| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1021 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 75.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1021 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "familiar" | | 2 | "rhythmic" | | 3 | "whisper" | | 4 | "warmth" |
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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 | 91 | | matches | (empty) | |
| 80.06% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 1021 | | ratio | 0.004 | | matches | | 0 | "cause of death: inconclusive" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1021 | | uniqueNames | 10 | | maxNameDensity | 0.59 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 6 | | Tuesday | 1 | | November | 1 | | Brewer | 1 | | Street | 1 | | Frith | 1 | | Renaissance | 1 | | Morris | 3 | | Met | 1 | | Tube | 1 |
| | persons | | | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Renaissance" | | 3 | "Met" |
| | globalScore | 1 | | windowScore | 1 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it belonged in a Renaissance" | | 1 | "patterns that seemed to move when she looked at them directly" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.979 | | wordCount | 1021 | | matches | | 0 | "not words, but the shape of words, the pressure of something trying to beco" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 91 | | matches | | |
| 86.30% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 40.84 | | std | 18.46 | | cv | 0.452 | | sampleLengths | | 0 | 15 | | 1 | 42 | | 2 | 54 | | 3 | 59 | | 4 | 57 | | 5 | 51 | | 6 | 47 | | 7 | 49 | | 8 | 44 | | 9 | 52 | | 10 | 42 | | 11 | 42 | | 12 | 56 | | 13 | 8 | | 14 | 72 | | 15 | 48 | | 16 | 49 | | 17 | 69 | | 18 | 15 | | 19 | 7 | | 20 | 42 | | 21 | 36 | | 22 | 22 | | 23 | 4 | | 24 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 91 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 91 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1029 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.022351797862001945 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0029154518950437317 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 11.22 | | std | 8.96 | | cv | 0.798 | | sampleLengths | | 0 | 15 | | 1 | 19 | | 2 | 5 | | 3 | 8 | | 4 | 10 | | 5 | 17 | | 6 | 18 | | 7 | 19 | | 8 | 3 | | 9 | 2 | | 10 | 37 | | 11 | 17 | | 12 | 3 | | 13 | 13 | | 14 | 10 | | 15 | 15 | | 16 | 16 | | 17 | 7 | | 18 | 4 | | 19 | 28 | | 20 | 6 | | 21 | 6 | | 22 | 4 | | 23 | 11 | | 24 | 11 | | 25 | 18 | | 26 | 3 | | 27 | 7 | | 28 | 4 | | 29 | 24 | | 30 | 14 | | 31 | 4 | | 32 | 4 | | 33 | 13 | | 34 | 4 | | 35 | 19 | | 36 | 8 | | 37 | 19 | | 38 | 3 | | 39 | 19 | | 40 | 3 | | 41 | 10 | | 42 | 10 | | 43 | 22 | | 44 | 16 | | 45 | 3 | | 46 | 3 | | 47 | 11 | | 48 | 3 | | 49 | 6 |
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| 48.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3626373626373626 | | totalSentences | 91 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 85 | | matches | | 0 | "Instead he dropped to his" | | 1 | "Somewhere far below, a door" | | 2 | "Somewhere ahead, she heard voices," |
| | ratio | 0.035 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 85 | | matches | | 0 | "Her shoulder clipped the brick." | | 1 | "She kicked the blade out" | | 2 | "It meant bouncerless clubs and" | | 3 | "He moved well." | | 4 | "She'd clocked that the moment" | | 5 | "He'd looked up, seen her" | | 6 | "Her breath came hard, fogging" | | 7 | "She caught the flash of" | | 8 | "He knew these streets." | | 9 | "She followed, coat snagging on" | | 10 | "She didn't stop." | | 11 | "Her hand closed on his" | | 12 | "He kicked backward." | | 13 | "His heel caught her wrist," | | 14 | "She let go." | | 15 | "He descended into the black" | | 16 | "She stood over the hole," | | 17 | "She reached for her radio," | | 18 | "She thought of Morris." | | 19 | "She clipped the radio back" |
| | ratio | 0.294 | |
| 89.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 85 | | matches | | 0 | "The bone knife hit the" | | 1 | "Harlow Quinn lunged forward, fingers" | | 2 | "Her shoulder clipped the brick." | | 3 | "The pain registered somewhere distant," | | 4 | "She kicked the blade out" | | 5 | "Soho on a Tuesday night" | | 6 | "It meant bouncerless clubs and" | | 7 | "Quinn's shoes slapped against the" | | 8 | "He moved well." | | 9 | "She'd clocked that the moment" | | 10 | "He'd looked up, seen her" | | 11 | "The suspect's silhouette cut between" | | 12 | "Her breath came hard, fogging" | | 13 | "The leather strap of her" | | 14 | "A motorbike roared past, drowning" | | 15 | "The suspect glanced back." | | 16 | "She caught the flash of" | | 17 | "He knew these streets." | | 18 | "She followed, coat snagging on" | | 19 | "She didn't stop." |
| | ratio | 0.741 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "She'd clocked that the moment she'd stepped into the flat above the laundromat in Brewer Street, found him crouched over a circle of chalk and a collection of g…" | | 1 | "She caught the flash of his face under a streetlamp, young, sharp-featured, the kind of face that looked like it belonged in a Renaissance painting or a morgue." | | 2 | "She stood over the hole, rain drumming against her shoulders, peering into a darkness that swallowed her torch beam forty feet down." | | 3 | "The cover scraped as she pulled it closed above her head, shutting out the orange light, the rain, the familiar world of witness statements and CPS packets and …" | | 4 | "Somewhere ahead, she heard voices, low and rhythmic, and the faint sound of music, something with strings that wove through the air like smoke." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |