| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1754 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slowly" | | 1 | "deliberately" | | 2 | "completely" |
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| 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) | |
| 71.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1754 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "database" | | 3 | "familiar" | | 4 | "flickered" | | 5 | "whisper" | | 6 | "silk" | | 7 | "charged" | | 8 | "scanned" | | 9 | "tapestry" |
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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 | 157 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 157 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 168 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1737 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1597 | | uniqueNames | 18 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 13 | | Morris | 6 | | Camden | 2 | | High | 1 | | Street | 1 | | Saint | 2 | | Christopher | 2 | | National | 1 | | Insurance | 1 | | Glock | 2 | | Seville | 2 | | Victorian-era | 1 | | Underground | 1 | | Tube | 1 | | East | 1 | | End | 1 | | London | 1 | | Herrera | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Glock" | | 5 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Seville" | | 4 | "East" | | 5 | "End" | | 6 | "London" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like whispering" | | 1 | "looked like animal bones wrapped in coppe" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.576 | | wordCount | 1737 | | matches | | 0 | "not in deference but in avoidance" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 168 | | matches | | 0 | "understanding that backup" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 33.4 | | std | 23.63 | | cv | 0.707 | | sampleLengths | | 0 | 57 | | 1 | 9 | | 2 | 44 | | 3 | 3 | | 4 | 33 | | 5 | 63 | | 6 | 57 | | 7 | 10 | | 8 | 59 | | 9 | 7 | | 10 | 67 | | 11 | 37 | | 12 | 69 | | 13 | 6 | | 14 | 40 | | 15 | 46 | | 16 | 30 | | 17 | 27 | | 18 | 9 | | 19 | 41 | | 20 | 29 | | 21 | 3 | | 22 | 5 | | 23 | 54 | | 24 | 29 | | 25 | 26 | | 26 | 7 | | 27 | 57 | | 28 | 6 | | 29 | 53 | | 30 | 28 | | 31 | 3 | | 32 | 60 | | 33 | 57 | | 34 | 43 | | 35 | 21 | | 36 | 95 | | 37 | 81 | | 38 | 23 | | 39 | 66 | | 40 | 10 | | 41 | 39 | | 42 | 26 | | 43 | 5 | | 44 | 6 | | 45 | 9 | | 46 | 38 | | 47 | 27 | | 48 | 10 | | 49 | 51 |
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| 94.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 157 | | matches | | 0 | "were gone" | | 1 | "been found" | | 2 | "been cracked" | | 3 | "were gone" | | 4 | "were filed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 270 | | matches | | 0 | "wasn't looking" | | 1 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 0 | | flaggedSentences | 15 | | totalSentences | 168 | | ratio | 0.089 | | matches | | 0 | "The figure—male, dark jacket, hood up—glanced back." | | 1 | "A man who shouldn't exist in any police database—no NHS record, no National Insurance number, no passport." | | 2 | "This wasn't panic—it was preparation." | | 3 | "Herrera stood at the edge of a dead end—or what should have been a dead end." | | 4 | "The steps vanished into a tunnel that smelled of damp stone and something else—something sweet and chemical, like the air above a bonfire mixed with incense." | | 5 | "The glass face was cracked—had been cracked since the night she'd found Morris's body." | | 6 | "The air thickened with each step—warmer, heavier, saturated with those competing smells of smoke and spice and something metallic she couldn't name." | | 7 | "And beneath the voices, movement—shuffling feet, the clink of glass, the whisper of fabric." | | 8 | "The tracks were gone—covered over with wooden boards and rugs that created a continuous floor from wall to wall." | | 9 | "They wore every kind of clothing imaginable—leather, silk, hooded cloaks, business suits—and some of them wore things Quinn's eyes refused to process." | | 10 | "Incense, copper, ozone, and something underneath it all like the air before a lightning strike—charged, dangerous, waiting." | | 11 | "The crowd parted for her—not in deference but in avoidance." | | 12 | "\"—bone token? Where's her token—\"" | | 13 | "\"—smells like surface. Smells like badge—\"" | | 14 | "\"—the green sign, take her to the green sign—\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1556 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.021208226221079693 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.005784061696658098 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 168 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 168 | | mean | 10.34 | | std | 7.74 | | cv | 0.748 | | sampleLengths | | 0 | 11 | | 1 | 13 | | 2 | 6 | | 3 | 9 | | 4 | 18 | | 5 | 9 | | 6 | 15 | | 7 | 10 | | 8 | 19 | | 9 | 3 | | 10 | 7 | | 11 | 2 | | 12 | 2 | | 13 | 22 | | 14 | 3 | | 15 | 14 | | 16 | 23 | | 17 | 3 | | 18 | 20 | | 19 | 8 | | 20 | 8 | | 21 | 11 | | 22 | 9 | | 23 | 21 | | 24 | 3 | | 25 | 7 | | 26 | 36 | | 27 | 17 | | 28 | 6 | | 29 | 3 | | 30 | 4 | | 31 | 8 | | 32 | 7 | | 33 | 16 | | 34 | 3 | | 35 | 15 | | 36 | 18 | | 37 | 25 | | 38 | 4 | | 39 | 3 | | 40 | 5 | | 41 | 6 | | 42 | 12 | | 43 | 4 | | 44 | 2 | | 45 | 11 | | 46 | 6 | | 47 | 28 | | 48 | 6 | | 49 | 16 |
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| 57.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3869047619047619 | | totalSentences | 168 | | uniqueOpeners | 65 | |
| 47.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 139 | | matches | | 0 | "Of course he didn't." | | 1 | "Then she descended." |
| | ratio | 0.014 | |
| 90.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 139 | | matches | | 0 | "She killed the engine and" | | 1 | "Her shoes slapped wet pavement" | | 2 | "He scrambled over a low" | | 3 | "She'd given up cigarettes eight" | | 4 | "Her knee protested." | | 5 | "She pushed harder, her jacket" | | 6 | "He'd already climbed it, his" | | 7 | "He didn't stop." | | 8 | "She dropped the other side" | | 9 | "He knew these routes." | | 10 | "He'd planned them." | | 11 | "Her hand went to her" | | 12 | "She hadn't drawn it." | | 13 | "You didn't draw on a" | | 14 | "She rounded a corner and" | | 15 | "He was looking at the" | | 16 | "His chest heaved." | | 17 | "His voice carried over the" | | 18 | "He took a step down" | | 19 | "He descended another step." |
| | ratio | 0.324 | |
| 31.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 139 | | matches | | 0 | "Harlow Quinn gripped the steering" | | 1 | "Tonight, a name had dropped" | | 2 | "A figure darted between parked" | | 3 | "She killed the engine and" | | 4 | "The rain hit her like" | | 5 | "Her shoes slapped wet pavement" | | 6 | "The figure—male, dark jacket, hood" | | 7 | "He scrambled over a low" | | 8 | "Quinn's lungs burned." | | 9 | "She'd given up cigarettes eight" | | 10 | "The watch on her left" | | 11 | "Her knee protested." | | 12 | "The alley stank of grease" | | 13 | "The suspect's footsteps echoed off" | | 14 | "She pushed harder, her jacket" | | 15 | "A chain-link fence blocked the" | | 16 | "He'd already climbed it, his" | | 17 | "The name belonged to a" | | 18 | "A man who shouldn't exist" | | 19 | "A ghost with warm brown" |
| | ratio | 0.856 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 51.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 9 | | matches | | 0 | "The name belonged to a 29-year-old former paramedic with olive skin, a knife scar on his forearm, and a Saint Christopher medallion that witnesses had described…" | | 1 | "A man who shouldn't exist in any police database—no NHS record, no National Insurance number, no passport." | | 2 | "The steps vanished into a tunnel that smelled of damp stone and something else—something sweet and chemical, like the air above a bonfire mixed with incense." | | 3 | "Some part of her understood that fixing it would mean moving on, and moving on meant forgetting, and forgetting meant the thing that killed him won." | | 4 | "But someone had strung bare bulbs along the ceiling at irregular intervals, and the light they cast was amber and unsteady, humming at a frequency that made the…" | | 5 | "Dozens of them, murmuring in overlapping conversations that bounced off the tunnel walls and merged into a single wall of sound." | | 6 | "The tracks were gone—covered over with wooden boards and rugs that created a continuous floor from wall to wall." | | 7 | "A stall selling jars of liquid that glowed faintly blue, arranged next to bundles of dried herbs and what looked like animal bones wrapped in copper wire." | | 8 | "Quinn stood alone in the middle of a black market she'd never known existed, rainwater still dripping from her jacket onto the wooden boards beneath her feet, w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 20 | | tagDensity | 0.05 | | leniency | 0.1 | | rawRatio | 1 | | effectiveRatio | 0.1 | |