| 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 | 1013 | | 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) | |
| 80.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1013 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "crystal" | | 2 | "pulse" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1003 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 891 | | uniqueNames | 14 | | maxNameDensity | 1.35 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 12 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Morris | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Tube" | | 7 | "Police" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.827 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1003 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 38.58 | | std | 31.36 | | cv | 0.813 | | sampleLengths | | 0 | 95 | | 1 | 20 | | 2 | 3 | | 3 | 78 | | 4 | 95 | | 5 | 74 | | 6 | 46 | | 7 | 25 | | 8 | 63 | | 9 | 54 | | 10 | 109 | | 11 | 32 | | 12 | 46 | | 13 | 1 | | 14 | 19 | | 15 | 10 | | 16 | 10 | | 17 | 16 | | 18 | 36 | | 19 | 43 | | 20 | 6 | | 21 | 12 | | 22 | 70 | | 23 | 10 | | 24 | 15 | | 25 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 86 | | ratio | 0.07 | | matches | | 0 | "The man—olive skin catching a streetlamp, short curly dark hair, Saint Christopher medallion bouncing at his throat—did not look back." | | 1 | "Herrera glanced back once—warm brown eyes wide, medallion swinging—and cut sharp into a narrow archway that stank of damp concrete and stagnant water." | | 2 | "Quinn had heard the whispers for years—moves every full moon, bone token only—but never stood inside its perimeter." | | 3 | "Herrera weaved between two stalls, flashed something small and pale—a token carved from bone—to a large man by a hanging curtain of black cloth." | | 4 | "The radio on her belt crackled again—units checking in, still above ground and losing time." | | 5 | "Ahead lay the market’s throat—unfamiliar, packed with faces that watched every twitch of her hand." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 909 | | adjectiveStacks | 2 | | stackExamples | | 0 | "short curly dark hair," | | 1 | "under battery-powered lanterns." |
| | adverbCount | 22 | | adverbRatio | 0.0242024202420242 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005500550055005501 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 11.66 | | std | 6.82 | | cv | 0.585 | | sampleLengths | | 0 | 32 | | 1 | 16 | | 2 | 9 | | 3 | 17 | | 4 | 21 | | 5 | 9 | | 6 | 11 | | 7 | 3 | | 8 | 17 | | 9 | 20 | | 10 | 2 | | 11 | 22 | | 12 | 8 | | 13 | 9 | | 14 | 8 | | 15 | 16 | | 16 | 22 | | 17 | 16 | | 18 | 12 | | 19 | 5 | | 20 | 16 | | 21 | 11 | | 22 | 10 | | 23 | 6 | | 24 | 20 | | 25 | 11 | | 26 | 2 | | 27 | 14 | | 28 | 18 | | 29 | 3 | | 30 | 19 | | 31 | 6 | | 32 | 25 | | 33 | 6 | | 34 | 7 | | 35 | 5 | | 36 | 23 | | 37 | 11 | | 38 | 11 | | 39 | 9 | | 40 | 6 | | 41 | 8 | | 42 | 7 | | 43 | 8 | | 44 | 8 | | 45 | 8 | | 46 | 14 | | 47 | 9 | | 48 | 25 | | 49 | 10 |
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| 77.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.46511627906976744 | | totalSentences | 86 | | uniqueOpeners | 40 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Somewhere below metal scraped, a" | | 1 | "Somewhere deeper past the cloth" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 75 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She gained two strides on" | | 2 | "Her voice bounced off wet" | | 3 | "He ducked left past a" | | 4 | "She planted a boot on" | | 5 | "He kicked free and the" | | 6 | "She closed again, lunged, fingers" | | 7 | "He tore free and tumbled" | | 8 | "She tore her radio free" | | 9 | "She clipped the radio and" | | 10 | "He ripped the bottom board" | | 11 | "She took it, gun still" | | 12 | "She kept the torch low," | | 13 | "She tasted copper where the" | | 14 | "She could radio the teams," |
| | ratio | 0.2 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 75 | | matches | | 0 | "The rain hammered the pavements" | | 1 | "Detective Harlow Quinn cut through" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "The worn leather watch on" | | 4 | "She gained two strides on" | | 5 | "The sharp line of her" | | 6 | "Her voice bounced off wet" | | 7 | "The man—olive skin catching a" | | 8 | "Quinn recognised the medallion from" | | 9 | "He ducked left past a" | | 10 | "Quinn took the corner wide" | | 11 | "Tyres hissed on the main" | | 12 | "A black cab swerved, horn" | | 13 | "She planted a boot on" | | 14 | "Herrera scrambled up a fire" | | 15 | "Quinn climbed after him, metal" | | 16 | "Midway she caught his ankle." | | 17 | "He kicked free and the" | | 18 | "Quinn followed without pause, landing" | | 19 | "Rain sheeted off the tar" |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn cut through the downpour, boots slamming water into arcs that slapped her trousers." | | 1 | "Herrera scrambled up a fire escape three floors of rusty iron that groaned under his weight." |
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| 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 | |