| 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 | 960 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 960 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "maw" | | 2 | "churning" | | 3 | "footfall" | | 4 | "absolutely" | | 5 | "flicker" | | 6 | "gloom" | | 7 | "echo" | | 8 | "echoes" | | 9 | "stark" | | 10 | "oppressive" | | 11 | "chaotic" | | 12 | "pulsed" | | 13 | "vibrated" | | 14 | "cacophony" | | 15 | "echoed" | | 16 | "glint" | | 17 | "familiar" |
| |
| 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) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 960 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 960 | | uniqueNames | 11 | | maxNameDensity | 1.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Herrera | 6 | | Soho | 1 | | Morris | 3 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | | globalScore | 0.979 | | windowScore | 1 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like nothing she had ever seen, pa" | | 1 | "blades that seemed to drink the light, trinkets that pulsed with an inner fire" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 960 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 58.63% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 73.85 | | std | 26.23 | | cv | 0.355 | | sampleLengths | | 0 | 83 | | 1 | 93 | | 2 | 69 | | 3 | 70 | | 4 | 81 | | 5 | 84 | | 6 | 69 | | 7 | 40 | | 8 | 131 | | 9 | 66 | | 10 | 33 | | 11 | 38 | | 12 | 103 |
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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 | 148 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 968 | | adjectiveStacks | 1 | | stackExamples | | 0 | "strange, bone-gated entrance." |
| | adverbCount | 15 | | adverbRatio | 0.015495867768595042 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006198347107438017 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 12.31 | | std | 7.17 | | cv | 0.582 | | sampleLengths | | 0 | 9 | | 1 | 15 | | 2 | 17 | | 3 | 20 | | 4 | 2 | | 5 | 20 | | 6 | 9 | | 7 | 20 | | 8 | 29 | | 9 | 22 | | 10 | 13 | | 11 | 8 | | 12 | 18 | | 13 | 2 | | 14 | 6 | | 15 | 17 | | 16 | 18 | | 17 | 11 | | 18 | 2 | | 19 | 5 | | 20 | 5 | | 21 | 31 | | 22 | 10 | | 23 | 6 | | 24 | 8 | | 25 | 12 | | 26 | 13 | | 27 | 6 | | 28 | 5 | | 29 | 13 | | 30 | 10 | | 31 | 13 | | 32 | 1 | | 33 | 11 | | 34 | 9 | | 35 | 17 | | 36 | 20 | | 37 | 10 | | 38 | 5 | | 39 | 12 | | 40 | 13 | | 41 | 17 | | 42 | 5 | | 43 | 12 | | 44 | 14 | | 45 | 8 | | 46 | 17 | | 47 | 8 | | 48 | 15 | | 49 | 10 |
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| 63.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3974358974358974 | | totalSentences | 78 | | uniqueOpeners | 31 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Then, a faint glow appeared" |
| | ratio | 0.014 | |
| 90.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 74 | | matches | | 0 | "He moved with the desperate" | | 1 | "He glanced over his shoulder," | | 2 | "She picked up her pace," | | 3 | "She wouldn’t draw unless absolutely" | | 4 | "She burst onto the street," | | 5 | "He couldn’t have just evaporated." | | 6 | "Her eyes snagged on a" | | 7 | "She peered into the gloom." | | 8 | "She could hear the faint" | | 9 | "She pushed through the gate," | | 10 | "Her hand gripped her weapon" | | 11 | "She didn't like unknown territory." | | 12 | "Her partner, Morris, had walked" | | 13 | "She swept the light ahead." | | 14 | "They looked like nothing she" | | 15 | "They displayed goods Quinn couldn't" | | 16 | "He pressed it against a" | | 17 | "He moved past it, disappearing" | | 18 | "He wore the Saint Christopher" | | 19 | "She watched a tall, cloaked" |
| | ratio | 0.324 | |
| 20.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 74 | | matches | | 0 | "The streetlights bled sickly yellow" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "Water streamed from the brim" | | 3 | "He moved with the desperate" | | 4 | "Quinn’s lungs burned, a raw" | | 5 | "He glanced over his shoulder," | | 6 | "She picked up her pace," | | 7 | "A military precision guided her" | | 8 | "The alley reeked of stale" | | 9 | "Quinn pulled her hand from" | | 10 | "She wouldn’t draw unless absolutely" | | 11 | "The alley opened onto a" | | 12 | "Herrera was a blot against" | | 13 | "She burst onto the street," | | 14 | "Dread coiled in her gut." | | 15 | "He couldn’t have just evaporated." | | 16 | "Her eyes snagged on a" | | 17 | "An abandoned service entrance, perhaps," | | 18 | "Nothing registered on her police" | | 19 | "Quinn ran towards the archway," |
| | ratio | 0.878 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 74 | | matches | | 0 | "Before her stretched a scene" | | 1 | "To lose him now meant" |
| | ratio | 0.027 | |
| 51.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 6 | | matches | | 0 | "She picked up her pace, her legs churning, the worn leather watch on her left wrist catching glints from the wet street." | | 1 | "The rain seemed to intensify under its shelter, dripping from crumbling stone." | | 2 | "They looked like nothing she had ever seen, patterns that made her teeth ache." | | 3 | "They displayed goods Quinn couldn't name: jars of shimmering dust, coiled serpents in glass, blades that seemed to drink the light, trinkets that pulsed with an…" | | 4 | "Quinn remembered a briefing, years ago, on an unconfirmed tip about an underground market that moved locations, required a specific entry." | | 5 | "Quinn stood at the precipice of a world she knew nothing about, a world that held the answers to a case that chafed at her, tied to the unexplained circumstance…" |
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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 | |