| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn's hand went automatically [automatically]" |
| | dialogueSentences | 10 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1095 | | 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) | |
| 77.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1095 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "footsteps" | | 2 | "electric" | | 3 | "profound" | | 4 | "familiar" |
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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 | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 50 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1067 | | uniqueNames | 13 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 2 | | Harlow | 1 | | Quinn | 10 | | Camden | 1 | | High | 1 | | Street | 1 | | Sully | 3 | | Hackney | 2 | | Morris | 2 | | Tube | 3 | | Tube-station | 1 | | Superintendent | 1 | | Adeyemi | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sully" | | 3 | "Morris" | | 4 | "Adeyemi" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Hackney" |
| | globalScore | 1 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 2 | | matches | | 0 | "something like it, cast a weak amber glow al" | | 1 | "not quite a smile" |
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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 | 1107 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 48.13 | | std | 31.75 | | cv | 0.66 | | sampleLengths | | 0 | 68 | | 1 | 24 | | 2 | 101 | | 3 | 104 | | 4 | 34 | | 5 | 15 | | 6 | 84 | | 7 | 58 | | 8 | 30 | | 9 | 76 | | 10 | 113 | | 11 | 32 | | 12 | 11 | | 13 | 20 | | 14 | 13 | | 15 | 55 | | 16 | 76 | | 17 | 47 | | 18 | 7 | | 19 | 31 | | 20 | 43 | | 21 | 58 | | 22 | 7 |
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| 82.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 46 | | matches | | 0 | "been worn" | | 1 | "been carved" | | 2 | "been closed" | | 3 | "been peeled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 50 | | ratio | 0.14 | | matches | | 0 | "Eighteen years on the force had taught her that hesitation cost you the whole night, and she had already spent three weeks on this one — three weeks of surveillance photos, of half-heard names in wire recordings, of a man named Sully who dealt in things that didn't have street value anywhere Quinn had ever heard of." | | 1 | "The man in the hoodie — Sully, if her information was right — glanced back once, rain slicking his face into a pale smear, and dove down the steps of the old Tube station." | | 2 | "She followed the sound of him down, one hand trailing along the cold rail, until the escalator dumped her out onto a platform that should have been empty — should have been nothing but dust and old advertising hoardings gone soft with damp — but instead opened up into something that made her stop dead, water dripping steadily off the hem of her coat onto stone that was not Tube-station stone at all." | | 3 | "The murmur of commerce drifted up toward her — haggling in half a dozen languages, the clink of coin that didn't sound like any coin she'd ever handled, a low laugh that raised the hair on the back of her neck." | | 4 | "Somewhere past her, deeper into the gold-lit sprawl, Quinn caught a flash of gray hoodie disappearing between a stall hung with what looked, impossibly, like bottled storms — swirling grey glass jars, lightning flickering thin and slow inside them — and a man selling what she prayed were not actual teeth strung on a wire." | | 5 | "To back out through the hoarding, get on her radio, call this in as — as what, exactly?" | | 6 | "The tusked woman studied her a moment longer, unimpressed, then shrugged, as if Quinn's problems were no longer any of her concern, and stepped aside just enough to let her pass — not an invitation, exactly, more the simple absence of a barrier." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1061 | | adjectiveStacks | 1 | | stackExamples | | 0 | "squat, broad-shouldered woman" |
| | adverbCount | 40 | | adverbRatio | 0.03770028275212064 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008482563619227144 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 50 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 50 | | mean | 22.14 | | std | 17.77 | | cv | 0.803 | | sampleLengths | | 0 | 34 | | 1 | 34 | | 2 | 24 | | 3 | 13 | | 4 | 3 | | 5 | 57 | | 6 | 5 | | 7 | 23 | | 8 | 7 | | 9 | 20 | | 10 | 51 | | 11 | 19 | | 12 | 7 | | 13 | 34 | | 14 | 10 | | 15 | 5 | | 16 | 50 | | 17 | 6 | | 18 | 28 | | 19 | 10 | | 20 | 28 | | 21 | 20 | | 22 | 30 | | 23 | 3 | | 24 | 73 | | 25 | 1 | | 26 | 39 | | 27 | 32 | | 28 | 41 | | 29 | 32 | | 30 | 4 | | 31 | 7 | | 32 | 18 | | 33 | 2 | | 34 | 9 | | 35 | 4 | | 36 | 55 | | 37 | 8 | | 38 | 18 | | 39 | 10 | | 40 | 40 | | 41 | 4 | | 42 | 28 | | 43 | 15 | | 44 | 7 | | 45 | 25 | | 46 | 6 | | 47 | 43 | | 48 | 58 | | 49 | 7 |
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| 94.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.62 | | totalSentences | 50 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 45 | | matches | | 0 | "Somewhere past her, deeper into" | | 1 | "Maybe this was the start" |
| | ratio | 0.044 | |
| 68.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 45 | | matches | | 0 | "she shouted, though she already" | | 1 | "Her boots hit a puddle" | | 2 | "She didn't slow." | | 3 | "She didn't believe in any" | | 4 | "She believed in evidence, in" | | 5 | "She had never told anyone" | | 6 | "She wasn't sure she believed" | | 7 | "she barked, closing the distance" | | 8 | "She went through the gap" | | 9 | "She could hear Sully's footsteps" | | 10 | "she called after him, though" | | 11 | "He didn't answer." | | 12 | "She followed the sound of" | | 13 | "Her voice was gravel dragged" | | 14 | "She thought of the report" | | 15 | "She thought of Morris." | | 16 | "She stepped forward anyway, into" |
| | ratio | 0.378 | |
| 71.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 45 | | matches | | 0 | "Rain came down in sheets" | | 1 | "she shouted, though she already" | | 2 | "Her boots hit a puddle" | | 3 | "She didn't slow." | | 4 | "Powders that glowed faintly blue." | | 5 | "A ring that, according to" | | 6 | "She didn't believe in any" | | 7 | "She believed in evidence, in" | | 8 | "She had never told anyone" | | 9 | "She wasn't sure she believed" | | 10 | "The man in the hoodie" | | 11 | "she barked, closing the distance" | | 12 | "The station had been closed" | | 13 | "Quinn didn't stop to consider" | | 14 | "She went through the gap" | | 15 | "The old ticket hall smelled" | | 16 | "Emergency lighting, or something like" | | 17 | "She could hear Sully's footsteps" | | 18 | "she called after him, though" | | 19 | "He didn't answer." |
| | ratio | 0.778 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 45 | | matches | | 0 | "To back out through the" |
| | ratio | 0.022 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 8 | | matches | | 0 | "Rain came down in sheets that turned the sodium streetlights into smeared halos, and Detective Harlow Quinn ran through all of it with her lungs burning and her…" | | 1 | "Eighteen years on the force had taught her that hesitation cost you the whole night, and she had already spent three weeks on this one — three weeks of surveill…" | | 2 | "A ring that, according to her informant, had been worn by a man who'd been dead for two years before he wore it." | | 3 | "But she believed, too, in the hollow place under her ribs that had been carved out three years ago on a rooftop in Hackney, when DS Morris had gone over the edg…" | | 4 | "She followed the sound of him down, one hand trailing along the cold rail, until the escalator dumped her out onto a platform that should have been empty — shou…" | | 5 | "Dozens of them, strung along a passage that stretched farther than any Tube platform had a right to, hanging from black iron hooks and burning with a low, stead…" | | 6 | "The murmur of commerce drifted up toward her — haggling in half a dozen languages, the clink of coin that didn't sound like any coin she'd ever handled, a low l…" | | 7 | "The tusked woman studied her a moment longer, unimpressed, then shrugged, as if Quinn's problems were no longer any of her concern, and stepped aside just enoug…" |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she called, though her voice sounded small in the dead air, swallowed by the tiled curves of the tunnel" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she barked (bark)" |
| | dialogueSentences | 10 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.4 | | effectiveRatio | 0.4 | |