| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "But Harlow moved too [too]" |
| | dialogueSentences | 48 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.067 | | effectiveRatio | 0.042 | |
| 96.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1331 | | 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) | |
| 69.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1331 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "churned" | | 1 | "magnetic" | | 2 | "standard" | | 3 | "shimmered" | | 4 | "echoed" | | 5 | "scanned" | | 6 | "etched" |
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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 | 142 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 142 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 6 | | totalWords | 1320 | | ratio | 0.005 | | matches | | 0 | "how" | | 1 | "why here" | | 2 | "The Veil Market." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 1 | | matches | | 0 | "Harlow dove aside, Reeves shouted, and the corpse hit the pedestrian path with a wet crack that echoed across the Thames…" |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1057 | | uniqueNames | 27 | | maxNameDensity | 1.32 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Millennium | 2 | | Bridge | 3 | | Reeves | 9 | | Harlow | 14 | | Thames | 3 | | Central | 1 | | London | 2 | | Oxfords | 1 | | South | 2 | | Bank | 2 | | City | 2 | | Southwark | 2 | | Corporation | 1 | | St | 3 | | Paul | 3 | | Row | 1 | | Victorian | 2 | | Camden | 3 | | December | 1 | | Highgate | 1 | | Cemetery | 1 | | Morris | 3 | | Met | 1 | | Quinn | 1 | | Clerkenwell | 1 | | Tube | 1 | | Veil | 1 |
| | persons | | 0 | "Reeves" | | 1 | "Harlow" | | 2 | "Oxfords" | | 3 | "Bank" | | 4 | "Paul" | | 5 | "Morris" | | 6 | "Quinn" |
| | places | | 0 | "Millennium" | | 1 | "Bridge" | | 2 | "Thames" | | 3 | "Central" | | 4 | "London" | | 5 | "South" | | 6 | "City" | | 7 | "Southwark" | | 8 | "Corporation" | | 9 | "St" | | 10 | "Camden" | | 11 | "Highgate" | | 12 | "Clerkenwell" |
| | globalScore | 0.838 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like charcoal but moved against th" |
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| 48.48% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.515 | | wordCount | 1320 | | matches | | 0 | "not *how* he hung there, but *why here*" | | 1 | "Not a brand she recognized, but money screamed from every detail" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 175 | | matches | | 0 | "accepting that Morris's" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 19.7 | | std | 19.22 | | cv | 0.975 | | sampleLengths | | 0 | 9 | | 1 | 48 | | 2 | 22 | | 3 | 55 | | 4 | 15 | | 5 | 70 | | 6 | 8 | | 7 | 27 | | 8 | 2 | | 9 | 55 | | 10 | 5 | | 11 | 5 | | 12 | 3 | | 13 | 5 | | 14 | 7 | | 15 | 58 | | 16 | 2 | | 17 | 29 | | 18 | 9 | | 19 | 2 | | 20 | 7 | | 21 | 2 | | 22 | 4 | | 23 | 50 | | 24 | 18 | | 25 | 3 | | 26 | 8 | | 27 | 9 | | 28 | 44 | | 29 | 34 | | 30 | 1 | | 31 | 7 | | 32 | 2 | | 33 | 3 | | 34 | 6 | | 35 | 43 | | 36 | 10 | | 37 | 33 | | 38 | 3 | | 39 | 29 | | 40 | 11 | | 41 | 47 | | 42 | 1 | | 43 | 3 | | 44 | 39 | | 45 | 3 | | 46 | 34 | | 47 | 18 | | 48 | 58 | | 49 | 29 |
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| 97.85% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 142 | | matches | | 0 | "been placed" | | 1 | "was, demanded" | | 2 | "been ordered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 183 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 175 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1066 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.027204502814258912 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008442776735459663 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 7.54 | | std | 5.67 | | cv | 0.751 | | sampleLengths | | 0 | 9 | | 1 | 6 | | 2 | 15 | | 3 | 12 | | 4 | 2 | | 5 | 2 | | 6 | 11 | | 7 | 7 | | 8 | 15 | | 9 | 11 | | 10 | 10 | | 11 | 3 | | 12 | 24 | | 13 | 7 | | 14 | 15 | | 15 | 5 | | 16 | 2 | | 17 | 2 | | 18 | 12 | | 19 | 20 | | 20 | 7 | | 21 | 22 | | 22 | 4 | | 23 | 4 | | 24 | 15 | | 25 | 12 | | 26 | 2 | | 27 | 3 | | 28 | 2 | | 29 | 19 | | 30 | 12 | | 31 | 4 | | 32 | 1 | | 33 | 1 | | 34 | 11 | | 35 | 1 | | 36 | 1 | | 37 | 5 | | 38 | 5 | | 39 | 3 | | 40 | 5 | | 41 | 7 | | 42 | 4 | | 43 | 12 | | 44 | 11 | | 45 | 14 | | 46 | 1 | | 47 | 1 | | 48 | 6 | | 49 | 9 |
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| 74.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.5142857142857142 | | totalSentences | 175 | | uniqueOpeners | 90 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 104 | | matches | | 0 | "Just empty air between his" | | 1 | "Just Victorian engineering and Thames" | | 2 | "Just light, bright enough to" | | 3 | "Just a man who'd fallen" | | 4 | "Just death now." | | 5 | "Just questions that led to" |
| | ratio | 0.058 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 104 | | matches | | 0 | "She stopped beneath the body." | | 1 | "His shoes, polished Oxfords, pointed" | | 2 | "She didn't answer." | | 3 | "She pulled out her phone" | | 4 | "She followed his gaze." | | 5 | "She moved to the bridge" | | 6 | "Her phone buzzed." | | 7 | "She ignored it." | | 8 | "She'd seen work like that" | | 9 | "He'd heard the stories." | | 10 | "It pointed north." |
| | ratio | 0.106 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 104 | | matches | | 0 | "The body dangled from a" | | 1 | "Harlow studied the scene from" | | 2 | "The victim, male, mid-thirties, hung" | | 3 | "DS Reeves gestured with his" | | 4 | "Harlow walked closer, her worn" | | 5 | "The Thames churned below, morning" | | 6 | "She stopped beneath the body." | | 7 | "The corpse hung perfectly still" | | 8 | "A red-haired man in an" | | 9 | "His shoes, polished Oxfords, pointed" | | 10 | "Harlow pointed to the flags" | | 11 | "She didn't answer." | | 12 | "The question that mattered sat" | | 13 | "Millennium Bridge connected the City" | | 14 | "Tourists crossed it daily." | | 15 | "The bridge had been in" | | 16 | "Someone wanted this man seen." | | 17 | "Harlow circled the lamppost." | | 18 | "The base showed no marks," | | 19 | "She pulled out her phone" |
| | ratio | 0.663 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 104 | | matches | | 0 | "Whether admitting it meant accepting" | | 1 | "Whether the evidence she'd buried," |
| | ratio | 0.019 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "Twenty feet below, the bridge surface showed no ladder marks, no footprints besides their own and the maintenance crew who'd found him." | | 1 | "As she watched, it spread up his wrists, into his sleeves, a cold light that had nothing to do with sunrise." | | 2 | "Just a man who'd fallen from significant height onto Victorian engineering." | | 3 | "The partner who'd died in an abandoned church in Clerkenwell, no signs of violence, no explanation." | | 4 | "The investigation that ended with Harlow taking six months leave and coming back with salt-and-pepper hair despite being forty-one." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "Reeves's voice pitched, youth showing through professional training" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.104 | | leniency | 0.208 | | rawRatio | 0 | | effectiveRatio | 0 | |