slop-detector

name	slop-detector
description	Score a draft, page, or caption set for AI slop with a four-tier severity model instead of a flat banned-words list. Exact production narration fails outright, rhetorical scaffolds warn only when repeated, lexical tells become review prompts, and document-level patterns are diagnosed. Use when the user asks to score, audit, grade, or check text for AI tells, runs /slop-detector, or wants a pass/fail gate before publishing.
argument-hint	[file]
license	MIT
metadata	{"version":"1.0.0","author":"Gaurav Tiwari"}

Slop Detector

Audit prose for AI slop and report findings by severity. Stop Slop tells a model what not to write; this skill checks what was written and says how bad it is.

A banned-words list fails in both directions. It flags "robust" in a sentence about a nonce implementation, and it passes a caption that says "a fresh capture, not a generic mockup." The severity model below separates the two.

Severity model

slop-detector.json in this folder is the authority. Four tiers:

Severity	Meaning	What to do
hard_fail	Exact, high-confidence production or process narration in reader-facing copy ("captured with Playwright", "Status in this study", "Weak spot: I didn't test")	Block publication and rewrite. A low score never rescues a hard fail.
warn_if_repeated	Rhetorical scaffolds that can carry one real turn: mirrored "not X, it's Y" contrasts, "No X. No Y. No Z." triads, per-item disclaimers, templated roundup filler	Count across the piece. Warn only at the family's repeat threshold.
contextual_review	Broad lexical signals: "leverage", "seamless", "here's the thing", academic connectors, empty intensifiers	Read the sentence. Quotations, code, proper names, and precise technical terms are not defects. Never auto-replace.
structural_diagnostic	Document-level patterns: identical paragraph lengths, a dead table column, the same sentence closing every item, passive-voice density	Diagnose the cause. Never add fragments, emotion, or asymmetry to make a metric pass.

Advisory score, counted only after context review: warn_if_repeated +2, confirmed contextual_review +1, structural_diagnostic +3, +2, or +1 by priority.

• 0 to 5: clean
• 6 to 15: needs editing
• 16+: rewrite the affected passages or structure

Truth, source fidelity, and authorial intention sit above this model. An unsupported claim or invented first-person experience blocks publication even when the score is 0.

Workflow

1. If the text is a file on disk, run the scanner first.

   python3 scripts/scan.py draft.md
   python3 scripts/scan.py post.html --json
   python3 scripts/scan.py a.md b.md c.md     # adds the sibling-boilerplate check
   

   It prints hard fails, warnings, structural findings, and contextual prompts, then two numbers: the score from automatic findings and the ceiling if every contextual prompt is confirmed. Matches inside quotation marks, blockquotes, and code are downgraded to contextual_review automatically.

2. Review every contextual prompt in context. Keep the word when it is quoted, a product name, a technical term, or the precise word. Confirm it only when the sentence would be better without it. Recompute the score from confirmed findings.

3. Run the structural checks the scanner cannot. Symmetric pros and cons, an FAQ entry that restates an H2, and boilerplate shared across sibling articles need a reader. The scanner names these as manual checks.

4. If there is no file, do the pass by hand. Load references/banned-phrases.md for the phrase families and references/ai-tells.md for the 28 structural, language, rhythm, content, and teaching tells. Apply the same four tiers.

5. Report, then fix the cause. For each confirmed finding give the severity, the quoted evidence, why it hurts this reader, and the smallest durable fix. Fix rhythm by following the thought, not by inserting one-word sentences. Fix a dead column by deleting it and stating the fact once. Fix a hard fail by naming what the image or result shows and nothing about how it was made.

For captions and alt text alone, scripts/check-assets.py post.html runs only the hard-fail production-commentary patterns and exits 1 on any hit. It is built for CI.

Output format

HARD FAIL (1)
  production_commentary: "captured with Playwright" [block 14, figcaption]
  Fix: "Semrush's affiliate program page inside Impact."

warn_if_repeated (1)  +2
  repeated_scaffolds/mirrored_contrast: 3 occurrences, threshold 2

contextual_review (4)  review before counting
  broad_lexical_tells: "robust" [block 9] -> technical term, keep
  ...

Advisory score: 2 (clean) from automatic findings; ceiling 6 (needs editing) if every prompt is confirmed.

Lead with the verdict. If nothing actionable is present, say so in one line.

What this skill does not do

• It does not fact-check. A clean score says nothing about whether a price, version, or quote is true.
• It does not detect authorship. Humans write slop too, and the tells are evidence to inspect, not proof a model wrote the text.
• It does not rewrite. Pair it with stop-slop for drafting rules and red-pen for a line edit.
• It does not reward roughness. Fragments, typos, and performed asides are tells of their own (see ai-tells.md, Authorial Intention Tells).

Regression tests

python3 scripts/test-severity.py

The test locks the contract: only production_commentary and the always_fail group of process_leakage may hard-fail, broad lexical matches stay contextual, scaffolds warn only past their thresholds, and the score bands do not drift. Run it after any edit to slop-detector.json.