| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1273 | | 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) | |
| 80.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1273 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "blown wide" | | 2 | "weight" | | 3 | "etched" | | 4 | "magnetic" |
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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 | 67 | | matches | (empty) | |
| 78.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1273 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.69% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 877 | | uniqueNames | 10 | | maxNameDensity | 1.03 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Okafor" | | discoveredNames | | Northern | 1 | | Harlow | 1 | | Quinn | 9 | | Kentish | 1 | | Road | 1 | | Constable | 1 | | Ray | 1 | | Okafor | 9 | | Saturday | 2 | | Like | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Ray" | | 3 | "Okafor" |
| | places | | | globalScore | 0.987 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1273 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 28.93 | | std | 25.17 | | cv | 0.87 | | sampleLengths | | 0 | 54 | | 1 | 9 | | 2 | 70 | | 3 | 64 | | 4 | 38 | | 5 | 4 | | 6 | 12 | | 7 | 61 | | 8 | 34 | | 9 | 38 | | 10 | 6 | | 11 | 4 | | 12 | 8 | | 13 | 59 | | 14 | 10 | | 15 | 4 | | 16 | 1 | | 17 | 74 | | 18 | 41 | | 19 | 7 | | 20 | 6 | | 21 | 22 | | 22 | 38 | | 23 | 73 | | 24 | 18 | | 25 | 27 | | 26 | 4 | | 27 | 2 | | 28 | 6 | | 29 | 20 | | 30 | 7 | | 31 | 64 | | 32 | 7 | | 33 | 44 | | 34 | 13 | | 35 | 38 | | 36 | 13 | | 37 | 103 | | 38 | 12 | | 39 | 56 | | 40 | 34 | | 41 | 19 | | 42 | 8 | | 43 | 41 |
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| 73.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 67 | | matches | | 0 | "been discontinued" | | 1 | "been sealed" | | 2 | "been buttoned" | | 3 | "been handled" | | 4 | "was etched" | | 5 | "been flung" |
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| 66.67% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 150 | | matches | | 0 | "was waiting" | | 1 | "was not resting" | | 2 | "were breathing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 91 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 878 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.030751708428246014 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00683371298405467 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 13.99 | | std | 10.37 | | cv | 0.741 | | sampleLengths | | 0 | 23 | | 1 | 31 | | 2 | 7 | | 3 | 2 | | 4 | 2 | | 5 | 22 | | 6 | 13 | | 7 | 12 | | 8 | 21 | | 9 | 21 | | 10 | 19 | | 11 | 24 | | 12 | 11 | | 13 | 27 | | 14 | 4 | | 15 | 12 | | 16 | 5 | | 17 | 26 | | 18 | 11 | | 19 | 19 | | 20 | 6 | | 21 | 28 | | 22 | 3 | | 23 | 12 | | 24 | 5 | | 25 | 18 | | 26 | 6 | | 27 | 2 | | 28 | 2 | | 29 | 8 | | 30 | 10 | | 31 | 33 | | 32 | 8 | | 33 | 8 | | 34 | 5 | | 35 | 5 | | 36 | 4 | | 37 | 1 | | 38 | 43 | | 39 | 31 | | 40 | 3 | | 41 | 23 | | 42 | 15 | | 43 | 7 | | 44 | 6 | | 45 | 22 | | 46 | 11 | | 47 | 19 | | 48 | 8 | | 49 | 10 |
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| 73.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4945054945054945 | | totalSentences | 91 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 33.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 58 | | matches | | 0 | "He was thirty-two, sharp in" | | 1 | "He had called her in" | | 2 | "he said, nodding at the" | | 3 | "His eyes were half open," | | 4 | "His jacket was good wool," | | 5 | "It was a plausible story." | | 6 | "It was also the story" | | 7 | "He lifted the torch and" | | 8 | "They ended a yard short" | | 9 | "She tapped the air above" | | 10 | "He looked at the dust" | | 11 | "She stood and pulled a" | | 12 | "It was small, no bigger" | | 13 | "Her left wrist itched under" | | 14 | "She did not look at" | | 15 | "It was not resting on" | | 16 | "It pointed past Okafor's shoulder," | | 17 | "She watched the needle quiver," | | 18 | "She rose, took the torch" | | 19 | "She ran her gloved fingers" |
| | ratio | 0.466 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 58 | | matches | | 0 | "The Northern line ran somewhere" | | 1 | "the constable said" | | 2 | "The platform had been sealed" | | 3 | "Quinn stopped at the foot" | | 4 | "Camden's old Kentish Road platform" | | 5 | "Someone had cut through a" | | 6 | "Detective Constable Ray Okafor was" | | 7 | "He was thirty-two, sharp in" | | 8 | "He had called her in" | | 9 | "he said, nodding at the" | | 10 | "Quinn crouched beside the body." | | 11 | "The man lay on his" | | 12 | "His eyes were half open," | | 13 | "His jacket was good wool," | | 14 | "Quinn looked up." | | 15 | "The gallery was twelve feet" | | 16 | "It was a plausible story." | | 17 | "It was also the story" | | 18 | "He lifted the torch and" | | 19 | "There, in a pale grey" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 5 | | matches | | 0 | "Ahead, the uniformed constable's torch swept across tiled walls gone the colour of weak tea, lighting up advertisements for cigarettes and a soap that had been …" | | 1 | "Detective Constable Ray Okafor was waiting beneath the work lamps, hands in his coat pockets, his breath showing in the cold." | | 2 | "She ran her gloved fingers along a seam and felt the faint cold draw of air, as though the wall were breathing out." | | 3 | "The cord was new, its knot tied with a precision that reminded her of the way certain people in her last case had folded their hands." | | 4 | "Beneath the cuff of the good wool jacket, the skin was marked with a faint pale band, the shape of a strap, as though someone had worn a watch there for years a…" |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, and the word sounded foolish in his mouth" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0 | | effectiveRatio | 0 | |