| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | 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) | |
| 89.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "stomach" | | 1 | "weight" | | 2 | "familiar" |
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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 | 1 | | narrationSentences | 91 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 61 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 10 | | markdownWords | 31 | | totalWords | 1444 | | ratio | 0.021 | | matches | | 0 | "Administered unauthorised treatments." | | 1 | "POLICE" | | 2 | "police!" | | 3 | "lit" | | 4 | "Contain. Withdraw. Call it in." | | 5 | "Wait here, I'll be two minutes," | | 6 | "unascertained" | | 7 | "tragic" | | 8 | "Ya, ya" | | 9 | "subject entered disused station premises, pursuit terminated on safety grounds" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1420 | | uniqueNames | 32 | | maxNameDensity | 0.77 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | Soho | 2 | | Herrera | 3 | | London | 2 | | Ambulance | 1 | | Service | 1 | | Twelve | 1 | | Bateman | 1 | | Greek | 1 | | Oxford | 1 | | Street | 5 | | Army | 1 | | Warren | 1 | | Astra | 1 | | Hampstead | 1 | | Road | 1 | | Camden | 1 | | High | 3 | | Santa | 1 | | November | 1 | | Lock | 1 | | Victorian | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Underground | 1 | | Three | 1 | | Whitechapel | 1 | | Commissioner | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Morris" | | 3 | "Herrera" | | 4 | "Lock" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Commissioner" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Oxford" | | 3 | "Street" | | 4 | "Army" | | 5 | "Warren" | | 6 | "Hampstead" | | 7 | "Road" | | 8 | "Camden" | | 9 | "High" | | 10 | "Santa" | | 11 | "November" | | 12 | "Three" | | 13 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like bone — warm when it shouldn't" |
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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 | 1444 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 41.26 | | std | 34.23 | | cv | 0.83 | | sampleLengths | | 0 | 111 | | 1 | 25 | | 2 | 25 | | 3 | 81 | | 4 | 48 | | 5 | 9 | | 6 | 67 | | 7 | 8 | | 8 | 76 | | 9 | 21 | | 10 | 61 | | 11 | 6 | | 12 | 7 | | 13 | 95 | | 14 | 21 | | 15 | 83 | | 16 | 5 | | 17 | 25 | | 18 | 67 | | 19 | 24 | | 20 | 11 | | 21 | 89 | | 22 | 10 | | 23 | 93 | | 24 | 15 | | 25 | 101 | | 26 | 16 | | 27 | 87 | | 28 | 18 | | 29 | 12 | | 30 | 22 | | 31 | 71 | | 32 | 12 | | 33 | 13 | | 34 | 9 |
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| 89.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 91 | | matches | | 0 | "being demolished" | | 1 | "been sealed" | | 2 | "was *lit" | | 3 | "was told" | | 4 | "been rubbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 223 | | matches | | 0 | "was already stiffening" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 93 | | ratio | 0.097 | | matches | | 0 | "That was the job — the boring, sodden, thankless spine of it." | | 1 | "He walked fast, north, cutting through the wet grid of Soho — Bateman, Greek, across Oxford Street against the lights with a bus horn tearing after him." | | 2 | "She got into hers — hers being a fourteen-year-old Astra she'd left on a single yellow with a *POLICE* card on the dash — and she followed the cab's brake lights up through the wet dark, Hampstead Road, Camden High Street, past kebab shops steaming into the rain and a man in a Santa hat in November shouting at a bin." | | 3 | "But he glanced over his shoulder as he crossed toward the canal and his whole body changed — shoulders dropping, bag shifting round to his back, weight coming forward onto the balls of his feet — and then he ran." | | 4 | "Past a shuttered lock-up, up a set of slick iron steps, and out into a road she couldn't name — Victorian brick, scaffolding, hoardings papered with layered posters gone to pulp." | | 5 | "Ahead of her his medallion caught a streetlamp and flashed — a small silver spark bouncing at his throat, Saint Christopher, patron of travellers, carrying the child across the flood — and then he stopped dead in front of a hoarding at the alley's end, glanced back at her, and pushed a board aside." | | 6 | "Not a door — a doorway, the arched mouth of a building that had been sealed for decades, oxblood tiles crazed and dulled, and above it, in the ghost of a font she recognised from a hundred station platforms, letters she could only half read where the render had fallen away." | | 7 | "Unknown premises, no lighting plan, no comms — she pulled her radio, thumbed it, and got a wash of noise like a held breath, no tone at all, and put it back." | | 8 | "Bone, or something that felt like bone — warm when it shouldn't be, yellowed as an old piano key, marked with a spiral cut so fine you couldn't feel it under the pad of your thumb, only see it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1420 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.02323943661971831 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005633802816901409 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 15.53 | | std | 14.61 | | cv | 0.941 | | sampleLengths | | 0 | 22 | | 1 | 30 | | 2 | 2 | | 3 | 7 | | 4 | 50 | | 5 | 9 | | 6 | 4 | | 7 | 12 | | 8 | 25 | | 9 | 6 | | 10 | 16 | | 11 | 7 | | 12 | 12 | | 13 | 19 | | 14 | 6 | | 15 | 15 | | 16 | 3 | | 17 | 3 | | 18 | 42 | | 19 | 9 | | 20 | 27 | | 21 | 4 | | 22 | 36 | | 23 | 8 | | 24 | 61 | | 25 | 7 | | 26 | 8 | | 27 | 21 | | 28 | 4 | | 29 | 17 | | 30 | 40 | | 31 | 3 | | 32 | 3 | | 33 | 2 | | 34 | 3 | | 35 | 2 | | 36 | 23 | | 37 | 16 | | 38 | 8 | | 39 | 17 | | 40 | 31 | | 41 | 12 | | 42 | 9 | | 43 | 7 | | 44 | 10 | | 45 | 12 | | 46 | 54 | | 47 | 5 | | 48 | 25 | | 49 | 10 |
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| 72.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4946236559139785 | | totalSentences | 93 | | uniqueOpeners | 46 | |
| 82.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 81 | | matches | | 0 | "Somewhere behind her on the" | | 1 | "Then she stepped through the" |
| | ratio | 0.025 | |
| 66.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 81 | | matches | | 0 | "Her tea had gone cold" | | 1 | "She sat still anyway." | | 2 | "She'd read his registration hearing" | | 3 | "He looked left." | | 4 | "He looked right." | | 5 | "He looked at nothing at" | | 6 | "She let him get forty" | | 7 | "He walked fast, north, cutting" | | 8 | "She had a coat the" | | 9 | "She got into hers —" | | 10 | "She didn't know how." | | 11 | "She was thirty metres back," | | 12 | "They never did." | | 13 | "He was quick and he" | | 14 | "He went over a chain" | | 15 | "She had air enough for" | | 16 | "He cut left into a" | | 17 | "She followed, boots skidding, one" | | 18 | "He went through the gap." | | 19 | "She'd walked past its bricked-up" |
| | ratio | 0.383 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 81 | | matches | | 0 | "The rain had been coming" | | 1 | "Water on the windscreen turned" | | 2 | "Her tea had gone cold" | | 3 | "The worn leather strap of" | | 4 | "She sat still anyway." | | 5 | "That was the job —" | | 6 | "Quinn knew him from the" | | 7 | "Tomás Herrera, twenty-nine, formerly of" | | 8 | "Olive skin gone grey under" | | 9 | "She'd read his registration hearing" | | 10 | "He looked left." | | 11 | "He looked right." | | 12 | "He looked at nothing at" | | 13 | "She let him get forty" | | 14 | "He walked fast, north, cutting" | | 15 | "Quinn kept the gap." | | 16 | "She had a coat the" | | 17 | "She got into hers —" | | 18 | "The cab pulled in by" | | 19 | "Herrera got out and didn't" |
| | ratio | 0.642 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 43.19% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain had been coming down for six hours and Quinn had stopped noticing it, which was its own kind of problem." | | 1 | "Warm-eyed in his HCPC photograph, the kind of face that talked people through a cardiac arrest in a stairwell." | | 2 | "She got into hers — hers being a fourteen-year-old Astra she'd left on a single yellow with a *POLICE* card on the dash — and she followed the cab's brake light…" | | 3 | "The hoarding surrounded a demolition site that wasn't being demolished." | | 4 | "She had a warrant card and a pair of cuffs and a knee that was already stiffening, and she was one woman on the lip of a hole in the ground with a suspect somew…" | | 5 | "Bone, or something that felt like bone — warm when it shouldn't be, yellowed as an old piano key, marked with a spiral cut so fine you couldn't feel it under th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |