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.
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
--langparameter (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.mdfor cultural calibration and concrete pattern sets
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”).
| Word | Context | Human Alternative |
|---|---|---|
| delve | ”delve into” | explore, examine, look at |
| tapestry | ”rich tapestry” | mix, combination, variety |
| realm | ”in the realm of” | in, within, regarding |
| embark | ”embark on a journey” | start, begin |
| beacon | ”a beacon of” | example, model |
| spearheaded | formal attribution | led, started |
| multifaceted | describing complexity | complex, varied |
| comprehensive | describing scope | thorough, complete |
| pivotal | importance marker | key, important |
| nuanced | sophistication signal | subtle, detailed |
| meticulous/meticulously | care marker | careful, detailed |
| intricate | complexity marker | detailed, complex |
| showcasing | display verb | showing, displaying |
| leveraging | business jargon | using |
| streamline | optimization verb | simplify, improve |
Tier 2: Medium-Confidence Markers (Score: 2 each)
Common but context-dependent:
| Category | Words |
|---|---|
| Transition overuse | moreover, furthermore, indeed, notably, subsequently |
| Intensity clustering | significantly, substantially, fundamentally, profoundly |
| Hedging stacks | potentially, typically, often, might, perhaps |
| Action inflation | revolutionize, transform, unlock, unleash, elevate |
| Empty emphasis | crucial, vital, essential, paramount |
Tier 3: Phrase Patterns (Score: 2-4 each)
| Phrase | Score | Issue |
|---|---|---|
| ”In today’s fast-paced world” | 4 | Vapid opener |
| ”It’s worth noting that” | 3 | Filler |
| ”At its core” | 2 | Positional crutch |
| ”Cannot be overstated” | 3 | Empty emphasis |
| ”A testament to” | 3 | Attribution cliche |
| ”Navigate the complexities” | 4 | Business speak |
| ”Unlock the potential” | 4 | Marketing speak |
| ”Treasure trove of” | 3 | Overused metaphor |
| ”Game changer” | 3 | Buzzword |
| ”Look no further” | 4 | Sales pitch |
| ”Nestled in the heart of” | 4 | Travel writing cliche |
| ”Embark on a journey” | 4 | Melodrama |
| ”Ever-evolving landscape” | 4 | Tech cliche |
| ”Hustle and bustle” | 3 | Filler |
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 -lTricolon 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:
- Identity leaks (“As a large language model”, “as of my training cutoff”, “I cannot provide”) — severity: critical, no exceptions.
- Conversational voice leaks (“Hope this helps!”, “Great question!”, “Sure!”) outside transcript blocks.
- Self-narration of structure (“In this section, we will cover…”, “Let’s dive into…”, “By the end of this guide…”).
- Hedging seesaw (“While X has its merits, it’s not without its challenges”).
- Parallel “not just” / “not only” as paragraph openers.
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):
| Phrase | Issue |
|---|---|
| ”I’d be happy to” | Servile opener |
| ”Great question!” | Empty validation |
| ”Absolutely!” | Over-agreement |
| ”That’s a wonderful point” | Flattery |
| ”I’m glad you asked” | Filler |
| ”You’re absolutely right” | Sycophancy |
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).
| Pattern | Form | Why it matters |
|---|---|---|
| Em-dash overuse | — used as rhetorical pause | Most-cited single tell of 2026 |
| Plus-sign for “and" | "hooks + skills” in prose | Strong: humans have “and” |
| Spatial copula | ”lives in”, “sits at”, “stands as”, “boasts” | Inanimate subject with animate verb |
| Negative parallelism | ”Not X but Y”, “No X. No Y. Just Z.”, “No X, no Y, no Z”, “It’s not X, it’s Y”, “Y, not X” | Rhetorical scaffold with no argument |
| Throat-clearing openers | ”Here’s the thing,”, “Look,”, “Let that sink in.” | Discourse markers signaling nothing |
| Three-fragment burst | ”Focused. Aligned. Measurable.” | Rhythm without information |
| Significance cluster | ”stands as a testament to”, “marks a turning point” | Asserts importance without showing it |
| Smart quotes in technical prose | "text" / "text" instead of "text" | Word-processor paste signature |
| Loop/cascade vocab | ”unpack”, “surface” (verb), “a quiet shift” | 2026 systems-theory affectation |
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| Score | Rating | Action |
|---|---|---|
| 0-1.0 | Clean | No action needed |
| 1.0-2.5 | Light | Spot remediation |
| 2.5-5.0 | Moderate | Section rewrite recommended |
| 5.0+ | Heavy | Full document review |
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):
- Thesis-first: does the lead state the single takeaway?
- Sentence weight: does every sentence carry, instance, bound, or repeat the thesis?
- 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:
- Phantom code references: every backticked identifier, function name, or file path in prose must exist in the codebase.
- Phantom dependencies: every recommended
pip install/cargo install/npm installmust resolve on the relevant registry (slopsquatting defense). - Dead URLs: every cited URL should return 200.
- Made-up config keys: every config key in docs must be read by the code.
- Bare TODO/FIXME: requires either a tracked-issue link or deletion.
- Hedging language (“for now”, “should work”, “placeholder”, “dummy”): each one is deferred work.
- 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:
- Do not strip safety comments (
// SAFETY:,// INVARIANT:, etc.) onunsafe, locked, or contract-bearing code. - Do not collapse public error variants without an explicit major-version-bump decision.
- Do not “simplify” typed errors to boxed/dynamic errors.
- Do not inline a function that has a domain-specific name even if it is short.
- Do not touch generated code, vendored code, or historical changelog entries.
- Do not auto-apply
confidence: lowfindings — 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:
| Pass | Focus |
|---|---|
| 0 | Pre-slop sweep: secrets, agent configs |
| 1 | Surface lint floor (formatter + linter) |
| 2 | Hallucination & stubs (modules: hallucination, stub-and-deferral) |
| 3 | Identity & voice leaks |
| 4 | Comment slop (translation, marketing, banner, deferral) |
| 5 | Prose slop (vocabulary + structural + document-economy + evidence-backed-claims) |
| 6 | Code idiom (delegate to language-specific plugins) |
| 7 | Architecture (judgment-heavy; see anti-goals) |
| 8 | Tests (tautology, mocks, snapshots) |
| 9 | README & public docs |
| 10 | Establish guardrails (CI, lints, constitution) |
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)
- 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 renamingPer anti-goals.md: surface confidence: low findings
in a separate section. Do not silently apply them.
Module Reference
- See
modules/fiction-patterns.mdfor narrative-specific slop markers - See
modules/remediation-strategies.mdfor 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