| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 660 | | 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) | |
| 54.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 660 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulse" | | 2 | "glint" | | 3 | "rhythmic" | | 4 | "flicked" | | 5 | "glinting" |
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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 | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 646 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.02% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 634 | | uniqueNames | 15 | | maxNameDensity | 1.42 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 9 | | Raven | 2 | | Nest | 2 | | Herrera | 1 | | Morris | 2 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Serbian | 1 | | Tommy | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Camden" | | 9 | "Tommy" |
| | places | | | globalScore | 0.79 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 646 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 26.92 | | std | 17.9 | | cv | 0.665 | | sampleLengths | | 0 | 48 | | 1 | 28 | | 2 | 3 | | 3 | 47 | | 4 | 57 | | 5 | 4 | | 6 | 36 | | 7 | 3 | | 8 | 2 | | 9 | 41 | | 10 | 28 | | 11 | 41 | | 12 | 1 | | 13 | 15 | | 14 | 52 | | 15 | 50 | | 16 | 38 | | 17 | 4 | | 18 | 33 | | 19 | 23 | | 20 | 21 | | 21 | 19 | | 22 | 12 | | 23 | 40 |
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| 99.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 65 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 114 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 68 | | ratio | 0.147 | | matches | | 0 | "There—between the bouncing neon reflections—the suspect darted around the corner toward The Raven’s Nest." | | 1 | "The figure ahead—male, hooded, moving with unnatural speed—ignored her shout." | | 2 | "Something moved near the far end—too quick, too fluid." | | 3 | "Too many odd-case reports passed through her desk lately—missing persons with claw marks on their doors, CCTV footage with inexplicable gaps." | | 4 | "Quinn pushed it—and the entire panel swung inward on silent hinges." | | 5 | "The air smelled of wet stone and something sharper—ozone, maybe." | | 6 | "The sound of distant chatter drifted up—hundreds of voices layered over the rhythmic dripping of groundwater." | | 7 | "Blood dripped from a gash on his forearm—the old knife scar bisected by fresh injury." | | 8 | "His head snapped up—their gazes locked across the underground expanse." | | 9 | "Whatever waited in the shadows tonight, she’d drag it into the light—even if it dragged her down with it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 653 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.026033690658499236 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007656967840735069 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 9.5 | | std | 5.23 | | cv | 0.55 | | sampleLengths | | 0 | 14 | | 1 | 20 | | 2 | 14 | | 3 | 12 | | 4 | 9 | | 5 | 7 | | 6 | 3 | | 7 | 10 | | 8 | 17 | | 9 | 14 | | 10 | 6 | | 11 | 20 | | 12 | 16 | | 13 | 7 | | 14 | 14 | | 15 | 4 | | 16 | 12 | | 17 | 9 | | 18 | 9 | | 19 | 6 | | 20 | 3 | | 21 | 2 | | 22 | 5 | | 23 | 6 | | 24 | 21 | | 25 | 5 | | 26 | 4 | | 27 | 10 | | 28 | 7 | | 29 | 11 | | 30 | 11 | | 31 | 11 | | 32 | 8 | | 33 | 10 | | 34 | 1 | | 35 | 1 | | 36 | 6 | | 37 | 5 | | 38 | 4 | | 39 | 7 | | 40 | 9 | | 41 | 20 | | 42 | 16 | | 43 | 10 | | 44 | 9 | | 45 | 14 | | 46 | 14 | | 47 | 3 | | 48 | 6 | | 49 | 24 |
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| 94.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5735294117647058 | | totalSentences | 68 | | uniqueOpeners | 39 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 61 | | matches | | 0 | "Instead, he veered toward the" | | 1 | "Too many odd-case reports passed" | | 2 | "Only the dim glow of" | | 3 | "Then she spotted him." | | 4 | "Then Tommy bolted deeper into" |
| | ratio | 0.082 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 61 | | matches | | 0 | "She pushed off the brick" | | 1 | "She vaulted over a stack" | | 2 | "Her pulse hammered against her" | | 3 | "She edged forward, weapon steady." | | 4 | "Her radio crackled at her" | | 5 | "She emerged onto an iron" | | 6 | "She took the first step" | | 7 | "His head snapped up—their gazes" | | 8 | "She was done letting mysteries" |
| | ratio | 0.148 | |
| 99.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 61 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn wiped water" | | 2 | "There—between the bouncing neon reflections—the" | | 3 | "She pushed off the brick" | | 4 | "The whiskey she’d nursed earlier" | | 5 | "The figure ahead—male, hooded, moving" | | 6 | "Tommy Herrera had patched up" | | 7 | "She vaulted over a stack" | | 8 | "The green neon sign above" | | 9 | "The suspect didn’t head for" | | 10 | "Quinn drew her sidearm." | | 11 | "Her pulse hammered against her" | | 12 | "Dumpsters lined the left wall," | | 13 | "Something moved near the far" | | 14 | "She edged forward, weapon steady." | | 15 | "The back of her neck" | | 16 | "Morris would’ve teased her paranoia." | | 17 | "A glint of metal caught" | | 18 | "Quinn crouched, keeping her gun" | | 19 | "A Saint Christopher medallion lay" |
| | ratio | 0.721 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 61 | | matches | | 0 | "Whether this was a supernatural" | | 1 | "If she revealed herself here," |
| | ratio | 0.033 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |