| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.323 | | leniency | 0.645 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1053 | | 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) | |
| 90.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1053 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 47 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 47 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1063 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 585 | | uniqueNames | 9 | | maxNameDensity | 1.2 | | worstName | "Okafor" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Okafor" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 6 | | Thames | 1 | | Tube | 1 | | Camden | 1 | | Idris | 1 | | Okafor | 7 | | Stamped | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Okafor" |
| | places | | | globalScore | 0.902 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 1063 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 30.37 | | std | 22.49 | | cv | 0.74 | | sampleLengths | | 0 | 50 | | 1 | 43 | | 2 | 37 | | 3 | 7 | | 4 | 66 | | 5 | 3 | | 6 | 17 | | 7 | 11 | | 8 | 80 | | 9 | 4 | | 10 | 17 | | 11 | 42 | | 12 | 5 | | 13 | 64 | | 14 | 30 | | 15 | 67 | | 16 | 18 | | 17 | 24 | | 18 | 3 | | 19 | 42 | | 20 | 31 | | 21 | 6 | | 22 | 58 | | 23 | 6 | | 24 | 58 | | 25 | 11 | | 26 | 66 | | 27 | 36 | | 28 | 34 | | 29 | 4 | | 30 | 47 | | 31 | 16 | | 32 | 32 | | 33 | 24 | | 34 | 4 |
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| 90.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 47 | | matches | | 0 | "been swept" | | 1 | "was etched" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 89 | | matches | | 0 | "was spinning" | | 1 | "was already dialling" | | 2 | "was looking" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 68 | | ratio | 0.088 | | matches | | 0 | "The smell hit Detective Harlow Quinn before the torchlight did — copper and river silt, the stink of a place the Thames had forgotten." | | 1 | "Someone had hung lanterns — actual oil lanterns — from hooks driven into the tile at regular intervals." | | 2 | "The face was etched with fine geometric sigils, and the needle — Quinn leaned in — the needle was spinning." | | 3 | "The needle completed another revolution and jerked, shuddered, then locked — quivering — toward the far end of the platform, toward a bricked-up archway that had once led to a tunnel." | | 4 | "Stamped into the case back was a maker's mark she didn't recognise — a crescent pierced by a vertical line." | | 5 | "In the dust between two tiles, half-erased by a shoe print, was a smudge of the same wiped blood — and pressed into it, a partial print." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 579 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02072538860103627 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0017271157167530224 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 15.63 | | std | 11.35 | | cv | 0.726 | | sampleLengths | | 0 | 24 | | 1 | 26 | | 2 | 6 | | 3 | 14 | | 4 | 23 | | 5 | 18 | | 6 | 19 | | 7 | 7 | | 8 | 16 | | 9 | 50 | | 10 | 3 | | 11 | 17 | | 12 | 6 | | 13 | 5 | | 14 | 13 | | 15 | 18 | | 16 | 14 | | 17 | 35 | | 18 | 4 | | 19 | 17 | | 20 | 18 | | 21 | 24 | | 22 | 5 | | 23 | 14 | | 24 | 50 | | 25 | 5 | | 26 | 25 | | 27 | 15 | | 28 | 52 | | 29 | 18 | | 30 | 24 | | 31 | 3 | | 32 | 31 | | 33 | 11 | | 34 | 14 | | 35 | 9 | | 36 | 8 | | 37 | 6 | | 38 | 2 | | 39 | 15 | | 40 | 20 | | 41 | 21 | | 42 | 6 | | 43 | 22 | | 44 | 31 | | 45 | 5 | | 46 | 11 | | 47 | 21 | | 48 | 20 | | 49 | 25 |
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| 84.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5441176470588235 | | totalSentences | 68 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 43 | | matches | | 0 | "Directly in front of the" | | 1 | "Too narrow for a thumb." | | 2 | "Too long for a finger." |
| | ratio | 0.07 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 43 | | matches | | 0 | "She ducked under the police" | | 1 | "She went to the walls." | | 2 | "She crouched at the edge" | | 3 | "She pointed her own torch" | | 4 | "She moved along the platform," | | 5 | "She stopped at a dark" | | 6 | "He held it up between" | | 7 | "She held out her hand" | | 8 | "She walked toward the bricked" | | 9 | "She'd never told anyone she" | | 10 | "She pocketed the compass." |
| | ratio | 0.256 | |
| 76.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 43 | | matches | | 0 | "The smell hit Detective Harlow" | | 1 | "She ducked under the police" | | 2 | "The other was younger, twenties," | | 3 | "DS Idris Okafor crouched over" | | 4 | "Okafor straightened, knees cracking" | | 5 | "Okafor pointed his torch at" | | 6 | "Quinn didn't go to the" | | 7 | "She went to the walls." | | 8 | "The station was a cavern" | | 9 | "Someone had hung lanterns —" | | 10 | "Strings of small cloth banners" | | 11 | "The floor of the platform" | | 12 | "She crouched at the edge" | | 13 | "She pointed her own torch" | | 14 | "Okafor exhaled through his nose." | | 15 | "She moved along the platform," | | 16 | "She stopped at a dark" | | 17 | "Okafor crouched over the younger" | | 18 | "Something small and metallic slid" | | 19 | "He held it up between" |
| | ratio | 0.767 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "The needle completed another revolution and jerked, shuddered, then locked — quivering — toward the far end of the platform, toward a bricked-up archway that ha…" | | 1 | "Quinn stayed at the archway, compass open in her hand, watching the needle turn slow circles against the brick like something on the other side was looking back…" |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Okafor straightened, knees cracking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.065 | | leniency | 0.129 | | rawRatio | 0 | | effectiveRatio | 0 | |