ai-slop-audit

name	ai-slop-audit
description	Analyse, evaluate, and audit any artefact for AI slop and score it. Runs after EACH major iteration of work and AUTO-RUNS whenever the user asks to analyse, review, evaluate, audit, critique, or de-slop any project, app, website, business plan, SRS or spec, proposal, blog post, social post, document, image, or codebase, or asks whether something looks AI-generated. Produces a graded slop report giving per-marker findings with severity, evidence, and a concrete fix. Pairs with anti-ai-slop, which prevents slop during production.
metadata	{"portable":true,"compatible_with":["claude-code","Codex","codex","generic-agent"],"priority":"high","source":"digital-research-engine / ai-slop-detector (2026-06-07), verified per EVIDENCE-AUDIT.md"}

AI Slop Audit

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

The detector. Given any artefact, it decides how much it reads as AI slop, names exactly why, and says how to fix each finding. Production-side prevention is the companion anti-ai-slop skill.

Use When

• After EACH major iteration of work on the project at hand — a drafted section, finished feature/module, completed deck, significant revision, or milestone. Log the verdict; block progression on grade F.
• The user asks to analyse, review, evaluate, audit, critique, score, or de-slop any website, web/mobile app, business plan, SRS/spec, proposal/EoI, blog/article, social post, marketing copy, document, image/video, or codebase.
• The user asks "is this AI slop / does this look AI-generated / why does this feel off?".
• As the final gate before publishing or delivering any output.

Do Not Use When

• There is no concrete artefact to inspect yet — generate it first under anti-ai-slop, then audit.

Required Inputs

• The artefact itself (text, files, screenshots, repo path, or URL) and its claimed purpose/audience.
• For code: access to the dependency manifest so packages can be resolved against their registry.

When this runs

Cadence — run after EACH major iteration of work on the project at hand. This is the default mode: whenever a meaningful unit of work is completed — a drafted section, a finished feature or module, a completed slide deck, a significant revision, a milestone — run this audit on what was just produced before moving on. Log the verdict. If the verdict is F (Blocked), do not progress to the next iteration until the blocking findings are fixed. Treat it like a test suite that runs at every checkpoint, not a one-time final review.

Also auto-run on request: when the user asks to analyse, review, evaluate, audit, critique, score, or de-slop any of: a website, web/mobile app, business plan, SRS or technical spec, proposal/EoI, blog or article, social-media post or campaign, marketing copy, document, image/video, or codebase — or asks "is this AI slop / does this look AI-generated / why does this feel off?".

Also run as the final gate before publishing or delivering any engine output.

The companion anti-ai-slop skill runs continuously during generation; this audit runs at each checkpoint to catch what slipped through.

What slop is (the yardstick)

Low-quality content produced in quantity by AI and pushed at people who did not ask for it (Merriam-Webster 2025 WOTY, verified). Three diagnostic properties (Kommers et al., arXiv 2601.06060): superficial competence, asymmetric effort, mass producibility. The human tell: absence of intent. You are measuring how strongly an artefact exhibits these.

Audit method — layered, cheapest first

Step 1 — Identify artefact type and load the right checklist

Map the artefact to one or more domains: written content (EN/FR), UI/UX, app/product, image/video, code. A "project" (e.g. a website or app) usually spans several — audit each layer.

Step 2 — Automated gates (🤖, machine-checkable) — any hit is hard evidence

Run every applicable check; a hit on a blocking marker (✗) fails the artefact outright.

Written content

• 🤖 Focal-word density — delve/tapestry/realm/navigate/underscore/pivotal/intricate/leverage etc. >2 per 500 words.
• 🤖 Em-dash density >1 per paragraph; reflexive rule-of-three; "it's not X, it's Y" repetition; uniform 15-25-word sentences (low burstiness).
• 🤖 Transition clichés ("in today's fast-paced world", "let's dive in", "in conclusion").
• 🤖 Mechanical formatting: Title-Case headers, excess bold, decorative emoji, leftover tool markup ("oaicite", "contentReference").
• ✗ 🤖 Broken/fake citations: dead URLs, invalid DOI/ISBN, fabricated stats, utm_source params copied in.
• French: "plongeons dans", "il est important de noter que", "force est de constater", filler connectors.

