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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").
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:
Identity leaks ("As a large language model",
"as of my training cutoff", "I cannot provide") —
severity: critical, no exceptions.
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").
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):
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.
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 and skills" in prose
Strong: humans have "and"
Spatial copula
"lives in", "sits at", "stands as", "boasts"
Inanimate subject with animate verb
Negative parallelism (contrastive negation)
"Not X but Y", "No X. No Y. Just Z.", "No X, no Y, no Z", "It's not X, it's Y", "It's X, not Y", "Y, not X"
Rhetorical scaffold with no argument
Contrastive parallelism (affirmative antithesis)
"Less X, more Y", "Where others X, we Y", "Humans propose; machines dispose"
Manufactured punch; same scaffold without the "not"
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
Semicolon splice
"The system is fast; it scales"
Prose semicolon joining two independent clauses. Rephrase into two sentences unless absolutely necessary
Loop/cascade vocab
"unpack", "surface" (verb), "a quiet shift"
2026 systems-theory affectation
Performative honesty
"to be honest", "Honestly,", "An Honest Review"
Manufactured authenticity
Sophistication marker
"survey the prior art", "state of the art", "body of work"
Rigor signaling in non-academic prose
Participial tail
", highlighting", ", underscoring", ", paving the way for"
Fake-analysis tack-on
Emphasis crutch
"Full stop.", "Make no mistake", "Read that again."
Manufactured-importance terminator
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.
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 install must
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.) on unsafe, 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: 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:
Prose slop (vocabulary, structural, document-economy, and 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 / 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)
### Recommendations1.**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
