| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Reyes said slowly [slowly]" |
| | dialogueSentences | 43 | | tagDensity | 0.256 | | leniency | 0.512 | | rawRatio | 0.091 | | effectiveRatio | 0.047 | |
| 92.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1427 | | totalAiIsmAdverbs | 2 | | 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) | |
| 82.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1427 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flicked" | | 1 | "perfect" | | 2 | "traced" | | 3 | "pristine" | | 4 | "etched" |
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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 | 61 | | matches | (empty) | |
| 96.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1443 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 807 | | uniqueNames | 10 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 9 | | Tube | 2 | | Okafor | 2 | | Dr | 1 | | Reyes | 7 | | Small | 1 | | Eighteen | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Okafor" | | 3 | "Dr" | | 4 | "Reyes" |
| | places | (empty) | | globalScore | 0.942 | | windowScore | 1 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it, scratched into the tile" |
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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 | 1443 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 28.29 | | std | 22.39 | | cv | 0.791 | | sampleLengths | | 0 | 46 | | 1 | 12 | | 2 | 30 | | 3 | 10 | | 4 | 16 | | 5 | 62 | | 6 | 4 | | 7 | 24 | | 8 | 41 | | 9 | 5 | | 10 | 9 | | 11 | 23 | | 12 | 51 | | 13 | 4 | | 14 | 10 | | 15 | 44 | | 16 | 1 | | 17 | 39 | | 18 | 46 | | 19 | 13 | | 20 | 4 | | 21 | 23 | | 22 | 41 | | 23 | 35 | | 24 | 82 | | 25 | 12 | | 26 | 22 | | 27 | 11 | | 28 | 6 | | 29 | 70 | | 30 | 4 | | 31 | 57 | | 32 | 3 | | 33 | 4 | | 34 | 42 | | 35 | 3 | | 36 | 2 | | 37 | 65 | | 38 | 64 | | 39 | 10 | | 40 | 13 | | 41 | 15 | | 42 | 16 | | 43 | 67 | | 44 | 31 | | 45 | 9 | | 46 | 64 | | 47 | 42 | | 48 | 53 | | 49 | 38 |
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| 93.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 61 | | matches | | 0 | "been punched" | | 1 | "been taught" |
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| 87.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 119 | | matches | | 0 | "was spinning" | | 1 | "was tracking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 2 | | flaggedSentences | 11 | | totalSentences | 94 | | ratio | 0.117 | | matches | | 0 | "The hole was clean — edges dusted with fresh mortar dust, not scattered." | | 1 | "His clothes were expensive and ruined — a wool overcoat, a good shirt." | | 2 | "A figure in a suit picked its way around the platform's edge — DS Reyes, still fastening her hood, clipboard under her arm." | | 3 | "She'd been first on scene; Quinn could tell from the proprietary way she stood between the body and the rest of the platform." | | 4 | "The platform's surface was a map of dried fluid trails — engine oil, water stains, years of grime — all threaded through with the fresh hoof-print smears of forensics' overshoes." | | 5 | "A pale line ran along the far wall — a chalk mark, or something like it, scratched into the tile." | | 6 | "The symbols circled a larger mark, one she almost recognised — a spiral with a line through it." | | 7 | "Small, shallow impressions in the oily grime, leading from the tunnel mouth on the north side to the body and back again — and then nothing." | | 8 | "Fast, jittering, aimed nowhere — then it stopped dead and swung, pointing back down the tunnel the way they'd come, straight toward the platform and the body." | | 9 | "Eighteen years of instinct told her not to touch it with bare skin; she photographed it instead and called for an evidence bag, though part of her wondered whether evidence bags worked on whatever that thing was tracking." | | 10 | "She checked her watch — 4:12 in the afternoon, a full four hours after the call, and a thought landed cold in her chest: the moon would rise tonight, and something in her gut said this station wasn't going to be here when it did." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 659 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.028831562974203338 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.006069802731411229 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 15.35 | | std | 12.9 | | cv | 0.84 | | sampleLengths | | 0 | 36 | | 1 | 10 | | 2 | 12 | | 3 | 7 | | 4 | 23 | | 5 | 10 | | 6 | 16 | | 7 | 6 | | 8 | 24 | | 9 | 13 | | 10 | 19 | | 11 | 4 | | 12 | 24 | | 13 | 12 | | 14 | 17 | | 15 | 12 | | 16 | 5 | | 17 | 9 | | 18 | 23 | | 19 | 10 | | 20 | 14 | | 21 | 13 | | 22 | 14 | | 23 | 4 | | 24 | 3 | | 25 | 7 | | 26 | 44 | | 27 | 1 | | 28 | 39 | | 29 | 23 | | 30 | 23 | | 31 | 13 | | 32 | 4 | | 33 | 12 | | 34 | 11 | | 35 | 2 | | 36 | 30 | | 37 | 9 | | 38 | 4 | | 39 | 31 | | 40 | 13 | | 41 | 69 | | 42 | 12 | | 43 | 22 | | 44 | 7 | | 45 | 4 | | 46 | 6 | | 47 | 18 | | 48 | 20 | | 49 | 5 |
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| 94.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5851063829787234 | | totalSentences | 94 | | uniqueOpeners | 55 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 53 | | matches | | 0 | "Her boots found puddles that" | | 1 | "She recognised the voice before" | | 2 | "His clothes were expensive and" | | 3 | "His shoes were polished leather," | | 4 | "She'd been first on scene;" | | 5 | "She stepped to the platform's" | | 6 | "She swung her torch up." | | 7 | "She photographed them with her" | | 8 | "She filed it away and" | | 9 | "They simply stopped three metres" | | 10 | "Her jaw worked for a" | | 11 | "They followed the shallow impressions" | | 12 | "Its needle was spinning." | | 13 | "She checked her watch —" |
| | ratio | 0.264 | |
| 73.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 53 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Her boots found puddles that" | | 2 | "She recognised the voice before" | | 3 | "Constable Okafor, young enough to" | | 4 | "The staircase curled down into" | | 5 | "Quinn's torch swept across a" | | 6 | "The hole was clean —" | | 7 | "A forensics tent glowed like" | | 8 | "Achebe, hood pulled tight against" | | 9 | "Quinn crouched at the mouth" | | 10 | "The man lay on his" | | 11 | "His clothes were expensive and" | | 12 | "His shoes were polished leather," | | 13 | "Achebe's pen paused." | | 14 | "A figure in a suit" | | 15 | "She'd been first on scene;" | | 16 | "Reyes flicked her pen toward" | | 17 | "The platform's surface was a" | | 18 | "Quinn rose and circled wide," | | 19 | "Reyes chewed the inside of" |
| | ratio | 0.774 | |
| 94.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 53 | | matches | | 0 | "Whoever broke in had done" |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Her boots found puddles that tasted of rust and diesel." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.047 | | leniency | 0.093 | | rawRatio | 0 | | effectiveRatio | 0 | |