern check: discover the visible page surface; compare each page against its category pattern.

• Apply the permission model before any click, form fill, or authenticated action.
• Mark pages or flows that cannot be safely tested as SKIPPED-SAFE.

1. Scenario audit: ask the four Viba-inspired questions for each key page:

• What scenario is this page preparing the user for?
• What self-image, business outcome, or action does it help the user move toward?
• Can the user see themselves in the next step?
• Is the page only inspiration, or does it convert inspiration into action?

1. Conversion surface audit: for each identified CTA, form, booking flow, checkout, or signup path:

• Is the primary CTA specific and actionable?
• Does the page contain a working conversion surface (not just a brochure)?
• Is there a visible path from inspiration to action in fewer than 3 clicks?

1. Business reality check: distinguish real products from templates.

• Is there operational depth (backend, database, CMS, auth, content system)?
• Is there a monetization path or demonstrated usage?
• Does the evidence (source, live, or physical) support a real business claim?

1. Evidence assembly and output: produce issue cards, pattern-fit table, and copyable fix prompts.

• Use the shared output shape for every finding.
• Include a Pattern Fit table and a Scenario Audit table.
• Bundle fix prompts so the user can copy them directly into Claude Code, Lovable, v0, or Bol.

1. Regression and lessons: convert repeated pattern failures into guardrail updates or benchmark labels.

• Propose updates to CLAUDE.md only when the pattern appears in 3+ audits with clear evidence.
• Append lessons to the audit ledger in validation/ for future five-pass reviews.

References

• references/product-pattern-rubric.md
• references/category-pattern-catalog.md
• ../audit/references/progressive-reporting.md (for batch audits and multi-step runs)
• ../visual-qa/references/aesthetic-quality-audit.md (for pattern reference and AI slop signals)
• ../audit/references/permission-model.md (before any live or authenticated action)

Output Format

AI Product-Pattern Audit Summary
Target:
Product Category:
Intended Scenario:
Pattern Fit Score:
Main Business Risk:
Fix First:

Pattern Fit Table
| Expected Pattern | Observed | Gap | Risk | S0-S4 |
| --- | --- | --- | --- | --- |

Scenario Audit
| Question | Answer | Evidence | Risk |
| --- | --- | --- | --- |

Conversion Surface Map
| Surface | Present | Actionable | Evidence |
| --- | --- | --- | --- |

Business Reality
| Signal | Present | Evidence |
| --- | --- | --- |

Issue Cards
 - 
- Area:
- URL:
- Live position:
- Expected pattern:
- Observed:
- Business risk:
- Fix:
- Copy prompt:
- Regression check:

Copyable Fix Pack
1. 
2. 
3. 

Lessons

Anti-Patterns

1. Judging product quality by visual polish alone — visual QA and product-pattern audit are different dimensions.
2. Applying SaaS patterns to a local service site, or portfolio patterns to a commerce site.
3. Treating "vibe" or "mood" as a substitute for scenario clarity.
4. Missing the "next step" test — if the user cannot describe what happens after clicking, the scenario is broken.
5. Batch-auditing without first categorizing each site — mixed-category batches produce misleading summaries.
6. Claiming a page "converts" because it has a CTA — the CTA must be specific, actionable, and lead to a working next step.
7. Using product-pattern findings to rewrite copy subjectively — always tie the fix to a pattern mismatch, not a taste preference.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: timeyour
• Source: timeyour/agentskills-audit-collection
• License: MIT
• Homepage: https://github.com/timeyour/agentskills-audit-collection

Install and usage instructions live in the source repository linked above.