frontend-slop-audit
Skill1 starsChanged 2 months agoMITView on GitHub
Audit generated web UI for AI-generated tells and ship-blocking layout defects. TRIGGER when: reviewing a landing page, portfolio, or marketing site before shipping; user asks why a page "looks AI-generated", "looks templated", or "looks generic"; or asks for an anti-slop pass, taste pass, or pre-flight check on frontend output. DO NOT TRIGGER when: making design-system decisions such as tokens, palettes, type scales, or motion architecture (use design-ux skill), reviewing code correctness or security (use code-review skill), generating favicons or social images (use web-asset-generator skill), working on terminal/TUI interfaces (use design-ux skill), or fixing the flagged page rather than auditing it (use impeccable skill if installed, else design-ux skill).
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 81
Countable before subjective: run the mechanical checks first.
- Distinctive: line 83
Empirical, not eternal: every tell carries a date stamp and an expiry expectation
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
frontend-slop-audit
Audit an already-built page for the patterns that mark it as machine-generated. This is a review pass, not a design pass: it takes existing output and returns a list of specific, located defects.
Two kinds of finding. Hard rules are countable or measurable -- a wrapped CTA, a two-line nav, five eyebrows across seven sections -- and failing one means the page is broken, not merely unfashionable. Tells are patterns that are individually defensible but appear so reliably in generated output that their presence identifies the author. Both are empirical, mined from repeated production failures rather than derived from principle, which is also why they need re-verification over time.
What You Get
- A catalog of AI-generated visual, copy, and structural tells with concrete replacements
- Mechanically checkable layout rules (hero fit, eyebrow ratio, bento cell count, CTA discipline)
- An 8-group pre-flight checklist ordered cheapest-check-first
- Contrast and consistency-lock verification steps that catch defects static review misses
- An optional automated pre-scan via the Impeccable detector, merged into the same findings list
- A reporting format that produces located, actionable findings
When to Use
- Auditing a generated landing page, portfolio, or marketing site before shipping
- Diagnosing why a page "looks AI-generated" or "looks templated"
- Running a final pre-flight pass over frontend output
- Reviewing a redesign against the page it replaced
- Establishing ship criteria for machine-generated UI
When NOT to Use
- Design decisions (tokens, palettes, type scales, grids, motion architecture) -- use
design-ux - Code correctness, security, blast radius -- use
code-review - Favicons, OG images, icon sets -- use
web-asset-generator - Terminal and TUI interfaces -- use
design-ux, which owns the monospace side - Dashboards, data tables, multi-step forms -- these rules target marketing and portfolio surfaces; product UI has different density constraints. Use
impeccable(/impeccable critiqueor/impeccable audit) when installed - Fixing the findings -- this skill returns defects; hand the fix pass to
impeccable(/impeccable polish) when installed, elsedesign-ux
Reading Guide
| Working on | Read |
| Running the audit end to end | pre-flight-check.md |
| Visual, copy, and decoration tells | ai-tells.md |
| Countable layout defects, hero, nav, CTA, contrast | layout-hard-rules.md |
See also
design-ux-- for the design decisions this skill audits againstcode-review-- for correctness, security, and blast radiusweb-asset-generator-- for producing the favicons and social images this skill expects to existplaywright-- for driving the page to verify rendered contrast and viewport behavior
Common Pitfalls
| Mistake | Why It Fails | Better Approach |
| Reporting a pass/fail verdict | Gives the author nothing to act on | Report each failure with file, line, and fix |
| Listing rules that passed | Buries the findings in noise | Report only failures |
| Treating tells as universal law | These are 2026 defaults, not design truths | Re-verify annually; drop entries that stop being defaults |
| Applying marketing rules to product UI | Density constraints are genuinely different | Scope the audit to marketing and portfolio surfaces |
| Assuming contrast from token names | bg-white plus text-white both look intentional | Check computed values against the rendered background |
| Rewriting the page | The ask is an audit, not a redesign | Return findings; let the author decide the fix |
| Flagging every em-dash in source | The rule is about rendered page text | Check visible strings, not code comments |
Key Conventions
- Countable before subjective: run the mechanical checks first; they need no judgment and catch the most
- Located findings only: a rule cited without a line number is not a finding
- Empirical, not eternal: every tell carries a date stamp and an expiry expectation
- Audit, do not redesign: this skill returns a defect list, not new markup
- Static is a valid outcome: a page with no motion passes the motion group; half-built motion does not
Attribution
The tell catalog, layout rules, and checklist are adapted from Leonxlnx/taste-skill (MIT, upstream commit ccbc156), restructured for progressive disclosure and scoped to the audit job. Full notice in THIRD-PARTY-NOTICES.md at the repository root.
In this repository61 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
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- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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