| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.458 | | leniency | 0.917 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1455 | | 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) | |
| 82.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1455 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "etched" | | 2 | "trembled" | | 3 | "navigating" | | 4 | "wavered" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1466 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 895 | | uniqueNames | 8 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Quinn | 12 | | July | 1 | | Ferreira | 9 | | Morris | 3 | | Good | 1 | | Eva | 2 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "July" | | 2 | "Ferreira" | | 3 | "Morris" | | 4 | "Eva" |
| | places | | | globalScore | 0.83 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1466 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 111 | | matches | | 0 | "had that look" | | 1 | "worn that face" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 31.19 | | std | 22.01 | | cv | 0.706 | | sampleLengths | | 0 | 70 | | 1 | 9 | | 2 | 53 | | 3 | 27 | | 4 | 55 | | 5 | 4 | | 6 | 62 | | 7 | 8 | | 8 | 7 | | 9 | 21 | | 10 | 43 | | 11 | 32 | | 12 | 64 | | 13 | 11 | | 14 | 7 | | 15 | 8 | | 16 | 32 | | 17 | 21 | | 18 | 53 | | 19 | 5 | | 20 | 28 | | 21 | 50 | | 22 | 44 | | 23 | 11 | | 24 | 75 | | 25 | 7 | | 26 | 23 | | 27 | 53 | | 28 | 18 | | 29 | 25 | | 30 | 29 | | 31 | 27 | | 32 | 10 | | 33 | 2 | | 34 | 57 | | 35 | 10 | | 36 | 11 | | 37 | 76 | | 38 | 53 | | 39 | 30 | | 40 | 11 | | 41 | 73 | | 42 | 32 | | 43 | 42 | | 44 | 25 | | 45 | 5 | | 46 | 47 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 111 | | ratio | 0.054 | | matches | | 0 | "His face held an expression she'd seen once before and had spent three years trying to forget — eyes wide, mouth open, the whole architecture of terror frozen mid-scream." | | 1 | "Fingernails clean — no, not clean." | | 2 | "Quinn stood and walked the scene in a slow circle, the way Morris had taught her — body first, then the space around it, then the space around that." | | 3 | "Wallet gone — Ferreira raised an eyebrow at that, mugging written all over his face — but the left breast pocket held something wrapped in a square of black cloth." | | 4 | "Then the beam caught something — a seam where no seam should be, a hairline of darker dark running in an arch from floor to apex, eight feet tall." | | 5 | "Bones — finger bones, bird bones, she couldn't tell — each one drilled through and strung on a thread of red that had rotted or been cut." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 892 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.01905829596412556 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002242152466367713 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 13.21 | | std | 10.2 | | cv | 0.772 | | sampleLengths | | 0 | 29 | | 1 | 19 | | 2 | 22 | | 3 | 9 | | 4 | 9 | | 5 | 25 | | 6 | 19 | | 7 | 10 | | 8 | 17 | | 9 | 55 | | 10 | 4 | | 11 | 5 | | 12 | 21 | | 13 | 2 | | 14 | 29 | | 15 | 5 | | 16 | 8 | | 17 | 6 | | 18 | 1 | | 19 | 3 | | 20 | 18 | | 21 | 25 | | 22 | 3 | | 23 | 6 | | 24 | 6 | | 25 | 3 | | 26 | 32 | | 27 | 29 | | 28 | 7 | | 29 | 28 | | 30 | 3 | | 31 | 2 | | 32 | 6 | | 33 | 7 | | 34 | 8 | | 35 | 32 | | 36 | 11 | | 37 | 10 | | 38 | 13 | | 39 | 9 | | 40 | 17 | | 41 | 14 | | 42 | 5 | | 43 | 9 | | 44 | 19 | | 45 | 14 | | 46 | 30 | | 47 | 6 | | 48 | 2 | | 49 | 23 |
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| 95.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.6216216216216216 | | totalSentences | 111 | | uniqueOpeners | 69 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Then the beam caught something" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 67 | | matches | | 0 | "He had that look he" | | 1 | "She crouched beside the body." | | 2 | "His face held an expression" | | 3 | "She pressed the thought down" | | 4 | "She moved her torch over" | | 5 | "Her leather watch ticked against" | | 6 | "She turned the torch on" | | 7 | "She swept the beam across" | | 8 | "She went back to the" | | 9 | "It trembled, then swung, and" | | 10 | "She tucked a curl behind" | | 11 | "She nodded at the corpse" | | 12 | "She crossed to the tiles" | | 13 | "She put her palm flat" | | 14 | "He said the word four" | | 15 | "She held his gaze until" |
| | ratio | 0.239 | |
| 42.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 67 | | matches | | 0 | "The stairs down to the" | | 1 | "Somebody had cut the chain" | | 2 | "Quinn ducked under the blue-and-white" | | 3 | "The constable at the bottom" | | 4 | "A knot of scene-of-crime officers" | | 5 | "DS Ferreira met her halfway," | | 6 | "He had that look he" | | 7 | "She crouched beside the body." | | 8 | "The man lay on his" | | 9 | "His face held an expression" | | 10 | "Morris had worn that face." | | 11 | "She pressed the thought down" | | 12 | "She moved her torch over" | | 13 | "Fingernails clean — no, not" | | 14 | "Something grey under them, like" | | 15 | "Quinn stood and walked the" | | 16 | "Her leather watch ticked against" | | 17 | "The platform dust lay thick" | | 18 | "Ferreira frowned into his notebook" | | 19 | "She turned the torch on" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 3 | | matches | | 0 | "Ferreira frowned into his notebook as if it had betrayed him." | | 1 | "Small, brass, green with verdigris, its face etched with marks that made her eyes slide sideways if she looked at them too long." | | 2 | "Massive cardiac event, no underlying disease, adrenaline off the scale, core temperature that made no sense at all." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "The voice came, and every head turned" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.271 | | leniency | 0.542 | | rawRatio | 0 | | effectiveRatio | 0 | |