Slop Detector

AI Slop Detection

Slop is a density problem, not a word problem.

A single "delve" is fine. Five "delves" near a "tapestry" and an "embark" is generated text. This skill scores density per 100 words, marker clustering, and whether the overall register fits the document type. It does not ban words. It flags concentrations.

When NOT To Use

• Writing the replacement prose (use scribe:doc-generator)
• Drift against a specific voice profile (use scribe:voice-review)

Execution Workflow

Identify target files and classify them as technical docs, narrative prose, or code comments. Classification feeds context-aware scoring: tier-1 markers in marketing copy score lower than the same markers in API reference.

Language Detection

• Auto-detect language from text content using function word frequency
• Override with explicit --lang parameter (en, de, fr, es)
• Load language-specific patterns from data/languages/{lang}.yaml
• Fall back to English if detection confidence is low
• See modules/language-handling.md for cultural calibration and concrete pattern sets

Spelling Normalization (British to American)

Convert British spellings to American by default, in any scanned document, unless the document opts out via .slop-config.yaml (spelling: british) or a per-word allowlist. This is a consistency pass, separate from slop scoring. Use the tested scribe.spelling functions (find_british_spellings, to_american); both preserve case and skip code, inline code, and URLs.

Load: @modules/spelling-normalization.md

Vocabulary and Phrase Detection

Load: @modules/vocabulary-patterns.md

Markers fall into three confidence tiers. Tier 1 words ("delve", "multifaceted", "leverage") appear far more often in AI text than human text. Tier 2 covers context-dependent transitions ("moreover", "subsequently"). Tier 3 covers vapid phrases ("In today's fast-paced world", "cannot be overstated").

Tier 2: Medium-Confidence Markers (Score: 2 each)

Common but context-dependent:

Tier 3: Phrase Patterns (Score: 2-4 each)

Step 3: Structural Pattern Detection

Load: @modules/structural-patterns.md

Em Dash Overuse

The single most-cited 2026 AI tell across Wikipedia, the Field Guide, and the Algorithmic Bridge. Detection runs in two modes:

Audit mode (forensic, applied to unknown prose):

• 0-1 per 1000 words: Normal human range
• 2-4: Elevated, review usage
• 5+: Strong AI signal

Prevention mode (applied to docs the agent just generated):

• Target zero. Every em-dash is a finding.
• Replace with commas (asides), parentheses (tangents), colons (definitions), or periods (separate thoughts). See modules/structural-patterns.md § Em Dash Analysis for the full replacement table.

# Count em dashes in file
grep -o '—' file.md | wc -l

Tricolon Detection

AI loves groups of three with alliteration:

• "fast, efficient, and reliable"
• "clear, concise, and compelling"
• "robust, reliable, and resilient"

Pattern: adjective, adjective, and adjective with similar sounds.

List-to-Prose Ratio

Count bullet points vs paragraph sentences:

• >60% bullets: AI tendency
• Emoji-led bullets: Strong AI signal in technical docs

Sentence Length Uniformity

Measure standard deviation of sentence lengths:

• Low variance (SD < 5 words): AI monotony
• High variance (SD > 10 words): Human variation

Paragraph Symmetry

AI produces "blocky" text with uniform paragraph lengths. Check whether paragraphs cluster around the same word count.

Step 4: Identity & Voice Leak Sweep (P0)

Load: @modules/identity-and-voice-leaks.md

Some patterns are not slop: they are direct evidence that AI generated text leaked into a published artifact. A single match in this class fails review independently of any other score.

Scan for:

1. Identity leaks ("As a large language model", "as of my training cutoff", "I cannot provide") — severity: critical, no exceptions.
2. Conversational voice leaks ("Hope this helps!", "Great question!", "Sure!") outside transcript blocks.
3. Self-narration of structure ("In this section, we will cover...", "Let's dive into...", "By the end of this guide...").
4. Hedging seesaw ("While X has its merits, it's not without its challenges").
5. Contrastive constructions, opening a clause or trailing it: both contrastive negation ("not just X, but Y", "It's not X, it's Y", and the trailing "It's X, not Y" / "Y, not X") and affirmative antithesis ("Less X, more Y", "Where others X, we Y"). Avoid in all but the most necessary cases; keep only when the contrast carries information that survives removal. The trailing copula form ("It's a tool, not a toy") is the easiest to miss because the opener reads as a plain definition.

See the module for the full pattern catalogue and false- positive guidance.

Step 4.5: Sycophantic Pattern Detection

Especially relevant for conversational or instructional content (complements Class 2 of the identity-and-voice-leaks module):

These phrases add no information and signal generated content.

Step 4.6: Tier 5 / 2026 Patterns (Prevention-Strict)

The 2026 cross-source consensus (Wikipedia Signs of AI writing, Algorithmic Bridge 10 Signs, Ignorance.ai Field Guide, Stop-Slop Claude skill, George Kao, ContentBeta, OliviaCal) identifies a handful of shapes that dominate post-GPT-5 / post-Claude-4.5 prose. Each is detailed in @modules/vocabulary-patterns.md (lexical form) and @modules/structural-patterns.md (structural form).

Prevention rule: when the slop-detector runs on docs the agent itself just generated (auto-invoked by /doc-generate, /doc-polish, /update-readme, /update-docs, etc.), every match in this table is a hard failure. Fix before write. See modules/remediation-strategies.md § Tier 5 / 2026 for the substitution tables.

Step 5: Calculate Slop Density Score

slop_score = (tier1_count * 3 + tier2_count * 2 + phrase_count * avg_phrase_score) / word_count * 100

Step 6: Document Economy Check

Load: @modules/document-economy.md

Sentence cleanliness is necessary, not sufficient. A document can score 0 on slop density and still waste reader time by being too long, lacking a thesis, or repeating everything except the one message that matters.

Score the document on three checks (0-2 each):

1. Thesis-first: does the lead state the single takeaway?
2. Sentence weight: does every sentence carry, instance, bound, or repeat the thesis?
3. Repetition rule: is the thesis echoed (good) while ambient repetition is cut (good)?

Combine sentence-level slop score with document-economy score. Both must pass. See modules/document-economy.md for the full rubric, the reader-time budget table, and a worked example.

Step 7: Hallucination & Stub Sweep

Load: @modules/hallucination-detection.md and @modules/stub-and-deferral.md.

Hallucination is not slop: it is wrongness with confident phrasing. Always P0.

Scan for:

1. Phantom code references: every backticked identifier, function name, or file path in prose must exist in the codebase.
2. Phantom dependencies: every recommended pip install / cargo install / npm install must resolve on the relevant registry (slopsquatting defense).
3. Dead URLs: every cited URL should return 200.
4. Made-up config keys: every config key in docs must be read by the code.
5. Bare TODO/FIXME: requires either a tracked-issue link or deletion.
6. Hedging language ("for now", "should work", "placeholder", "dummy"): each one is deferred work.
7. Stub constructs (todo!(), unimplemented!(), NotImplementedError): defects in any path reachable from a public API.

See modules for detection commands and severity matrix.

Step 8: Evidence-Backed Claims (READMEs and public docs)

Load: @modules/evidence-backed-claims.md

Every quality claim must point to evidence in the same repository. No evidence, delete the claim.

For each claim of "production-ready", "fast", "memory- safe", "scalable", etc., verify the corresponding evidence (CI workflow, benchmark directory, audit markers, etc.) actually exists. The module contains the full claim → required-evidence table and language- specific detection commands.

This step is highest-leverage for crate/library/project READMEs, where feature-list buzzword soup is the most common AI-generated failure mode.

Step 9: Apply Anti-Goals (safety check)

Load: @modules/anti-goals.md

Aggressive de-slopping has its own failure modes.

Before applying any fix surfaced by the prior steps, verify it does not violate the anti-goals:

1. Do not strip safety comments (// SAFETY:, // INVARIANT:, etc.) on unsafe, locked, or contract-bearing code.
2. Do not collapse public error variants without an explicit major-version-bump decision.
3. Do not "simplify" typed errors to boxed/dynamic errors.
4. Do not inline a function that has a domain-specific name even if it is short.
5. Do not touch generated code, vendored code, or historical changelog entries.
6. Do not auto-apply confidence: low findings — surface them for human decision.

When in doubt: leave the match flagged, do not delete.

The full multi-pass cleanup workflow

For systematic project-wide cleanup, run the multi-pass workflow in order. See @modules/cleanup-workflow.md for the full ten-pass methodology and the rationale for the ordering. Summary:

Cardinal rules: one pass per commit; deletion beats rewriting; do not silently apply low-confidence fixes; stop when a pass finds nothing.

Empirical baseline (cite when justifying severity)

Load: @modules/empirical-baseline.md for the 2025-Q1 2026 research baseline that justifies the severity weighting. Headline numbers:

• AI PRs ship 1.7x more total issues, 1.75x more logic/correctness issues, 2.74x more XSS, ~8x more excessive I/O than human-only PRs (CodeRabbit, Dec 2025).
• 92-96% of detected AI-code issues are maintainability ("code smell"), not correctness (Sonar, Q4 2025).
• Model-specific patterns: GPT fabricates; Claude omits. Calibrate the audit accordingly.

When a finding's severity is challenged in review, cite from this module rather than asserting from authority.

Step 10: Generate Report

For per-finding output that reviewers can accept or reject independently, use the canonical structured format defined in @modules/structured-finding-output.md. Each finding carries file, line, category, severity, confidence, evidence, rationale, fix, and (for high-confidence) diff. Auto-apply policy is set by confidence; never auto-apply confidence: low.

Summary report format (human-readable):

## Slop Detection Report: [filename]

**Overall Score**: X.X / 10 (Rating)
**Word Count**: N words
**Markers Found**: N total

### CRITICAL (P0, must resolve before merge)
- Line 8: "As a large language model". IDENTITY LEAK
- Line 47: References `Client.connect_with_timeout(...)` —
  HALLUCINATION (method does not exist; closest match is
  `Client.connect`)
- Line 102: "production-ready" claim with no CI workflow
 . UNVERIFIED CLAIM

### High-Confidence Markers (vocabulary)
- Line 23: "delve into" -> consider: "explore"
- Line 45: "rich tapestry" -> consider: "variety"

### Structural Issues
- Em dash density: 8/1000 words (HIGH)
- Bullet ratio: 72% (ELEVATED)
- Sentence length SD: 3.2 words (LOW VARIANCE)

### Phrase Patterns
- Line 12: "In today's fast-paced world" (vapid opener)
- Line 89: "cannot be overstated" (empty emphasis)
- Line 134: "Let's dive into" (self-narration of structure)

### Tier 5 / 2026 Patterns
- Line 19: "The skill lives in `plugins/scribe/`" → "is in"
  (spatial copula, inanimate subject)
- Line 27: "hooks + skills" → "hooks and skills" (plus-sign
  conjunction in prose)
- Line 34: "It's not a tool, it's a transformation" →
  rewrite positively (negative parallelism / contrastive
  negation)
- Line 38: "Less config, more code" → state plainly
  (contrastive parallelism; keep only if load-bearing)
- Line 56: "Here's the thing," → delete (throat-clearing
  opener)
- Line 78: "Focused. Aligned. Measurable." → "Focused,
  aligned, and measurable." (three-fragment burst)
- Line 91: 3 smart quotes outside code blocks (Word-processor
  paste signature)

### Stub & Deferral
- Line 56: bare `// TODO: handle expired tokens` (no
  tracked issue link)
- Line 71: "for now, we recommend" (deferral language)

### Document Economy Score: X / 6
- Thesis-first: 1/2 (thesis present but buried in para 3)
- Sentence weight: 1/2 (~65% of sentences earn weight)
- Repetition: 2/2 (thesis echoed; ambient repetition cut)

### Recommendations
1. **CRITICAL**: delete line 8 identity leak before merge
2. **CRITICAL**: replace `Client.connect_with_timeout`
   with `Client.connect(opts)` and update example
3. **CRITICAL**: either add CI + version >= 1.0 to back
   "production-ready", or delete the claim
4. Replace [specific word] with [alternative]
5. Convert bullet list at line 34-56 to prose
6. Hoist the thesis (line 47) into the lead paragraph
7. Link bare TODOs to tracked issues or delete code path

### Confidence-low findings (require human decision)
- Line 89: bullet count of 8 may be appropriate for this
  enumeration; do not auto-flatten
- Line 156: `Manager` suffix may be domain-meaningful;
  verify before renaming

Per anti-goals.md: surface confidence: low findings in a separate section. Do not silently apply them.

Module Reference

• See modules/fiction-patterns.md for narrative-specific slop markers
• See modules/remediation-strategies.md for fix recommendations

Integration with Remediation

After detection, invoke Skill(scribe:doc-generator) with the --remediate flag to apply fixes, or manually edit using the report as a guide.

Exit Criteria

• All target files scanned
• Density scores calculated
• Report generated with specific, line-anchored fixes
• High-severity items flagged for immediate attention

Source: athola/claude-night-market → plugins/scribe/skills/slop-detector/SKILL.md