UI/UX

• 🤖 Indigo/purple-gradient default (HSL 250-280°, sat 70%+); Inter/Roboto/Poppins-only; uniform border-radius; glassmorphism; gradient text; shadcn coloured card-border.
• ✗ 🤖 Dark-mode/body contrast <4.5:1 (WCAG fail).
• ✗ 🤖 Missing states (error/empty/loading/focus/disabled) — state-coverage audit.

Code

• ✗ 🤖 Hallucinated/uninstallable imports & packages (slopsquatting) — resolve every dependency against its registry. Package-hallucination is measured, not hypothetical: Spracklen et al. (USENIX Security 2025) found 19.7% of LLM-recommended dependencies did not exist.
• ✗ 🤖 Hardcoded secrets; SQL built by string interpolation; innerHTML = userInput (XSS); insecure defaults. AI-generated code fails security checks at scale: Veracode found 45% of AI code samples introduced a known vulnerability, rising to 86% for XSS and 88% for log injection.
• 🤖 Placeholder stubs/TODO/NotImplementedError/... in shipped code; dead code; duplication; bare-except; cross-language constructs; inconsistent style in one file; tautological tests (assert true). Copy-paste/duplication is rising in AI-assisted repos: GitClear measured cloned code blocks up from 8.3% (2020) to 12.3% (2024).

Image/video

• 🤖 Missing/contradictory C2PA provenance; SynthID absence (Google-only — absence != authentic); ELA/JPEG-forensics anomalies.

Step 3 — Structural score (🤖) → 0-100 "genericness"

Combine burstiness, focal-word density, duplication, and template-similarity into a single genericness score. Higher = more slop-like. Report the score and its drivers.

Step 4 — Human-judgement review (👁) — the checklist no tool replaces

• 👁 Substance: what does this assert/decide that required real work? If nothing — slop.
• 👁 Intent / authored voice: is there a point of view, or is it relentlessly positive and viewpoint-free?
• 👁 Specificity: real named examples/people/numbers, or generic placeholders?
• 👁 Hard parts: are errors, edge cases, risks, counter-arguments handled?
• 👁 Visuals: anatomy (hands/eyes/teeth), "AI sheen", garbled text-in-image, impossible geometry, video "boiling"/lip-sync.
• 👁 Product/app: wrapper with nothing added? fleeceware/dark-pattern billing? deceptive AI claims? fake reviews?
• 👁 Domain-specific (per artefact):
  • Business plan: fabricated market stats, generic TAM/SAM filler, no authored strategy, "studies show" without a named study.
  • SRS/spec: vague requirements, placeholder "Challenges and Future Prospects" sections, missing edge/error specs, hallucinated APIs.
  • Proposal/EoI: inflated superlatives, hollow analogies, unverifiable claims, no visible logic (evidence→warrant→implication).
  • Blog/social: engagement-bait, no lived experience, clichés, AI-sheen imagery.

Scoring & verdict

Aggregate into a grade:

Grade	Meaning	Trigger
A — Clean	No blocking hits; genericness low; substance & intent present	ship
B — Minor slop	A few automated hits, no blockers; some genericness	fix listed items
C — Slopy	Multiple automated hits or weak substance/intent	rework before ship
F — Blocked	Any ✗ blocker (hallucinated fact/citation/package, secret, WCAG fail, missing states) OR no substance at all	do not ship

Output format (the audit report)

# AI Slop Audit — <artefact name> — <date>
Verdict: <A/B/C/F>   Genericness score: <0-100>
Artefact type(s): <...>

## Blocking findings (✗) — must fix
- [marker] <what was found> · evidence: <quote/line/URL/screenshot ref> · fix: <concrete action>

## Slop findings (by severity)
- [marker] <finding> · evidence: <...> · fix: <...>

## What's good (so it isn't stripped in the fix)
- <substantive, specific, authored elements worth keeping>

## Recommended next step
- <rework / targeted fixes / ship>

Discipline (anti-hallucination — applies to the audit itself)

• Every finding cites concrete evidence from the artefact (a quote, a line number, a colour value, a screenshot region, a URL). No finding without evidence.
• Do not invent a flaw to pad the report. "This artefact is clean" is a valid, wanted verdict.
• Mark inferences "(inference)"; never present a guess as a measured fact.

See also

• anti-ai-slop — prevention companion (write/design/code so slop never appears).
• Host engine house-style skill — apply domain tone on top.