| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1106 | | totalAiIsmAdverbs | 1 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1106 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1113 | | uniqueNames | 22 | | maxNameDensity | 0.45 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Herrera" | | discoveredNames | | Saint | 1 | | Christopher | 1 | | Herrera | 5 | | Bermondsey | 1 | | Hackney | 1 | | King | 2 | | College | 2 | | Hospital | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Leicester | 1 | | Square | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Camden | 2 | | Morris | 3 | | Tomás | 2 | | Veil | 1 | | Market | 1 | | Quinn | 3 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Market" | | 6 | "Quinn" |
| | places | | 0 | "Bermondsey" | | 1 | "Hackney" | | 2 | "King" | | 3 | "Raven" | | 4 | "Soho" | | 5 | "Leicester" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like something out of a surgical t" | | 1 | "not quite a click, not quite a tone — and the door opened" | | 2 | "not quite a tone — and the door opened" | | 3 | "kind that seemed to come from the air itself, and with it came sound: voices layered on top of each other, the movement of people, something that might have been music if the intervals had been normal" | | 4 | "quite total — there was a faint luminescence below, sourceless, the kind that seemed to come from the air itself, and with it came sound: voices layered on top of each other, the movement of people, something that might have been music if the intervals had been normal" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1113 | | matches | (empty) | |
| 35.09% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 76 | | matches | | 0 | "doing that thing" | | 1 | "see that the" | | 2 | "understood that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 38.38 | | std | 32.51 | | cv | 0.847 | | sampleLengths | | 0 | 14 | | 1 | 87 | | 2 | 16 | | 3 | 97 | | 4 | 62 | | 5 | 78 | | 6 | 25 | | 7 | 47 | | 8 | 29 | | 9 | 2 | | 10 | 89 | | 11 | 62 | | 12 | 9 | | 13 | 8 | | 14 | 97 | | 15 | 21 | | 16 | 48 | | 17 | 16 | | 18 | 61 | | 19 | 9 | | 20 | 8 | | 21 | 2 | | 22 | 2 | | 23 | 87 | | 24 | 11 | | 25 | 59 | | 26 | 2 | | 27 | 53 | | 28 | 12 |
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| 91.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 76 | | matches | | 0 | "been declared" | | 1 | "been removed" | | 2 | "been retrofitted" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 177 | | matches | | 0 | "was doing" | | 1 | "was making" | | 2 | "was heading" | | 3 | "was calling" | | 4 | "was looking" | | 5 | "was calling" | | 6 | "was revising" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 76 | | ratio | 0.132 | | matches | | 0 | "He was doing that thing suspects always did when they thought they were clear — moving with a kind of deliberate casualness that screamed the opposite, shoulders hunched, eyes cutting left-right-left at every junction." | | 1 | "She'd pulled him in connection with three separate incidents — a man found in Bermondsey with wounds that no attending physician could explain, a woman in Hackney who'd been declared dead and then wasn't, and a boy in his teens who'd turned up outside King's College Hospital with sutures so precise the registrar had said they looked like something out of a surgical textbook, except the technique was one nobody taught anymore." | | 2 | "She checked her watch — the worn leather band soft against her wrist, the face scratched but functional." | | 3 | "Herrera turned down a side street she didn't recognize — that was unusual enough in itself, because she knew Camden the way she knew her own handwriting." | | 4 | "No — not a door." | | 5 | "He pressed it against the frame, and she heard a sound she couldn't categorize — not quite a click, not quite a tone — and the door opened." | | 6 | "Up close she could see that the door was old but the mechanism was not — it had been retrofitted with something, a recessed fitting that gleamed dully even in the poor light." | | 7 | "She thought about the way Morris had looked in the last week of his life — that particular expression she'd spent three years trying to name, the one she'd eventually decided was not fear exactly but something adjacent to it." | | 8 | "The stairs descended steeply into a darkness that wasn't quite total — there was a faint luminescence below, sourceless, the kind that seemed to come from the air itself, and with it came sound: voices layered on top of each other, the movement of people, something that might have been music if the intervals had been normal." | | 9 | "The smell of a market, unmistakably — commerce, bodies, transactions — but underneath it something older." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1106 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.03616636528028933 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.012658227848101266 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 14.64 | | std | 13.73 | | cv | 0.937 | | sampleLengths | | 0 | 14 | | 1 | 27 | | 2 | 5 | | 3 | 34 | | 4 | 21 | | 5 | 2 | | 6 | 1 | | 7 | 2 | | 8 | 11 | | 9 | 72 | | 10 | 10 | | 11 | 7 | | 12 | 2 | | 13 | 6 | | 14 | 24 | | 15 | 7 | | 16 | 6 | | 17 | 25 | | 18 | 25 | | 19 | 30 | | 20 | 23 | | 21 | 5 | | 22 | 20 | | 23 | 18 | | 24 | 3 | | 25 | 15 | | 26 | 2 | | 27 | 9 | | 28 | 19 | | 29 | 8 | | 30 | 2 | | 31 | 2 | | 32 | 27 | | 33 | 12 | | 34 | 28 | | 35 | 22 | | 36 | 5 | | 37 | 5 | | 38 | 16 | | 39 | 6 | | 40 | 2 | | 41 | 28 | | 42 | 3 | | 43 | 6 | | 44 | 8 | | 45 | 13 | | 46 | 33 | | 47 | 37 | | 48 | 3 | | 49 | 3 |
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| 42.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.35526315789473684 | | totalSentences | 76 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 65 | | matches | | 0 | "Just enough to feel like" | | 1 | "Then she crossed the street," | | 2 | "Somewhere below her, Tomás Herrera" |
| | ratio | 0.046 | |
| 35.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 65 | | matches | | 0 | "He hadn't seen her yet." | | 1 | "He was doing that thing" | | 2 | "She'd had his file open" | | 3 | "She'd pulled him in connection" | | 4 | "She'd been sitting in a" | | 5 | "She'd seen him go in" | | 6 | "She'd expected to wait until" | | 7 | "He'd come back out at" | | 8 | "She followed him north through" | | 9 | "He moved through Leicester Square" | | 10 | "He was heading somewhere specific." | | 11 | "She checked her watch —" | | 12 | "She pulled out her phone," | | 13 | "She thought about Morris, briefly," | | 14 | "It was narrow, running between" | | 15 | "He stopped at a door." | | 16 | "He produced something from his" | | 17 | "He pressed it against the" | | 18 | "He went through." | | 19 | "She examined the frame and" |
| | ratio | 0.462 | |
| 52.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 65 | | matches | | 0 | "The rain had been falling" | | 1 | "Quinn turned up the collar" | | 2 | "He hadn't seen her yet." | | 3 | "He was doing that thing" | | 4 | "The Saint Christopher medallion caught" | | 5 | "She'd had his file open" | | 6 | "She'd pulled him in connection" | | 7 | "Herrera's name surfaced in the" | | 8 | "A witness here, a CCTV" | | 9 | "She'd been sitting in a" | | 10 | "She'd seen him go in" | | 11 | "She'd expected to wait until" | | 12 | "He'd come back out at" | | 13 | "She followed him north through" | | 14 | "He moved through Leicester Square" | | 15 | "He was heading somewhere specific." | | 16 | "The zigzags and doublebacks of" | | 17 | "She checked her watch —" | | 18 | "Quarter past eleven." | | 19 | "She pulled out her phone," |
| | ratio | 0.815 | |
| 76.92% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 65 | | matches | | 0 | "By the time they reached" |
| | ratio | 0.015 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 6 | | matches | | 0 | "He was doing that thing suspects always did when they thought they were clear — moving with a kind of deliberate casualness that screamed the opposite, shoulder…" | | 1 | "She'd pulled him in connection with three separate incidents — a man found in Bermondsey with wounds that no attending physician could explain, a woman in Hackn…" | | 2 | "The zigzags and doublebacks of a man being careful rather than the loose wandering of a man who was lost." | | 3 | "An old service entrance, the kind that opened onto maintenance stairs, the frame inset with iron." | | 4 | "The look of a man who had seen the edge of something enormous and was revising everything he thought he knew." | | 5 | "The stairs descended steeply into a darkness that wasn't quite total — there was a faint luminescence below, sourceless, the kind that seemed to come from the a…" |
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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 | |