| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1464 | | 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) | |
| 89.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1464 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "lilt" | | 1 | "glinting" | | 2 | "glint" |
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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 | 76 | | matches | (empty) | |
| 86.47% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1464 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1391 | | uniqueNames | 26 | | maxNameDensity | 0.5 | | worstName | "Harlow" | | maxWindowNameDensity | 1 | | worstWindowName | "Raven" | | discoveredNames | | Soho | 1 | | Old | 1 | | Compton | 1 | | Street | 1 | | Raven | 2 | | Nest | 3 | | American | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Hackney | 1 | | Morris | 2 | | Camden | 2 | | Harlow | 7 | | Lock | 1 | | Veil | 2 | | Market | 2 | | Tube | 2 | | Met | 1 | | Seville | 1 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Tomás | 3 | | London | 1 | | Detective | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Morris" | | 3 | "Harlow" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Hackney" | | 8 | "Veil" | | 9 | "Met" | | 10 | "Seville" | | 11 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1464 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 97.6 | | std | 63.22 | | cv | 0.648 | | sampleLengths | | 0 | 226 | | 1 | 126 | | 2 | 114 | | 3 | 172 | | 4 | 111 | | 5 | 38 | | 6 | 27 | | 7 | 97 | | 8 | 30 | | 9 | 28 | | 10 | 100 | | 11 | 161 | | 12 | 33 | | 13 | 28 | | 14 | 173 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 76 | | matches | | 0 | "been spotted" | | 1 | "been welded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 206 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 76 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 304 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.009868421052631578 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 19.26 | | std | 13.83 | | cv | 0.718 | | sampleLengths | | 0 | 29 | | 1 | 27 | | 2 | 54 | | 3 | 25 | | 4 | 22 | | 5 | 25 | | 6 | 44 | | 7 | 19 | | 8 | 31 | | 9 | 13 | | 10 | 30 | | 11 | 30 | | 12 | 3 | | 13 | 26 | | 14 | 5 | | 15 | 4 | | 16 | 54 | | 17 | 25 | | 18 | 37 | | 19 | 26 | | 20 | 4 | | 21 | 46 | | 22 | 23 | | 23 | 36 | | 24 | 26 | | 25 | 33 | | 26 | 13 | | 27 | 15 | | 28 | 24 | | 29 | 20 | | 30 | 18 | | 31 | 6 | | 32 | 16 | | 33 | 5 | | 34 | 22 | | 35 | 26 | | 36 | 49 | | 37 | 4 | | 38 | 6 | | 39 | 11 | | 40 | 6 | | 41 | 3 | | 42 | 8 | | 43 | 5 | | 44 | 15 | | 45 | 25 | | 46 | 22 | | 47 | 36 | | 48 | 17 | | 49 | 4 |
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| 36.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3157894736842105 | | totalSentences | 76 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 57.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 74 | | matches | | 0 | "She’d staked out the Raven’s" | | 1 | "She’d watched every person who" | | 2 | "Her breath stays even, military-honed" | | 3 | "She doesn’t call into her" | | 4 | "Her superiors had labeled her" | | 5 | "She works alone." | | 6 | "He knows she’s there." | | 7 | "She’d spent three years chasing" | | 8 | "She’d laughed it off at" | | 9 | "He yanks a small, weathered" | | 10 | "He pulls a phone from" | | 11 | "She didn’t lose the trail." | | 12 | "She gets a flash of" | | 13 | "She can’t cross the threshold" | | 14 | "It eats them." | | 15 | "We can’t miss the meet," | | 16 | "She’d worn it on a" | | 17 | "She’d never known what it" | | 18 | "She could turn around." | | 19 | "She could call for backup," |
| | ratio | 0.405 | |
| 14.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 74 | | matches | | 0 | "Harlow’s boots hit the Soho" | | 1 | "The suspect, a lanky kid" | | 2 | "She’d staked out the Raven’s" | | 3 | "The bar’s green neon sign" | | 4 | "The Raven’s Nest’s walls, lined" | | 5 | "She’d watched every person who" | | 6 | "This kid was the first" | | 7 | "Her breath stays even, military-honed" | | 8 | "The worn leather watch on" | | 9 | "She doesn’t call into her" | | 10 | "Her superiors had labeled her" | | 11 | "She works alone." | | 12 | "The chase cuts north through" | | 13 | "The kid never glances back." | | 14 | "He knows she’s there." | | 15 | "Harlow gains 10 yards when" | | 16 | "She’d spent three years chasing" | | 17 | "The main stretch of Camden" | | 18 | "Tonight’s the full moon." | | 19 | "The snitch who’d first tipped" |
| | ratio | 0.892 | |
| 67.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 74 | | matches | | 0 | "Now it weighed heavy against" |
| | ratio | 0.014 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 10 | | matches | | 0 | "The suspect, a lanky kid in a faded black hoodie, hangs 20 yards ahead, weaving through clusters of drunk tourists that spill out of the area’s barfronts." | | 1 | "She’d staked out the Raven’s Nest for 21 straight nights, posed as a lost American backpacker for an hour inside, nursed a flat lager that warmed to room temper…" | | 2 | "She’d watched every person who walked in and out, cross-referenced their faces with the list of names she’d pulled from her dead partner’s case files." | | 3 | "The chase cuts north through Tottenham Court Road, past shuttered electronics stores and bus stops that glow orange under streetlights that bleed golden into ra…" | | 4 | "Harlow gains 10 yards when the kid pauses to dodge a black cab that slides through a red light, and she gets a clear look at his profile, the same sharp cheekbo…" | | 5 | "The snitch who’d first tipped her off to the Veil Market had told her the market moved locations every full moon, that it set up shop in abandoned Tube stations…" | | 6 | "The faint smell of ozone and burnt sugar drifts up, along with a hum of voices that doesn’t match any language she’s ever heard." | | 7 | "She gets a flash of him as he steps into the faint glow cast by the kid’s phone, his short curly dark hair sticking to his forehead, the Saint Christopher medal…" | | 8 | "She’d never known what it was for, had only held onto it because it was the only unexplained piece of evidence at the crime scene, the only thing that didn’t fi…" | | 9 | "The supernatural hints that had lingered at the edge of her case files, the vials of glowing liquid, the kid who could run a mile in five minutes, the bone toke…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |