ai-slop-audit

name	ai-slop-audit
description	Use when auditing a concrete artefact for AI slop after a major iteration or before release, with evidence-backed findings, fixes, genericness score, and A/B/C/F verdict; use anti-ai-slop during production rather than for retrospective grading.
metadata	{"portable":true,"compatible_with":["claude-code","codex"],"priority":"high","source":"ai-slop-detector research (2026-06-07), verified"}

AI Slop Audit

The detector. Given any artefact, it decides how strongly it reads as AI slop, names exactly why, and says how to fix each finding. Prevention is the companion anti-ai-slop skill; truthfulness is source-evaluation.

When this runs

Cadence — run after EACH major iteration of work on the project at hand. This is the default mode: when a meaningful unit is completed — a drafted report section, a finished cohort, a synthesis pass, a generated .docx, a significant revision — audit 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.

Also auto-run on request: when the user asks to analyse, review, evaluate, audit, critique, score, or de-slop any report, dossier, document, website, app, business plan, SRS/spec, proposal, blog/article, social post, image/video, or codebase — or asks "is this AI slop / does this look AI-generated?".

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

The companion anti-ai-slop runs continuously during writing; 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 Word of the Year, verified). Three diagnostic properties (Kommers et al., arXiv 2601.06060, verified): superficial competence, asymmetric effort, mass producibility. The human tell: absence of intent.

Audit method — layered, cheapest first

Step 1 — Identify artefact type and load the right checklist

Map the artefact to one or more domains: research report/document (written EN/FR), UI/UX, app/product, image/video, code. A project (website, app, dossier) usually spans several — audit each layer.

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

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

Written content / reports

• [auto] Focal-word density — delve/tapestry/realm/navigate/underscore/pivotal/intricate/leverage etc. >2 per 500 words.
• [auto] 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).
• [auto] Transition clichés ("in today's fast-paced world", "let's dive in", "in conclusion").
• [auto] Mechanical formatting: Title-Case headers, excess bold, decorative emoji, leftover tool markup ("oaicite", "contentReference").
• [BLOCK] [auto] Broken/fake citations: dead URLs, invalid DOI/ISBN, fabricated stats, "studies show" with no named study, source missing at point of claim.

UI/UX

• [auto] Indigo/purple-gradient default (HSL 250-280deg, sat 70%+); Inter/Roboto/Poppins-only; uniform border-radius; glassmorphism; gradient text; shadcn coloured card-border.
• [BLOCK] [auto] Body/dark-mode contrast <4.5:1 (WCAG fail); missing states (error/empty/loading/focus/disabled).

Code

• [BLOCK] [auto] Hallucinated/uninstallable imports & packages (slopsquatting) — resolve every dependency against its registry.
• [BLOCK] [auto] Hardcoded secrets; SQL by string interpolation; innerHTML = userInput (XSS); insecure defaults.
• [auto] Placeholder stubs/TODO/NotImplementedError in shipped code; dead code; duplication; bare-except; tautological tests (assert true).

Image/video

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

Step 3 — Structural score ([auto]) -> 0-100 "genericness"

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

Step 4 — Human-judgement review ([human]) — the checklist no tool replaces

• [human] Substance: what does this assert/decide that required real work? If nothing — slop.
• [human] Intent / authored judgement: is there a stated analytic point of view, or viewpoint-free summary?
• [human] Specificity: real named sources, figures, dated events — or generic placeholders?
• [human] Hard parts: counter-case, limitation, contradicting source, gap addressed?
• [human] Domain-specific:
  • Research report/dossier: claims sourced at point of use; analytic judgement per section; gaps marked, not filled with plausible filler.
  • Business plan: fabricated market stats, generic TAM/SAM filler, no authored strategy.
  • 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.
  • Blog/social: engagement-bait, no lived experience, clichés, AI-sheen imagery.

Scoring & verdict

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

Output format (the audit report)

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

## Blocking findings ([BLOCK]) — must fix
- [marker] <finding> · evidence: <quote/line/URL/colour/region> · fix: <action>

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

## What's good (keep in the fix)
- <substantive, specific, authored elements worth keeping>

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

Discipline (the audit itself must not hallucinate)

• Every finding cites concrete evidence from the artefact (a quote, line number, colour value, region, URL). No evidence, no finding.
• Do not invent a flaw to pad the report. "This artefact is clean" is a valid, wanted verdict.
• Mark inferences "(inference)".

See also

Use When

Use after a concrete iteration exists and before release.

Do Not Use When

Do not use to generate the artefact or edit it without separate remediation authority.

Inputs

Input	Source/provider	If absent
Concrete artefact, purpose, audience	Requester or workspace	Stop; no artefact means no auditable evidence
Applicable source, dependency, render, or state evidence	Owner and available tools	Mark unavailable checks not assessed

Audit Core Method

1. Classify the artefact and applicable checks.
2. Run machine-checkable gates before judgment checks.
3. Cite exact evidence for each finding and stop on any blocking marker.
4. Grade consistently; name unavailable checks rather than passing them.

Outputs

Artefact	Consumer	Acceptance condition
Graded audit report	Author or release owner	Every finding has location, severity, evidence, fix, and unassessed checks

Audit Evidence Guidance

The audit report, check results, and evidence locations support the verdict.

Capability Contract

Default to read-only. Editing, publication, destructive action, spending, and certification require separate explicit authority.

Degraded Mode

Report inaccessible checks as not assessed; do not lower risk, invent findings, or issue a clean verdict that depends on them.

Decision Rules

Evidence	Action	Failure/risk avoided
Blocking factual, security, citation, or accessibility defect	Grade F and block	Harmful release
Supported non-blocking marker	Grade B or C and prescribe fix	Vague critique
No supported finding	Preserve clean verdict	Invented debt

Audit Quality Notes

Verdicts follow evidence, not preference, and unavailable checks remain visible.

Audit Pitfalls

• Reporting an “AI feel”; cite a marker.
• Inventing flaws to fill sections; allow a clean audit.
• Editing during diagnosis; request authority.
• Treating inaccessible checks as passes; mark them.
• Removing authored specificity; list what to preserve.

Audit Scenario

An inaccessible rendered page is marked not assessed; text-only findings do not imply a visual pass.

References

• Production-time prevention

Workflow

1. Classify the artefact and list every applicable machine and judgment check.
2. Run objective checks first and preserve exact locations for each hit.
3. Stop and grade F when a blocking defect is supported by evidence.
4. Recover unavailable checks by marking them not assessed; retry when the required artefact or tool becomes available.
5. Assign the verdict from the evidence and preserve authored material that should survive remediation.

Evidence Produced

Evidence	Consumer	Acceptance condition
Audit report and check log	Author and release owner	Every finding has an exact location, severity, supporting evidence, and concrete correction

Quality Standards

Every verdict agrees with its blocking markers, every finding cites observable evidence, and every unavailable check remains explicitly unassessed.

Anti-Patterns

• Reporting an undefined “AI feel.” Fix: cite the exact marker and location.
• Inventing defects to fill the report. Fix: allow a clean verdict when evidence supports it.
• Editing during diagnosis. Fix: request separate remediation authority.
• Passing an inaccessible check. Fix: mark it not assessed.
• Removing useful authored specificity. Fix: identify material that remediation must preserve.

Worked Example

If a document page cannot be rendered, the audit marks visual checks unassessed while still reporting separately evidenced citation and prose findings.

See also

• anti-ai-slop — real-time prevention companion.
• source-evaluation — the truth/credibility gate.
• professional-word-output — apply when the audited artefact is a .docx.