| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1942 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 87.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1942 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flicked" | | 1 | "pulse" | | 2 | "velvet" | | 3 | "could feel" | | 4 | "charm" |
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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 | 146 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 146 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 182 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1942 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1514 | | uniqueNames | 9 | | maxNameDensity | 1.65 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Sergeant | 1 | | Priya | 19 | | Sethi | 1 | | Quinn | 25 | | One | 4 |
| | persons | | 0 | "Camden" | | 1 | "Market" | | 2 | "Sergeant" | | 3 | "Priya" | | 4 | "Sethi" | | 5 | "Quinn" |
| | places | | | globalScore | 0.674 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | 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 | 1942 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 182 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 22.32 | | std | 22.57 | | cv | 1.011 | | sampleLengths | | 0 | 15 | | 1 | 6 | | 2 | 61 | | 3 | 79 | | 4 | 50 | | 5 | 7 | | 6 | 30 | | 7 | 7 | | 8 | 17 | | 9 | 36 | | 10 | 45 | | 11 | 2 | | 12 | 10 | | 13 | 15 | | 14 | 91 | | 15 | 7 | | 16 | 9 | | 17 | 7 | | 18 | 4 | | 19 | 36 | | 20 | 17 | | 21 | 47 | | 22 | 3 | | 23 | 3 | | 24 | 4 | | 25 | 67 | | 26 | 49 | | 27 | 4 | | 28 | 46 | | 29 | 2 | | 30 | 45 | | 31 | 64 | | 32 | 13 | | 33 | 37 | | 34 | 74 | | 35 | 12 | | 36 | 17 | | 37 | 46 | | 38 | 6 | | 39 | 1 | | 40 | 28 | | 41 | 12 | | 42 | 6 | | 43 | 5 | | 44 | 4 | | 45 | 23 | | 46 | 2 | | 47 | 21 | | 48 | 46 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 146 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 218 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 182 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1517 | | adjectiveStacks | 1 | | stackExamples | | 0 | "bent wrong beneath him" |
| | adverbCount | 37 | | adverbRatio | 0.024390243902439025 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.001977587343441002 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 182 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 182 | | mean | 10.67 | | std | 8.78 | | cv | 0.823 | | sampleLengths | | 0 | 15 | | 1 | 6 | | 2 | 15 | | 3 | 26 | | 4 | 20 | | 5 | 26 | | 6 | 5 | | 7 | 11 | | 8 | 37 | | 9 | 21 | | 10 | 19 | | 11 | 10 | | 12 | 7 | | 13 | 14 | | 14 | 16 | | 15 | 7 | | 16 | 17 | | 17 | 36 | | 18 | 18 | | 19 | 10 | | 20 | 17 | | 21 | 2 | | 22 | 10 | | 23 | 15 | | 24 | 3 | | 25 | 18 | | 26 | 13 | | 27 | 23 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 6 | | 32 | 14 | | 33 | 3 | | 34 | 5 | | 35 | 7 | | 36 | 9 | | 37 | 7 | | 38 | 4 | | 39 | 17 | | 40 | 19 | | 41 | 17 | | 42 | 7 | | 43 | 16 | | 44 | 5 | | 45 | 2 | | 46 | 2 | | 47 | 15 | | 48 | 3 | | 49 | 3 |
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| 46.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3076923076923077 | | totalSentences | 182 | | uniqueOpeners | 56 | |
| 25.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 129 | | matches | | 0 | "Then another crack split above" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 129 | | matches | | 0 | "She stopped with one boot" | | 1 | "She wore her hair in" | | 2 | "Her eyes flicked to Quinn," | | 3 | "Her left wrist turned, and" | | 4 | "His cuffs showed no soot" | | 5 | "Her gaze moved to his" | | 6 | "It had a crude knot" | | 7 | "She sniffed it once, quick." | | 8 | "She stood and paced the" | | 9 | "It always mattered." | | 10 | "Its face caught the station" | | 11 | "It pointed, hard and eager," | | 12 | "She reached for the poster" | | 13 | "She lifted the hem of" | | 14 | "His right hand had curled" | | 15 | "She eased it open with" | | 16 | "She placed it beside the" | | 17 | "His rings clinked against each" |
| | ratio | 0.14 | |
| 45.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 107 | | totalSentences | 129 | | matches | | 0 | "Quinn caught the forensic tent" | | 1 | "She stopped with one boot" | | 2 | "The dead man lay half" | | 3 | "A dark stain spread beneath" | | 4 | "The abandoned Tube station beneath" | | 5 | "Graffiti crawled over the tiles." | | 6 | "Detective Sergeant Priya Sethi stood" | | 7 | "She wore her hair in" | | 8 | "Her eyes flicked to Quinn," | | 9 | "Quinn crouched beside the body" | | 10 | "Her left wrist turned, and" | | 11 | "Priya snorted and crouched the" | | 12 | "Quinn’s gaze travelled from the" | | 13 | "The line looked bright against" | | 14 | "Someone had drawn it with" | | 15 | "Priya tilted her chin towards" | | 16 | "Quinn leaned in." | | 17 | "The dead man wore expensive" | | 18 | "His cuffs showed no soot" | | 19 | "Her gaze moved to his" |
| | ratio | 0.829 | |
| 38.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 129 | | matches | | 0 | "Whoever drew it had worked" |
| | ratio | 0.008 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 5 | | matches | | 0 | "The abandoned Tube station beneath Camden breathed old concrete, rust, and something sharper underneath, a bitter, metallic tang that sat on the back of the ton…" | | 1 | "Beyond the cordon, the Veil Market had gone half-struck, stalls shuttered with black tarps and charms hanging from the frames, little rows of bells, teeth, brig…" | | 2 | "She wore her hair in a tight knot and a dark raincoat that had seen too many cold rooms." | | 3 | "The wall behind the poster had begun to hum, a low vibration that travelled through the tiles and into her boots." | | 4 | "Something under the body hauled at the joint, slow and deliberate, as if a hand had found the dead man from the far side of the wall and taken hold." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |