| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1601 | | 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) | |
| 78.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1601 | | totalAiIsms | 7 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "down her spine" | | 1 | "flickered" | | 2 | "pulse" | | 3 | "footsteps" | | 4 | "roaring" | | 5 | "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 | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 103 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 30 | | totalWords | 1619 | | ratio | 0.019 | | matches | | 0 | "The patient's presentation was not consistent with human physiology." | | 1 | "for" | | 2 | "Control, DS Quinn, requesting urgent assistance, my location is—" | | 3 | "DS Morris, last seen —" | | 4 | "never go in blind, Harlow, ever" |
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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 | 62 | | wordCount | 1600 | | uniqueNames | 33 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Bateman | 1 | | Street | 2 | | Raven | 2 | | Nest | 2 | | Tomás | 3 | | Herrera | 4 | | Quinn | 13 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Cambridge | 1 | | Circus | 1 | | Morris | 4 | | Tottenham | 1 | | Court | 1 | | Northern | 1 | | Line | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 3 | | Town | 1 | | High | 1 | | Thursday | 1 | | Thames | 1 | | Water | 3 | | Victorian | 2 | | Lock | 1 | | Bermondsey | 1 | | Wall | 1 | | Twenty-two | 1 | | Harlow | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Quinn" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Water" | | 9 | "Wall" | | 10 | "Harlow" |
| | places | | 0 | "Bateman" | | 1 | "Street" | | 2 | "Charing" | | 3 | "Cross" | | 4 | "Road" | | 5 | "Cambridge" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Camden" | | 9 | "Town" | | 10 | "High" | | 11 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "something like a wet animal" |
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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 | 1619 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 38.55 | | std | 34.96 | | cv | 0.907 | | sampleLengths | | 0 | 66 | | 1 | 12 | | 2 | 79 | | 3 | 10 | | 4 | 114 | | 5 | 11 | | 6 | 7 | | 7 | 100 | | 8 | 10 | | 9 | 60 | | 10 | 20 | | 11 | 43 | | 12 | 14 | | 13 | 55 | | 14 | 77 | | 15 | 14 | | 16 | 62 | | 17 | 7 | | 18 | 27 | | 19 | 85 | | 20 | 12 | | 21 | 54 | | 22 | 50 | | 23 | 10 | | 24 | 124 | | 25 | 5 | | 26 | 7 | | 27 | 107 | | 28 | 21 | | 29 | 75 | | 30 | 46 | | 31 | 10 | | 32 | 41 | | 33 | 56 | | 34 | 7 | | 35 | 20 | | 36 | 6 | | 37 | 6 | | 38 | 3 | | 39 | 80 | | 40 | 3 | | 41 | 3 |
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| 98.45% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 103 | | matches | | 0 | "were stacked" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 255 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 105 | | ratio | 0.124 | | matches | | 0 | "The sign flickered — it always flickered, some fault nobody had bothered to fix, or nobody wanted to — and the light on the road went from green to nothing to green again, like a pulse." | | 1 | "He crossed against the light on Charing Cross Road to see who crossed with him — Quinn didn't, she stayed on her side and let the buses screen her — and he doubled back once through the arcade off Cambridge Circus, which would have worked on a single tail." | | 2 | "But his shoulders changed — she saw the exact moment, the small tightening — and then he was running." | | 3 | "He had youth and he had panic and he knew these streets in a way that couldn't be learned from a map — he took a gap between a skip and a wall that Quinn had to turn her shoulders for, vaulted the low chain outside the market gates, went left where the streetlights ended." | | 4 | "For half a stride, his head came round, and she saw his face in the sodium wash — warm brown eyes, wide, and something in them that wasn't fear of arrest." | | 5 | "And below, twelve steps down, an old ox-blood tile arch and the ghost of lettering in the tilework — the disused station, one of the dozens sealed up before the war and forgotten by everyone but urban explorers and the tunnel maintenance crews." | | 6 | "Dozens of them, hundreds maybe, a market's worth, the pitch and roll of haggling — and none of it in any language she could put a name to." | | 7 | "*Control, DS Quinn, requesting urgent assistance, my location is—* And then what." | | 8 | "*DS Morris, last seen —* Twenty-two months of investigation and the coroner's inquest recorded an open verdict, and every senior officer she'd ever respected had looked at her with a particular careful patience, and the patience was worse than the disbelief." | | 9 | "Inside was a token the size of a poker chip, cut from bone — real bone, the lab had said, human, and had refused to be drawn further — with a mark scored into one face that hurt slightly to look at directly." | | 10 | "Everything in eighteen decorated years — everything Morris himself had drilled into her, *never go in blind, Harlow, ever* — said: withdraw, observe, build the case, come back with thirty officers and a plan." | | 11 | "The arch was deeper than it looked — six feet of Victorian brick, sweating — and at the far side a man sat on a stool with a lantern between his knees, a big man, grey-skinned, with a jaw like something built rather than born." | | 12 | "And the Veil Market opened up in front of her — the great curved tunnel of a station platform strung with a thousand hanging lamps, stalls crowding the tiled walls all the way into the dark, cages, glass jars, smoke, a woman with too many joints in her fingers weighing something that screamed faintly on a brass scale — and forty feet away, halfway up the platform, Tomás Herrera stopped dead and turned and saw her come through the arch." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1597 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.028804007514088917 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0031308703819661866 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 15.42 | | std | 13.99 | | cv | 0.908 | | sampleLengths | | 0 | 22 | | 1 | 44 | | 2 | 2 | | 3 | 10 | | 4 | 36 | | 5 | 8 | | 6 | 2 | | 7 | 6 | | 8 | 27 | | 9 | 10 | | 10 | 32 | | 11 | 2 | | 12 | 17 | | 13 | 2 | | 14 | 11 | | 15 | 17 | | 16 | 13 | | 17 | 12 | | 18 | 8 | | 19 | 11 | | 20 | 7 | | 21 | 7 | | 22 | 11 | | 23 | 49 | | 24 | 17 | | 25 | 16 | | 26 | 10 | | 27 | 31 | | 28 | 16 | | 29 | 4 | | 30 | 9 | | 31 | 20 | | 32 | 4 | | 33 | 20 | | 34 | 19 | | 35 | 14 | | 36 | 9 | | 37 | 33 | | 38 | 9 | | 39 | 4 | | 40 | 22 | | 41 | 55 | | 42 | 4 | | 43 | 10 | | 44 | 3 | | 45 | 31 | | 46 | 5 | | 47 | 7 | | 48 | 16 | | 49 | 7 |
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| 60.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.41904761904761906 | | totalSentences | 105 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 96 | | matches | | 0 | "Maybe the reflection in the" | | 1 | "Then he was gone down" | | 2 | "Then some tired sergeant sends" | | 3 | "Then she took out the" | | 4 | "Then she went down the" |
| | ratio | 0.052 | |
| 57.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 96 | | matches | | 0 | "She'd spent a good portion" | | 1 | "She'd looked at the floor" | | 2 | "She knew him from the" | | 3 | "She'd read the tribunal transcript" | | 4 | "He came down the step," | | 5 | "He was good, but he" | | 6 | "He checked reflections in the" | | 7 | "He crossed against the light" | | 8 | "She'd worked with Morris for" | | 9 | "She used one now, out" | | 10 | "She sat two carriages down" | | 11 | "She'd seen it in the" | | 12 | "He got off at Camden" | | 13 | "She never knew how." | | 14 | "she shouted, because you had" | | 15 | "She was forty-one and she" | | 16 | "He had youth and he" | | 17 | "Her voice tore" | | 18 | "It was fear *for* her." | | 19 | "She'd seen that look on" |
| | ratio | 0.406 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 96 | | matches | | 0 | "The rain had been going" | | 1 | "Quinn stood in the doorway" | | 2 | "She'd spent a good portion" | | 3 | "The sign flickered — it" | | 4 | "Quinn had counted forty-one people" | | 5 | "That was wrong on its" | | 6 | "She'd looked at the floor" | | 7 | "She knew him from the" | | 8 | "Canvas satchel over one shoulder," | | 9 | "She'd read the tribunal transcript" | | 10 | "*The patient's presentation was not" | | 11 | "Nobody on the panel had" | | 12 | "That was the part that" | | 13 | "He came down the step," | | 14 | "Quinn gave him twelve seconds," | | 15 | "He was good, but he" | | 16 | "He checked reflections in the" | | 17 | "He crossed against the light" | | 18 | "She'd worked with Morris for" | | 19 | "She used one now, out" |
| | ratio | 0.75 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 4 | | matches | | 0 | "*The patient's presentation was not consistent with human physiology.* That was the phrase." | | 1 | "He had youth and he had panic and he knew these streets in a way that couldn't be learned from a map — he took a gap between a skip and a wall that Quinn had to…" | | 2 | "For half a stride, his head came round, and she saw his face in the sodium wash — warm brown eyes, wide, and something in them that wasn't fear of arrest." | | 3 | "And the Veil Market opened up in front of her — the great curved tunnel of a station platform strung with a thousand hanging lamps, stalls crowding the tiled wa…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.5 | | effectiveRatio | 0.333 | |