| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1427 | | 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) | |
| 78.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1427 | | totalAiIsms | 6 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "down her spine" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "echo" | | 4 | "silk" | | 5 | "could feel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 136 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 136 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1414 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1391 | | uniqueNames | 15 | | maxNameDensity | 0.79 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 11 | | Herrera | 10 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Met | 1 | | You | 2 | | Tomás | 2 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Morris" | | 8 | "Market" | | 9 | "You" | | 10 | "Tomás" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | 0 | "-soaked, still obviously police" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.707 | | wordCount | 1414 | | matches | | 0 | "not the hollow echo of an empty tunnel, but voices" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 38.22 | | std | 30.24 | | cv | 0.791 | | sampleLengths | | 0 | 69 | | 1 | 77 | | 2 | 9 | | 3 | 41 | | 4 | 2 | | 5 | 3 | | 6 | 100 | | 7 | 15 | | 8 | 63 | | 9 | 83 | | 10 | 76 | | 11 | 5 | | 12 | 46 | | 13 | 17 | | 14 | 55 | | 15 | 72 | | 16 | 76 | | 17 | 8 | | 18 | 85 | | 19 | 25 | | 20 | 4 | | 21 | 5 | | 22 | 96 | | 23 | 31 | | 24 | 29 | | 25 | 8 | | 26 | 34 | | 27 | 67 | | 28 | 54 | | 29 | 35 | | 30 | 3 | | 31 | 35 | | 32 | 4 | | 33 | 30 | | 34 | 20 | | 35 | 5 | | 36 | 27 |
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| 87.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 136 | | matches | | 0 | "been peeled" | | 1 | "been abandoned" | | 2 | "been painted" | | 3 | "was obscured" | | 4 | "got reported" | | 5 | "been transformed" | | 6 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 234 | | matches | | 0 | "was crying" | | 1 | "was telling" | | 2 | "was waiting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 138 | | ratio | 0.072 | | matches | | 0 | "She kept her pace steady, shoulders squared, eyes fixed on the figure forty yards ahead—short, dark-haired, moving with the hunched urgency of a man who knew exactly how much trouble followed him." | | 1 | "She gave him ten seconds to notice her, and when he glanced back—just once, under the sickly orange glow of a streetlamp—she saw his face change." | | 2 | "The sign above the entrance had been painted over, but the shape of it remained—the rounded arch, the ironwork, the tiled lintel." | | 3 | "She could hear sounds from below—not the hollow echo of an empty tunnel, but voices." | | 4 | "Her job was the world above—the one with warrants and custody records and CCTV." | | 5 | "The old platform had been transformed—stalls built from pallets and scaffolding, tarpaulins strung overhead, bulbs hanging on cords that looped from the ceiling." | | 6 | "A child—a child—selling glass vials from a tray." | | 7 | "She followed the flow of people down a ramp, past a stall selling bottled memories, past a woman who whispered her name—her real name, not her rank—and past a curtained alcove where someone was crying quietly." | | 8 | "And ahead, at the edge of the darkness, a door closed—a heavy metal door, rusted and riveted, with a symbol on it she didn’t recognise." | | 9 | "The darkness beyond smelled of rain and old stone and something else—something patient." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1409 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.027679205110007096 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0056777856635912 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 10.25 | | std | 8.43 | | cv | 0.823 | | sampleLengths | | 0 | 7 | | 1 | 30 | | 2 | 32 | | 3 | 2 | | 4 | 2 | | 5 | 28 | | 6 | 17 | | 7 | 28 | | 8 | 9 | | 9 | 3 | | 10 | 6 | | 11 | 26 | | 12 | 1 | | 13 | 2 | | 14 | 3 | | 15 | 2 | | 16 | 3 | | 17 | 25 | | 18 | 6 | | 19 | 13 | | 20 | 22 | | 21 | 13 | | 22 | 7 | | 23 | 7 | | 24 | 7 | | 25 | 6 | | 26 | 9 | | 27 | 18 | | 28 | 28 | | 29 | 5 | | 30 | 12 | | 31 | 4 | | 32 | 4 | | 33 | 16 | | 34 | 11 | | 35 | 22 | | 36 | 1 | | 37 | 16 | | 38 | 9 | | 39 | 3 | | 40 | 3 | | 41 | 22 | | 42 | 10 | | 43 | 22 | | 44 | 7 | | 45 | 9 | | 46 | 5 | | 47 | 11 | | 48 | 4 | | 49 | 18 |
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| 26.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 33 | | diversityRatio | 0.2608695652173913 | | totalSentences | 138 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 123 | | matches | | 0 | "Then the decision." | | 1 | "Somewhere deeper, a burst of" | | 2 | "Then he was gone, swallowed" | | 3 | "Somewhere in that darkness, Tomás" |
| | ratio | 0.033 | |
| 37.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 123 | | matches | | 0 | "It came off the canal" | | 1 | "She kept her pace steady," | | 2 | "He had a Saint Christopher" | | 3 | "He had been leaving the" | | 4 | "She didn’t shout." | | 5 | "She didn’t break into a" | | 6 | "She gave him ten seconds" | | 7 | "Her coat snapped around her" | | 8 | "She was forty-one years old" | | 9 | "Her worn leather watch bit" | | 10 | "Her breath came in hard," | | 11 | "You even panic like you’re" | | 12 | "She still heard him at" | | 13 | "He didn’t stop for it." | | 14 | "He disappeared between two buildings," | | 15 | "She didn’t vault it." | | 16 | "She went through the gap" | | 17 | "She scooped it up without" | | 18 | "She shoved it into her" | | 19 | "Her radio had been hissing" |
| | ratio | 0.455 | |
| 12.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 123 | | matches | | 0 | "The rain hit Camden like" | | 1 | "It came off the canal" | | 2 | "She kept her pace steady," | | 3 | "He had a Saint Christopher" | | 4 | "He had been leaving the" | | 5 | "Quinn had been waiting in" | | 6 | "She didn’t shout." | | 7 | "She didn’t break into a" | | 8 | "She gave him ten seconds" | | 9 | "Quinn ran too." | | 10 | "The chase went west along" | | 11 | "Her coat snapped around her" | | 12 | "Rain found the gap at" | | 13 | "She was forty-one years old" | | 14 | "Her worn leather watch bit" | | 15 | "Her breath came in hard," | | 16 | "Military precision, Morris used to" | | 17 | "You even panic like you’re" | | 18 | "Morris had been dead three" | | 19 | "She still heard him at" |
| | ratio | 0.894 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 37.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 8 | | matches | | 0 | "It came off the canal in sheets, drummed on the awnings of shuttered kebab shops, and turned the pavement into a black mirror that kept breaking under Harlow Qu…" | | 1 | "She kept her pace steady, shoulders squared, eyes fixed on the figure forty yards ahead—short, dark-haired, moving with the hunched urgency of a man who knew ex…" | | 2 | "Every detective in the Met had heard the whispers, usually from informants who were drunk or desperate or both." | | 3 | "Herrera, who had been treating people off the books for years, stitching up wounds that never got reported, prescribing things that never came from a pharmacy." | | 4 | "The old platform had been transformed—stalls built from pallets and scaffolding, tarpaulins strung overhead, bulbs hanging on cords that looped from the ceiling…" | | 5 | "The tracks were still there, two dark rails running through the middle of everything, and people walked between them as if it were the most natural thing in the…" | | 6 | "He watched her pass with a smile that was all teeth." | | 7 | "She followed the flow of people down a ramp, past a stall selling bottled memories, past a woman who whispered her name—her real name, not her rank—and past a c…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |