| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.471 | | leniency | 0.941 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 78.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 474 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 26.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 474 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "traced" | | 2 | "pulse" | | 3 | "familiar" | | 4 | "standard" | | 5 | "gloom" | | 6 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 467 | | ratio | 0.021 | | matches | | 0 | "The Veil Market – One Compass – Paid in Full." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 56.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 373 | | uniqueNames | 12 | | maxNameDensity | 1.88 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 7 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Davies | 6 | | One | 1 | | Compass | 1 | | Paid | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Davies" | | 4 | "Compass" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.562 | | windowScore | 0.833 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 26 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 467 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 20.3 | | std | 15.41 | | cv | 0.759 | | sampleLengths | | 0 | 46 | | 1 | 49 | | 2 | 8 | | 3 | 40 | | 4 | 8 | | 5 | 44 | | 6 | 9 | | 7 | 46 | | 8 | 17 | | 9 | 29 | | 10 | 17 | | 11 | 14 | | 12 | 2 | | 13 | 27 | | 14 | 7 | | 15 | 9 | | 16 | 12 | | 17 | 5 | | 18 | 28 | | 19 | 8 | | 20 | 32 | | 21 | 5 | | 22 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 90.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 61 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 59 | | ratio | 0.119 | | matches | | 0 | "The stench hit Detective Harlow Quinn before she even reached the platform—copper and something sweet, like rotting fruit left in the sun." | | 1 | "His torchlight flickered over the victim’s face—a young woman, mid-twenties, lips parted in what might’ve been surprise." | | 2 | "The sole was worn smooth, but the left heel had a fresh scuff—white paint." | | 3 | "*The Veil Market – One Compass – Paid in Full.*" | | 4 | "She’d heard it before—where?" | | 5 | "The needle spun wildly before jerking north—toward the tunnel’s mouth, where the darkness seemed thicker." | | 6 | "Then—a laugh." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 379 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.029023746701846966 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005277044854881266 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 7.92 | | std | 5.47 | | cv | 0.691 | | sampleLengths | | 0 | 22 | | 1 | 24 | | 2 | 13 | | 3 | 17 | | 4 | 2 | | 5 | 10 | | 6 | 7 | | 7 | 8 | | 8 | 8 | | 9 | 8 | | 10 | 2 | | 11 | 1 | | 12 | 13 | | 13 | 8 | | 14 | 3 | | 15 | 5 | | 16 | 10 | | 17 | 14 | | 18 | 17 | | 19 | 3 | | 20 | 2 | | 21 | 7 | | 22 | 7 | | 23 | 16 | | 24 | 13 | | 25 | 10 | | 26 | 6 | | 27 | 2 | | 28 | 9 | | 29 | 12 | | 30 | 17 | | 31 | 3 | | 32 | 10 | | 33 | 4 | | 34 | 7 | | 35 | 4 | | 36 | 3 | | 37 | 2 | | 38 | 12 | | 39 | 15 | | 40 | 3 | | 41 | 4 | | 42 | 4 | | 43 | 3 | | 44 | 2 | | 45 | 3 | | 46 | 9 | | 47 | 5 | | 48 | 16 | | 49 | 12 |
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| 75.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4915254237288136 | | totalSentences | 59 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 40 | | matches | | 0 | "His torchlight flickered over the" | | 1 | "He tipped the bag" | | 2 | "She pointed to the smudges" | | 3 | "Her torch beam traced the" | | 4 | "She plucked a crumpled slip" | | 5 | "She’d heard it before—where?" | | 6 | "She reached down." | | 7 | "She was running before Davies" |
| | ratio | 0.2 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 40 | | matches | | 0 | "The stench hit Detective Harlow" | | 1 | "The abandoned Tube station beneath" | | 2 | "Davies stood by the body," | | 3 | "His torchlight flickered over the" | | 4 | "He tipped the bag" | | 5 | "A bone token clattered against" | | 6 | "The kind they used at" | | 7 | "Harlow crouched, ignoring the protest" | | 8 | "The girl’s nails were broken," | | 9 | "She pointed to the smudges" | | 10 | "Harlow tilted the girl’s foot" | | 11 | "The sole was worn smooth," | | 12 | "Her torch beam traced the" | | 13 | "Harlow’s watch ticked loud against" | | 14 | "She plucked a crumpled slip" | | 15 | "*The Veil Market – One" | | 16 | "The shadows near the tracks" | | 17 | "Davies flipped his notebook" | | 18 | "Harlow’s pulse stuttered." | | 19 | "The name rang familiar, but" |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 12.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 2 | | matches | | 0 | "The abandoned Tube station beneath Camden should’ve been silent, but whispers slithered through the cracked tiles, voices that didn’t belong to the forensics te…" | | 1 | "She was running before Davies could protest, the beam of her torch slicing through the gloom." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |