| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.409 | | leniency | 0.818 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1423 | | 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) | |
| 82.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1423 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanning" | | 1 | "electric" | | 2 | "silk" | | 3 | "velvet" | | 4 | "stomach" |
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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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 18 | | totalWords | 1432 | | ratio | 0.013 | | matches | | 0 | "sorry" | | 1 | "give me ten minutes" | | 2 | "Don't be stupid, Harlow." | | 3 | "DEBTS SETTLED AT THE MOUTH. NO EXCEPTIONS." | | 4 | "dismissed" | | 5 | "Don't." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1342 | | uniqueNames | 29 | | maxNameDensity | 0.82 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Dean | 1 | | Street | 2 | | Tomás | 1 | | Herrera | 3 | | Soho | 2 | | Square | 1 | | Raven | 1 | | Nest | 1 | | Whitechapel | 1 | | London | 2 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Sutton | 1 | | Row | 1 | | Chinese | 1 | | Charing | 1 | | Cross | 1 | | Road | 2 | | West | 1 | | End | 1 | | Tuesday | 1 | | Morris | 2 | | Court | 1 | | Line | 1 | | Quinn | 11 | | God | 1 | | Tube | 1 | | Oxford | 1 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Line" | | 6 | "Quinn" | | 7 | "God" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Raven" | | 4 | "Whitechapel" | | 5 | "London" | | 6 | "Seville" | | 7 | "Charing" | | 8 | "Cross" | | 9 | "Road" | | 10 | "West" | | 11 | "End" | | 12 | "Court" | | 13 | "Oxford" |
| | globalScore | 1 | | windowScore | 1 | |
| 70.63% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 2 | | matches | | 0 | "something between cinnamon and wet copper, and" | | 1 | "felt like when a room clocked a copper" |
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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 | 1432 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 28.64 | | std | 26.99 | | cv | 0.942 | | sampleLengths | | 0 | 50 | | 1 | 15 | | 2 | 7 | | 3 | 104 | | 4 | 32 | | 5 | 44 | | 6 | 9 | | 7 | 24 | | 8 | 4 | | 9 | 30 | | 10 | 4 | | 11 | 39 | | 12 | 45 | | 13 | 58 | | 14 | 68 | | 15 | 3 | | 16 | 39 | | 17 | 8 | | 18 | 18 | | 19 | 4 | | 20 | 34 | | 21 | 38 | | 22 | 83 | | 23 | 11 | | 24 | 7 | | 25 | 4 | | 26 | 7 | | 27 | 49 | | 28 | 53 | | 29 | 25 | | 30 | 3 | | 31 | 39 | | 32 | 14 | | 33 | 111 | | 34 | 27 | | 35 | 4 | | 36 | 82 | | 37 | 24 | | 38 | 3 | | 39 | 2 | | 40 | 26 | | 41 | 2 | | 42 | 16 | | 43 | 35 | | 44 | 11 | | 45 | 71 | | 46 | 6 | | 47 | 27 | | 48 | 6 | | 49 | 7 |
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| 95.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 104 | | matches | | 0 | "was frightened" | | 1 | "was gone" | | 2 | "been ripped" | | 3 | "was worn" |
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| 44.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 215 | | matches | | 0 | "was gaining" | | 1 | "was doing" | | 2 | "was burning" | | 3 | "was carving" | | 4 | "was talking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 116 | | ratio | 0.06 | | matches | | 0 | "Quinn went round, along the railings, betting on the exit at the north corner, and she won the bet — he came out of the gate six feet from her and she got a fistful of his jacket." | | 1 | "He drove his elbow into her forearm — not hard, not a fighter's blow, a paramedic's blow, precise on the nerve — and her hand opened before her brain agreed to it." | | 2 | "He looked back at her once — and God help her, he looked *sorry* — and slipped through." | | 3 | "The rain, the buses, the drunk shouting on Oxford Street — gone, like someone had shut a door in her head." | | 4 | "The escalators had been ripped out decades ago; someone had bolted a wooden stair into the shaft, and the wood was new, and it was worn in the middle where feet had gone up and down it, thousands of feet, for years." | | 5 | "The air changed — got thicker, sweeter, spiced with something between cinnamon and wet copper, and under it a low sound she felt in her sternum before she heard it." | | 6 | "His eyes were entirely ordinary — that was the thing that frightened her — brown, tired, a bit bloodshot, the eyes of a bloke who worked nights." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1337 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.020942408376963352 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002243829468960359 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 12.34 | | std | 12.79 | | cv | 1.036 | | sampleLengths | | 0 | 9 | | 1 | 22 | | 2 | 19 | | 3 | 8 | | 4 | 7 | | 5 | 3 | | 6 | 4 | | 7 | 3 | | 8 | 32 | | 9 | 3 | | 10 | 66 | | 11 | 32 | | 12 | 6 | | 13 | 38 | | 14 | 7 | | 15 | 2 | | 16 | 10 | | 17 | 6 | | 18 | 8 | | 19 | 4 | | 20 | 16 | | 21 | 14 | | 22 | 4 | | 23 | 33 | | 24 | 6 | | 25 | 32 | | 26 | 13 | | 27 | 4 | | 28 | 7 | | 29 | 47 | | 30 | 6 | | 31 | 3 | | 32 | 30 | | 33 | 29 | | 34 | 3 | | 35 | 3 | | 36 | 9 | | 37 | 3 | | 38 | 24 | | 39 | 6 | | 40 | 2 | | 41 | 18 | | 42 | 4 | | 43 | 34 | | 44 | 3 | | 45 | 5 | | 46 | 30 | | 47 | 11 | | 48 | 44 | | 49 | 6 |
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| 65.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4482758620689655 | | totalSentences | 116 | | uniqueOpeners | 52 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Actually laughed, out loud, and" |
| | ratio | 0.011 | |
| 73.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 90 | | matches | | 0 | "Her shout bounced off brick" | | 1 | "He didn't slow." | | 2 | "She'd had him." | | 3 | "She'd been polite." | | 4 | "She'd sat down across from" | | 5 | "He cut through the square" | | 6 | "She wrenched him against the" | | 7 | "His accent thickened when he" | | 8 | "His eyes went past her" | | 9 | "He looked at her then," | | 10 | "He drove his elbow into" | | 11 | "He was gone up Sutton" | | 12 | "He knew Soho the way" | | 13 | "She ignored it." | | 14 | "She'd have to explain the" | | 15 | "She kept running." | | 16 | "He hit the hoarding at" | | 17 | "She was twenty feet away" | | 18 | "He looked back at her" | | 19 | "She was alone." |
| | ratio | 0.367 | |
| 82.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 90 | | matches | | 0 | "The rain came down like" | | 1 | "Quinn hit Dean Street at" | | 2 | "Her shout bounced off brick" | | 3 | "He didn't slow." | | 4 | "That was answer enough." | | 5 | "She'd had him." | | 6 | "She'd been polite." | | 7 | "She'd sat down across from" | | 8 | "He cut through the square" | | 9 | "Quinn went round, along the" | | 10 | "She wrenched him against the" | | 11 | "His accent thickened when he" | | 12 | "Seville came through the London" | | 13 | "His eyes went past her" | | 14 | "He looked at her then," | | 15 | "He drove his elbow into" | | 16 | "He was gone up Sutton" | | 17 | "He knew Soho the way" | | 18 | "Quinn's radio crackled at her" | | 19 | "She ignored it." |
| | ratio | 0.756 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 90 | | matches | | 0 | "Now her lungs burned and" | | 1 | "If she called it in" |
| | ratio | 0.022 | |
| 69.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn hit Dean Street at a dead run, coat flapping, and the wet hit her face in sheets that stole her breath." | | 1 | "Somebody's child sat on a crate, cross-legged, feeding grapes to something in a cage that had too many joints in its legs." | | 2 | "She stood in her sodden coat with her hand on her useless torch and understood, with a clarity that made her stomach drop, that she was the least dangerous thin…" | | 3 | "His eyes were entirely ordinary — that was the thing that frightened her — brown, tired, a bit bloodshot, the eyes of a bloke who worked nights." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 22 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.25 | | effectiveRatio | 0.091 | |