| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.476 | | leniency | 0.952 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1104 | | 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) | |
| 86.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1104 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "etched" | | 1 | "trembled" | | 2 | "weight" |
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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 | 66 | | matches | (empty) | |
| 99.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 66 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 77 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1094 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 779 | | uniqueNames | 9 | | maxNameDensity | 1.41 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 1 | | Quinn | 11 | | Amit | 1 | | Rao | 5 | | Bermondsey | 1 | | Morris | 2 | | Eva | 2 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Amit" | | 3 | "Rao" | | 4 | "Morris" | | 5 | "Eva" | | 6 | "Kowalski" |
| | places | | | globalScore | 0.794 | | windowScore | 0.833 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "something like that once, three years ago, a" |
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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 | 1094 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 77 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 39.07 | | std | 24.6 | | cv | 0.63 | | sampleLengths | | 0 | 38 | | 1 | 71 | | 2 | 43 | | 3 | 1 | | 4 | 32 | | 5 | 68 | | 6 | 31 | | 7 | 51 | | 8 | 19 | | 9 | 68 | | 10 | 19 | | 11 | 3 | | 12 | 31 | | 13 | 81 | | 14 | 8 | | 15 | 62 | | 16 | 20 | | 17 | 60 | | 18 | 47 | | 19 | 69 | | 20 | 32 | | 21 | 20 | | 22 | 51 | | 23 | 27 | | 24 | 4 | | 25 | 92 | | 26 | 30 | | 27 | 16 |
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| 89.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 66 | | matches | | 0 | "been closed" | | 1 | "was disturbed" | | 2 | "were paired" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 139 | | matches | | 0 | "wasn't pointing" | | 1 | "wasn't pointing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 77 | | ratio | 0.104 | | matches | | 0 | "The stairs down into the old Camden passage smelled of wet stone and something underneath it—something coppery and sweet that Harlow Quinn had learned, over eighteen years, to associate with bodies left too long in the wrong place." | | 1 | "The crime scene techs had rigged lamps along the platform, and their light made everything worse—harsh shadows, no hiding places." | | 2 | "He could have been sleeping rough, except rough sleepers didn't wear coats that cost more than Quinn's monthly rent, and they didn't carry nothing—no wallet, no phone, no keys." | | 3 | "It wasn't pointing anywhere consistently—it trembled, wandered, swung back toward the far tunnel like a hound pulling at a lead." | | 4 | "\"There's more. Or rather, there's less. SOCO went over the platform twice. No footprints in the dust except the victim's and the explorers'. No fibres. No fingerprints anywhere on the body. But—\" he flipped a page—\"there are scorch marks on the rails. Spaced evenly, every three metres or so, all down the tunnel. And the pathologist found this in the victim's fist.\"" | | 5 | "She'd seen something like that once, three years ago, at a warehouse in Bermondsey—the case that had taken Morris." | | 6 | "The victim's footprints came up the stairs and along the platform—she tracked them backwards—and stopped." | | 7 | "But they weren't random scorch—they were paired, one on each rail, directly opposite one another, like the cross-ties of a ladder laid flat." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 495 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.022222222222222223 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.006060606060606061 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 77 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 77 | | mean | 14.21 | | std | 12.79 | | cv | 0.9 | | sampleLengths | | 0 | 38 | | 1 | 19 | | 2 | 10 | | 3 | 22 | | 4 | 20 | | 5 | 14 | | 6 | 29 | | 7 | 1 | | 8 | 11 | | 9 | 21 | | 10 | 4 | | 11 | 25 | | 12 | 29 | | 13 | 10 | | 14 | 2 | | 15 | 13 | | 16 | 12 | | 17 | 4 | | 18 | 26 | | 19 | 5 | | 20 | 20 | | 21 | 5 | | 22 | 14 | | 23 | 6 | | 24 | 62 | | 25 | 6 | | 26 | 8 | | 27 | 5 | | 28 | 3 | | 29 | 31 | | 30 | 23 | | 31 | 6 | | 32 | 4 | | 33 | 19 | | 34 | 29 | | 35 | 8 | | 36 | 10 | | 37 | 52 | | 38 | 5 | | 39 | 15 | | 40 | 24 | | 41 | 1 | | 42 | 1 | | 43 | 6 | | 44 | 15 | | 45 | 13 | | 46 | 3 | | 47 | 1 | | 48 | 2 | | 49 | 18 |
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| 93.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5844155844155844 | | totalSentences | 77 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 56 | | matches | | 0 | "Instead she walked the platform" | | 1 | "Just stopped, eight metres from" | | 2 | "Evenly spaced, Rao had said." |
| | ratio | 0.054 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 56 | | matches | | 0 | "She ducked under the tape" | | 1 | "He sat propped against the" | | 2 | "He could have been sleeping" | | 3 | "She angled her torch." | | 4 | "It wasn't pointing anywhere consistently—it" | | 5 | "he flipped a page—\"there are" | | 6 | "He held up an evidence" | | 7 | "It took Quinn a moment." | | 8 | "She'd seen something like that" | | 9 | "She pushed the memory down" | | 10 | "It was a tidy story." | | 11 | "She crouched again." | | 12 | "You didn't do that for" | | 13 | "She looked at them again" |
| | ratio | 0.25 | |
| 76.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 56 | | matches | | 0 | "The stairs down into the" | | 1 | "She ducked under the tape" | | 2 | "The station had been closed" | | 3 | "The crime scene techs had" | | 4 | "DS Amit Rao met her" | | 5 | "Rao led her along the" | | 6 | "The man did, too." | | 7 | "He sat propped against the" | | 8 | "He could have been sleeping" | | 9 | "The dead man's hands lay" | | 10 | "She angled her torch." | | 11 | "A small brass compass, greened" | | 12 | "The needle wasn't pointing north." | | 13 | "It wasn't pointing anywhere consistently—it" | | 14 | "he flipped a page—\"there are" | | 15 | "He held up an evidence" | | 16 | "It took Quinn a moment." | | 17 | "Quinn stood slowly and looked" | | 18 | "The scorch marks on the" | | 19 | "The evenly spaced burns." |
| | ratio | 0.768 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "He could have been sleeping rough, except rough sleepers didn't wear coats that cost more than Quinn's monthly rent, and they didn't carry nothing—no wallet, no…" | | 1 | "Green eyes that didn't flinch when Quinn put on her face." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "the fire investigator had (have)" |
| | dialogueSentences | 21 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.167 | | effectiveRatio | 0.095 | |