| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "The woman's smile showed too [too]" |
| | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.25 | | effectiveRatio | 0.154 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1159 | | 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) | |
| 78.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1159 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulsed" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "velvet" | | 4 | "pulse" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1159 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.95% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1041 | | uniqueNames | 13 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 15 | | Neal | 1 | | Yard | 1 | | London | 2 | | Daniel | 2 | | Ostrowski | 7 | | Camden | 2 | | High | 1 | | Street | 1 | | Underground | 1 | | Tube | 1 | | Morris | 3 | | Hendon | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Neal" | | 2 | "Daniel" | | 3 | "Ostrowski" | | 4 | "Morris" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Hendon" |
| | globalScore | 0.78 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "something like burnt sugar threading through" |
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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 | 1159 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 33.11 | | std | 29.82 | | cv | 0.9 | | sampleLengths | | 0 | 16 | | 1 | 5 | | 2 | 6 | | 3 | 112 | | 4 | 40 | | 5 | 59 | | 6 | 7 | | 7 | 20 | | 8 | 38 | | 9 | 69 | | 10 | 4 | | 11 | 11 | | 12 | 75 | | 13 | 3 | | 14 | 71 | | 15 | 44 | | 16 | 98 | | 17 | 19 | | 18 | 7 | | 19 | 21 | | 20 | 11 | | 21 | 29 | | 22 | 42 | | 23 | 87 | | 24 | 15 | | 25 | 35 | | 26 | 2 | | 27 | 31 | | 28 | 80 | | 29 | 31 | | 30 | 36 | | 31 | 9 | | 32 | 7 | | 33 | 6 | | 34 | 13 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "been trained" | | 1 | "been dressed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 179 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 86 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1048 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.03244274809160305 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006679389312977099 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 13.48 | | std | 11.17 | | cv | 0.829 | | sampleLengths | | 0 | 16 | | 1 | 5 | | 2 | 3 | | 3 | 3 | | 4 | 24 | | 5 | 30 | | 6 | 12 | | 7 | 11 | | 8 | 8 | | 9 | 13 | | 10 | 14 | | 11 | 28 | | 12 | 3 | | 13 | 9 | | 14 | 17 | | 15 | 3 | | 16 | 9 | | 17 | 30 | | 18 | 7 | | 19 | 2 | | 20 | 10 | | 21 | 8 | | 22 | 14 | | 23 | 2 | | 24 | 11 | | 25 | 11 | | 26 | 34 | | 27 | 18 | | 28 | 17 | | 29 | 2 | | 30 | 2 | | 31 | 8 | | 32 | 3 | | 33 | 25 | | 34 | 6 | | 35 | 25 | | 36 | 10 | | 37 | 9 | | 38 | 3 | | 39 | 21 | | 40 | 17 | | 41 | 33 | | 42 | 6 | | 43 | 8 | | 44 | 15 | | 45 | 9 | | 46 | 6 | | 47 | 55 | | 48 | 43 | | 49 | 19 |
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| 76.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5 | | totalSentences | 86 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 67 | | matches | | 0 | "Somewhere behind her a car" | | 1 | "Then she caught the flash" | | 2 | "Somewhere below, a light pulsed" | | 3 | "Just Morris's radio, still transmitting" | | 4 | "Somewhere in the market's throat," |
| | ratio | 0.075 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 67 | | matches | | 0 | "He didn't stop." | | 1 | "She catalogued him as she" | | 2 | "He cut left down a" | | 3 | "She'd chased men into worse" | | 4 | "Her lungs burned." | | 5 | "She'd have laughed herself if" | | 6 | "She'd walked past that gate" | | 7 | "She should have called for" | | 8 | "She should have waited for" | | 9 | "She kept moving." | | 10 | "She'd waited outside for backup." | | 11 | "She thought of Morris's radio," | | 12 | "She thought about turning round." |
| | ratio | 0.194 | |
| 79.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 67 | | matches | | 0 | "The suspect's trainers hit a" | | 1 | "He didn't stop." | | 2 | "Nobody ever did." | | 3 | "Quinn's boots hammered the wet" | | 4 | "She catalogued him as she" | | 5 | "The file on her desk" | | 6 | "The look he'd given her" | | 7 | "Something that made the hair" | | 8 | "He cut left down a" | | 9 | "Quinn followed anyway." | | 10 | "She'd chased men into worse" | | 11 | "Water sluiced off the awnings" | | 12 | "Her lungs burned." | | 13 | "Ostrowski's shoulders ducked low as" | | 14 | "That wasn't the reaction of" | | 15 | "Quinn's hand went to the" | | 16 | "Nothing but static, in the" | | 17 | "She'd have laughed herself if" | | 18 | "The chase spilled out onto" | | 19 | "A padlocked gate hung wide," |
| | ratio | 0.761 | |
| 74.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 67 | | matches | | 0 | "By the time she'd gone" |
| | ratio | 0.015 | |
| 3.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 7 | | matches | | 0 | "Eighteen years chasing men through London's back alleys had taught her one thing above all else: fit men who ran from coppers ran because they had something wor…" | | 1 | "Quinn's torch beam skittered off cracked tile, off a faded Underground roundel with a name she didn't recognise, CAMDEN HOLLOW, in a font that looked decades to…" | | 2 | "The stairs opened into what used to be a platform, and Quinn's torch beam died in the open air, swallowed by a light that came from nowhere and everywhere, lant…" | | 3 | "Rows of stalls stretched into a dark that shouldn't have existed under a Tube station, hawkers calling out in accents that curled strangely around vowels, wares…" | | 4 | "Ostrowski's laugh rang out again, further off now, swallowed by the market's low murmur, its clink of coin and creak of leather and something underneath all of …" | | 5 | "Ostrowski, Daniel, twenty-six, no fixed address, three complaints filed by neighbours about lights and noises at odd hours, one witness statement that mentioned…" | | 6 | "Somewhere in the market's throat, Ostrowski's laughter rose one more time, and underneath it, fainter, another sound threaded through, something that might have…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "The woman's head tilted, birdlike, unblinking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |