| 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 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1470 | | 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) | |
| 89.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1470 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 122 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 122 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 18 | | totalWords | 1470 | | ratio | 0.012 | | matches | | 0 | "Oi, it's the copper from Tuesday." | | 1 | "away" | | 2 | "toward" | | 3 | "So who was the second one for?" | | 4 | "misadventure" | | 5 | "Not here." |
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| 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 | 44 | | wordCount | 1461 | | uniqueNames | 20 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 3 | | High | 2 | | Street | 2 | | Harlow | 1 | | Quinn | 11 | | Tomás | 2 | | Herrera | 8 | | Soho | 2 | | Vauxhall | 1 | | Raven | 1 | | Nest | 1 | | Arsenal | 1 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Underground | 1 | | Morris | 2 | | Walthamstow | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Nest" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Vauxhall" | | 5 | "Raven" | | 6 | "Arsenal" | | 7 | "London" | | 8 | "Walthamstow" | | 9 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 2 | | matches | | 0 | "quite fear" | | 1 | "not quite faces at all" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.68 | | wordCount | 1470 | | matches | | 0 | "not like a suspect cornered but like a man watching someone step toward the edge of a platfo" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 35 | | std | 26.84 | | cv | 0.767 | | sampleLengths | | 0 | 36 | | 1 | 23 | | 2 | 90 | | 3 | 7 | | 4 | 54 | | 5 | 30 | | 6 | 37 | | 7 | 79 | | 8 | 15 | | 9 | 60 | | 10 | 11 | | 11 | 65 | | 12 | 8 | | 13 | 3 | | 14 | 32 | | 15 | 44 | | 16 | 6 | | 17 | 66 | | 18 | 24 | | 19 | 7 | | 20 | 64 | | 21 | 23 | | 22 | 5 | | 23 | 57 | | 24 | 61 | | 25 | 8 | | 26 | 64 | | 27 | 20 | | 28 | 111 | | 29 | 45 | | 30 | 42 | | 31 | 2 | | 32 | 3 | | 33 | 77 | | 34 | 12 | | 35 | 32 | | 36 | 41 | | 37 | 10 | | 38 | 34 | | 39 | 10 | | 40 | 38 | | 41 | 14 |
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| 93.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 122 | | matches | | 0 | "been pried" | | 1 | "was gone" | | 2 | "been trained" | | 3 | "been hung" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 232 | | matches | | 0 | "was sprinting" | | 1 | "wasn't running" | | 2 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 124 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1473 | | adjectiveStacks | 1 | | stackExamples | | 0 | "brake-light red bleeding" |
| | adverbCount | 34 | | adverbRatio | 0.02308214528173795 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004073319755600814 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 11.85 | | std | 9.56 | | cv | 0.806 | | sampleLengths | | 0 | 36 | | 1 | 23 | | 2 | 6 | | 3 | 20 | | 4 | 25 | | 5 | 33 | | 6 | 6 | | 7 | 7 | | 8 | 5 | | 9 | 23 | | 10 | 10 | | 11 | 16 | | 12 | 10 | | 13 | 5 | | 14 | 15 | | 15 | 10 | | 16 | 3 | | 17 | 16 | | 18 | 8 | | 19 | 15 | | 20 | 17 | | 21 | 3 | | 22 | 19 | | 23 | 25 | | 24 | 6 | | 25 | 4 | | 26 | 5 | | 27 | 27 | | 28 | 10 | | 29 | 7 | | 30 | 16 | | 31 | 11 | | 32 | 10 | | 33 | 9 | | 34 | 29 | | 35 | 17 | | 36 | 4 | | 37 | 4 | | 38 | 3 | | 39 | 3 | | 40 | 20 | | 41 | 9 | | 42 | 10 | | 43 | 6 | | 44 | 5 | | 45 | 2 | | 46 | 21 | | 47 | 6 | | 48 | 3 | | 49 | 25 |
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| 51.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.35772357723577236 | | totalSentences | 123 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 114 | | matches | | 0 | "Somewhere in there was Tomás" | | 1 | "Somewhere in there, maybe, was" | | 2 | "Then she set the warm" | | 3 | "Then the figure reached back" |
| | ratio | 0.035 | |
| 97.19% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 114 | | matches | | 0 | "She had tailed him from" | | 1 | "She'd let him walk three" | | 2 | "Her shoulder clipped the brickwork," | | 3 | "It never did any good." | | 4 | "She said it anyway, because" | | 5 | "She hit the pallets, planted" | | 6 | "Her knee protested." | | 7 | "She landed badly, caught herself," | | 8 | "He was fast." | | 9 | "She could see something else," | | 10 | "He wasn't running *away*." | | 11 | "He was running *toward* something." | | 12 | "He was halfway through the" | | 13 | "She got a fistful of" | | 14 | "He twisted, and for one" | | 15 | "he said, breathless" | | 16 | "He wrenched sideways." | | 17 | "She crouched and picked it" | | 18 | "It was bone." | | 19 | "She was sure of it" |
| | ratio | 0.307 | |
| 87.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 114 | | matches | | 0 | "The rain had turned Camden" | | 1 | "She had tailed him from" | | 2 | "She'd let him walk three" | | 3 | "Herrera had turned, seen her," | | 4 | "Quinn took the corner hard." | | 5 | "Her shoulder clipped the brickwork," | | 6 | "The alley was narrow and" | | 7 | "The word bounced off the" | | 8 | "It never did any good." | | 9 | "She said it anyway, because" | | 10 | "She hit the pallets, planted" | | 11 | "Her knee protested." | | 12 | "She landed badly, caught herself," | | 13 | "Herrera was sprinting beneath them," | | 14 | "He was fast." | | 15 | "She could see something else," | | 16 | "He wasn't running *away*." | | 17 | "He was running *toward* something." | | 18 | "A council notice curling off" | | 19 | "Herrera went straight for a" |
| | ratio | 0.746 | |
| 43.86% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 114 | | matches | | | ratio | 0.009 | |
| 46.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 8 | | matches | | 0 | "Two hours in an unmarked Vauxhall outside the Raven's Nest, watching that green neon sign buzz above the door, waiting." | | 1 | "Paramedic fast, she thought, the kind of man who'd spent years running up tower-block stairwells with a defib kit." | | 2 | "He twisted, and for one close, rain-streaked second they were face to face: dark curls plastered to his forehead, warm brown eyes wide with something that wasn'…" | | 3 | "And a small, pale disc, no wider than a two-pence coin, that caught the streetlight and glowed faintly, like old ivory." | | 4 | "It drifted up the stairwell on air that smelled of candle wax and cloves and something sharper underneath, a metallic tang like the air before a lightning storm…" | | 5 | "It had been a routine thing, a missing-persons lead, nothing that should have gone wrong." | | 6 | "The same clique who drank under those old maps and faded photographs and fell silent whenever she walked in." | | 7 | "The figure tilted its head, a slow, considering motion, as though it had caught a scent it didn't recognize." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |