| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn moved back [back]" |
| | dialogueSentences | 57 | | tagDensity | 0.298 | | leniency | 0.596 | | rawRatio | 0.059 | | effectiveRatio | 0.035 | |
| 94.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1897 | | totalAiIsmAdverbs | 2 | | 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) | |
| 76.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1897 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "chill" | | 1 | "pristine" | | 2 | "weight" | | 3 | "velvet" | | 4 | "measured" | | 5 | "predictable" | | 6 | "absolutely" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1916 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1318 | | uniqueNames | 15 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 19 | | Bovril | 1 | | Camden | 2 | | Farris | 5 | | London | 1 | | November | 1 | | Tube | 1 | | Scene | 2 | | Crime | 2 | | Morris | 2 | | Docklands | 1 | | Kowalski | 1 | | Young | 1 | | Eva | 11 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bovril" | | 3 | "Farris" | | 4 | "Crime" | | 5 | "Morris" | | 6 | "Kowalski" | | 7 | "Eva" |
| | places | | | globalScore | 0.779 | | windowScore | 0.5 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like herbs, tied with copper wire" | | 1 | "as if reaching for the track bed" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.522 | | wordCount | 1916 | | matches | | 0 | "not met, but DCI Marsh" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | 0 | "believed that knowledge" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 31.41 | | std | 29.08 | | cv | 0.926 | | sampleLengths | | 0 | 80 | | 1 | 12 | | 2 | 63 | | 3 | 103 | | 4 | 37 | | 5 | 117 | | 6 | 7 | | 7 | 33 | | 8 | 3 | | 9 | 30 | | 10 | 95 | | 11 | 26 | | 12 | 1 | | 13 | 1 | | 14 | 4 | | 15 | 13 | | 16 | 15 | | 17 | 61 | | 18 | 32 | | 19 | 1 | | 20 | 5 | | 21 | 84 | | 22 | 8 | | 23 | 47 | | 24 | 74 | | 25 | 11 | | 26 | 44 | | 27 | 8 | | 28 | 5 | | 29 | 38 | | 30 | 2 | | 31 | 47 | | 32 | 8 | | 33 | 49 | | 34 | 5 | | 35 | 28 | | 36 | 54 | | 37 | 69 | | 38 | 13 | | 39 | 6 | | 40 | 62 | | 41 | 21 | | 42 | 36 | | 43 | 19 | | 44 | 47 | | 45 | 7 | | 46 | 12 | | 47 | 5 | | 48 | 11 | | 49 | 92 |
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| 92.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 113 | | matches | | 0 | "been pulled" | | 1 | "been frozen" | | 2 | "been placed" | | 3 | "been removed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 203 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 151 | | ratio | 0.06 | | matches | | 0 | "The air changed halfway down — went from the damp chill of a London November to something thicker, warmer, carrying a sweetness like overripe fruit left in a closed room." | | 1 | "His skin had taken on a waxy, grayish pallor that Quinn had seen only once before — on DS Morris, three years ago, laid out on the concrete floor of a Docklands warehouse with that same reaching posture, that same expression of absolute bewilderment frozen into features that had already begun to cool." | | 2 | "She could see the edge of it — brass, gone green with age — poking from between his fingers." | | 3 | "The dust around the body was pristine, settled evenly, as if the man had been placed here with surgical care — or had simply appeared." | | 4 | "This one displayed small items on a velvet cloth — rings, pendants, what appeared to be teeth strung on silver chain." | | 5 | "Fine wool, yes, but the lining was visible where it had pulled away at the collar — hand-stitched, with a repeating pattern embroidered in dark thread." | | 6 | "It wasn't shadow in the way she understood shadow — an absence of light with predictable geometry." | | 7 | "A line — a single, curving line scored into the tile, cut so finely it would have been invisible without the raking angle of the work lamps." | | 8 | "It had a direction, a flow, and if she read it correctly — the way she'd read a thousand crime scenes, tracking trajectories, sight lines, the geometry of violence — whatever had made the mark had come from the tunnel and moved out through the market." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1314 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.02663622526636225 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.00989345509893455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 12.69 | | std | 10.92 | | cv | 0.86 | | sampleLengths | | 0 | 25 | | 1 | 23 | | 2 | 32 | | 3 | 9 | | 4 | 3 | | 5 | 5 | | 6 | 28 | | 7 | 30 | | 8 | 5 | | 9 | 11 | | 10 | 1 | | 11 | 3 | | 12 | 27 | | 13 | 10 | | 14 | 13 | | 15 | 7 | | 16 | 26 | | 17 | 22 | | 18 | 15 | | 19 | 19 | | 20 | 22 | | 21 | 18 | | 22 | 3 | | 23 | 2 | | 24 | 53 | | 25 | 5 | | 26 | 2 | | 27 | 20 | | 28 | 13 | | 29 | 3 | | 30 | 30 | | 31 | 5 | | 32 | 8 | | 33 | 19 | | 34 | 5 | | 35 | 7 | | 36 | 30 | | 37 | 9 | | 38 | 4 | | 39 | 8 | | 40 | 1 | | 41 | 25 | | 42 | 1 | | 43 | 1 | | 44 | 4 | | 45 | 5 | | 46 | 3 | | 47 | 5 | | 48 | 6 | | 49 | 9 |
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| 72.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4768211920529801 | | totalSentences | 151 | | uniqueOpeners | 72 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 99 | | matches | | 0 | "Instead she studied the floor" | | 1 | "Then at the body." | | 2 | "Then at the undisturbed dust" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 99 | | matches | | 0 | "She adjusted, descended the rest" | | 1 | "She pulled a pair of" | | 2 | "He wore a wool coat" | | 3 | "His skin had taken on" | | 4 | "She pressed the memory flat." | | 5 | "She could see the edge" | | 6 | "She didn't touch it yet." | | 7 | "He looked at the floor." | | 8 | "She caught herself, glanced at" | | 9 | "She crouched where Quinn had" | | 10 | "Her breath caught." | | 11 | "She thought about Morris." | | 12 | "She turned to one of" | | 13 | "She measured it against the" | | 14 | "She knelt again, this time" | | 15 | "She pointed to the embroidery" | | 16 | "It wasn't shadow in the" | | 17 | "It sat in the tunnel" | | 18 | "It began at a point" | | 19 | "She followed it with her" |
| | ratio | 0.242 | |
| 50.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 99 | | matches | | 0 | "The platform tiles still bore" | | 1 | "Detective Harlow Quinn ducked beneath" | | 2 | "The false wall had been" | | 3 | "Quinn's boot found the gap." | | 4 | "She adjusted, descended the rest" | | 5 | "The air changed halfway down" | | 6 | "The platform opened before her." | | 7 | "Dozens of them." | | 8 | "Glass bottles filled with liquids" | | 9 | "Books with no titles on" | | 10 | "The whole thing had the" | | 11 | "Quinn said to no one" | | 12 | "She pulled a pair of" | | 13 | "The body was at the" | | 14 | "A man, mid-fifties, heavy build," | | 15 | "He wore a wool coat" | | 16 | "His skin had taken on" | | 17 | "She pressed the memory flat." | | 18 | "Farris said, appearing at her" | | 19 | "Quinn crouched beside the body." |
| | ratio | 0.818 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 9 | | matches | | 0 | "Detective Harlow Quinn ducked beneath the police tape strung across the bricked-up entrance that wasn't, as it turned out, bricked up at all." | | 1 | "The false wall had been pulled aside on a rail system hidden behind a rusted advertisement for Bovril, revealing a passage that dropped into the guts of the aba…" | | 2 | "She adjusted, descended the rest of the narrow staircase with the kind of control that came from years of moving through spaces that didn't want her in them." | | 3 | "Wooden frames draped in oilcloth and heavy canvas, tables still laden with merchandise that caught the light from the halogen work lamps Scene of Crime had erec…" | | 4 | "The whole thing had the feel of a market that had been frozen mid-transaction, its sellers and buyers scattered like roaches when the lights flipped on." | | 5 | "A man, mid-fifties, heavy build, face down on the tile with his right arm extended as if reaching for the track bed." | | 6 | "He wore a wool coat too fine for this place and shoes that had cost someone real money." | | 7 | "His skin had taken on a waxy, grayish pallor that Quinn had seen only once before — on DS Morris, three years ago, laid out on the concrete floor of a Docklands…" | | 8 | "Eva approached the body with the careful steps of someone who spent more time with the dead in books than in person." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |