| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1410 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "slightly" | | 2 | "gently" |
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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) | |
| 75.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1410 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "footsteps" | | 1 | "footfall" | | 2 | "weight" | | 3 | "scanned" | | 4 | "measured" | | 5 | "flicked" |
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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 | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1416 | | 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 | 25 | | wordCount | 1300 | | uniqueNames | 18 | | maxNameDensity | 0.38 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 5 | | Shaftesbury | 1 | | Avenue | 1 | | Brewer | 1 | | Street | 1 | | Met | 1 | | Thames | 1 | | Whitechapel | 1 | | Soho | 1 | | London | 1 | | Tube | 2 | | Portobello | 1 | | Road | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Tomás | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Tomás" |
| | places | | 0 | "Shaftesbury" | | 1 | "Avenue" | | 2 | "Brewer" | | 3 | "Street" | | 4 | "Thames" | | 5 | "Whitechapel" | | 6 | "Soho" | | 7 | "London" | | 8 | "Portobello" | | 9 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1416 | | matches | (empty) | |
| 73.21% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 107 | | matches | | 0 | "knew that tile" | | 1 | "knew that tile" | | 2 | "looked that night" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 50.57 | | std | 31.4 | | cv | 0.621 | | sampleLengths | | 0 | 4 | | 1 | 107 | | 2 | 82 | | 3 | 122 | | 4 | 68 | | 5 | 72 | | 6 | 70 | | 7 | 19 | | 8 | 69 | | 9 | 12 | | 10 | 75 | | 11 | 86 | | 12 | 15 | | 13 | 34 | | 14 | 70 | | 15 | 7 | | 16 | 56 | | 17 | 41 | | 18 | 28 | | 19 | 5 | | 20 | 53 | | 21 | 51 | | 22 | 65 | | 23 | 66 | | 24 | 16 | | 25 | 15 | | 26 | 75 | | 27 | 33 |
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| 95.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 103 | | matches | | 0 | "was connected" | | 1 | "been torn" | | 2 | "been floored" | | 3 | "was meant" |
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| 69.28% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 204 | | matches | | 0 | "was begging" | | 1 | "was watching" | | 2 | "were planning" | | 3 | "was getting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 107 | | ratio | 0.056 | | matches | | 0 | "She'd lost the tail twice already tonight — once on Shaftesbury Avenue when the bastard had ducked into a crowd spilling out of a theatre, and again on Brewer Street when he'd climbed a fire escape like he had somewhere urgent to be." | | 1 | "She could hear his footsteps ahead — quicker than hers now, lighter, the footfall of someone younger or more afraid or both." | | 2 | "She could hear something underneath the rain — voices, low and many, the murmur of a crowd in a confined space." | | 3 | "At the bottom, the door had a small, barred window, and through it she could see the corner of a tiled wall — old, the kind you didn't see anymore above ground." | | 4 | "Each step groaned under her weight, the sound swallowed almost immediately by the deeper sound below — that hum of voices, the scratch of something that might have been music, very far away and very low." | | 5 | "The market noise washed over her — the low murmur, the soft clink of transactions she didn't want to understand." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1300 | | adjectiveStacks | 2 | | stackExamples | | 0 | "half-open, ancient wood" | | 1 | "Short curly dark hair," |
| | adverbCount | 43 | | adverbRatio | 0.03307692307692308 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005384615384615384 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 13.23 | | std | 9.85 | | cv | 0.744 | | sampleLengths | | 0 | 4 | | 1 | 37 | | 2 | 43 | | 3 | 22 | | 4 | 5 | | 5 | 12 | | 6 | 22 | | 7 | 20 | | 8 | 28 | | 9 | 4 | | 10 | 17 | | 11 | 16 | | 12 | 6 | | 13 | 22 | | 14 | 5 | | 15 | 38 | | 16 | 14 | | 17 | 15 | | 18 | 20 | | 19 | 20 | | 20 | 13 | | 21 | 8 | | 22 | 24 | | 23 | 9 | | 24 | 10 | | 25 | 21 | | 26 | 8 | | 27 | 18 | | 28 | 32 | | 29 | 3 | | 30 | 1 | | 31 | 8 | | 32 | 4 | | 33 | 6 | | 34 | 9 | | 35 | 2 | | 36 | 36 | | 37 | 8 | | 38 | 1 | | 39 | 1 | | 40 | 21 | | 41 | 7 | | 42 | 5 | | 43 | 12 | | 44 | 20 | | 45 | 28 | | 46 | 15 | | 47 | 9 | | 48 | 9 | | 49 | 13 |
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| 55.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.411214953271028 | | totalSentences | 107 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 90 | | matches | | 0 | "Somewhere beyond it, the figure" | | 1 | "Almost gently, the way a" | | 2 | "Then she stepped forward, past" |
| | ratio | 0.033 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 90 | | matches | | 0 | "She'd lost the tail twice" | | 1 | "She was forty-one years old" | | 2 | "She ignored them the way" | | 3 | "Her leather watch slapped against" | | 4 | "She followed, ducking under a" | | 5 | "Her breath came in ragged" | | 6 | "She could hear his footsteps" | | 7 | "She didn't know his name." | | 8 | "She knew his face from" | | 9 | "She knew he'd led her" | | 10 | "They burst out the other" | | 11 | "She stopped at the top" | | 12 | "She could hear something underneath" | | 13 | "She knew that tile." | | 14 | "She stepped through and stopped." | | 15 | "She caught a flash of" | | 16 | "She scanned the crowd and" | | 17 | "She started after him, then" | | 18 | "He was watching her." | | 19 | "She felt her grip tighten" |
| | ratio | 0.4 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 90 | | matches | | 0 | "The rain came sideways." | | 1 | "Detective Harlow Quinn hit the" | | 2 | "She'd lost the tail twice" | | 3 | "The one that unraveled everything." | | 4 | "She was forty-one years old" | | 5 | "She ignored them the way" | | 6 | "Her leather watch slapped against" | | 7 | "The figure darted left." | | 8 | "She followed, ducking under a" | | 9 | "The alley stank of rotting" | | 10 | "Her breath came in ragged" | | 11 | "She could hear his footsteps" | | 12 | "She didn't know his name." | | 13 | "She knew his face from" | | 14 | "She knew he'd led her" | | 15 | "They burst out the other" | | 16 | "The figure skidded, changed direction," | | 17 | "Quinn heard the clang of" | | 18 | "She stopped at the top" | | 19 | "A door hung half-open, ancient" |
| | ratio | 0.8 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 48.52% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 7 | | matches | | 0 | "She followed, ducking under a striped awning that flapped uselessly in the wind, its metal arms groaning." | | 1 | "She knew his face from three weeks of surveillance photos, knew he was connected to the same clique that had left two bodies in the Thames and a third in a skip…" | | 2 | "Each step groaned under her weight, the sound swallowed almost immediately by the deeper sound below — that hum of voices, the scratch of something that might h…" | | 3 | "Stallholders had set up along both walls under canvas awnings and strings of bare bulbs that gave the place the look of a fever dream of Portobello Road." | | 4 | "A stall to her left displayed jars of something that moved, something viscous and faintly luminous." | | 5 | "The air tasted of dust and something older, something that had no business being underground." | | 6 | "Beyond it, somewhere in the labyrinth of stalls and corridors that the old Tube station had become, her suspect was moving, and with him, maybe, the answer to a…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |