| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 935 | | 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) | |
| 51.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 935 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "calculated" | | 1 | "pristine" | | 2 | "weight" | | 3 | "chill" | | 4 | "etched" | | 5 | "intricate" | | 6 | "furrowing" | | 7 | "reminder" | | 8 | "whisper" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 932 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 932 | | uniqueNames | 15 | | maxNameDensity | 1.18 | | worstName | "Vance" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Vance" | | discoveredNames | | Camden | 2 | | Tube | 1 | | Harlow | 10 | | Quinn | 2 | | Metropolitan | 2 | | Police | 2 | | Vance | 11 | | Detective | 2 | | South | 1 | | London | 1 | | Sergeant | 2 | | Morris | 1 | | Soho | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Vance" | | 3 | "Sergeant" | | 4 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "South" | | 2 | "London" | | 3 | "Soho" |
| | globalScore | 0.91 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 932 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 46.6 | | std | 26.55 | | cv | 0.57 | | sampleLengths | | 0 | 80 | | 1 | 20 | | 2 | 85 | | 3 | 37 | | 4 | 62 | | 5 | 35 | | 6 | 12 | | 7 | 13 | | 8 | 51 | | 9 | 73 | | 10 | 33 | | 11 | 48 | | 12 | 86 | | 13 | 27 | | 14 | 40 | | 15 | 103 | | 16 | 17 | | 17 | 22 | | 18 | 59 | | 19 | 29 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 82 | | matches | | 0 | "been ripped" | | 1 | "was curled" | | 2 | "cornered" | | 3 | "trained" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 156 | | matches | | 0 | "was vibrating" | | 1 | "weren't playing" |
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| 38.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 82 | | ratio | 0.037 | | matches | | 0 | "And the dust down here—it settles thick." | | 1 | "It reminded her of something she’d tried to forget for three agonizing years—the damp chill that had settled into the air the night DS Morris vanished from a locked basement in Soho, leaving behind no footprints, no struggle, and no answers." | | 2 | "She didn't flip it open—she didn't need to." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 942 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.024416135881104035 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.010615711252653927 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 11.37 | | std | 8.39 | | cv | 0.738 | | sampleLengths | | 0 | 22 | | 1 | 34 | | 2 | 24 | | 3 | 6 | | 4 | 14 | | 5 | 15 | | 6 | 7 | | 7 | 18 | | 8 | 27 | | 9 | 8 | | 10 | 10 | | 11 | 17 | | 12 | 20 | | 13 | 6 | | 14 | 12 | | 15 | 7 | | 16 | 3 | | 17 | 25 | | 18 | 3 | | 19 | 6 | | 20 | 5 | | 21 | 15 | | 22 | 15 | | 23 | 3 | | 24 | 9 | | 25 | 2 | | 26 | 5 | | 27 | 2 | | 28 | 4 | | 29 | 13 | | 30 | 21 | | 31 | 7 | | 32 | 10 | | 33 | 3 | | 34 | 23 | | 35 | 14 | | 36 | 4 | | 37 | 3 | | 38 | 8 | | 39 | 10 | | 40 | 8 | | 41 | 10 | | 42 | 3 | | 43 | 7 | | 44 | 7 | | 45 | 6 | | 46 | 10 | | 47 | 10 | | 48 | 5 | | 49 | 23 |
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| 76.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4878048780487805 | | totalSentences | 82 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 90.13% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 77 | | matches | | 0 | "She cared about the corpse" | | 1 | "She checked the time, her" | | 2 | "She kept her eyes fixed" | | 3 | "she asked, her voice flat," | | 4 | "They weren't spent casings or" | | 5 | "They were knuckle bones, meticulously" | | 6 | "She pointed her light at" | | 7 | "It’s like they materialized out" | | 8 | "They walk in circles when" | | 9 | "It could've drifted over their" | | 10 | "She turned her brown eyes" | | 11 | "She walked past him, circling" | | 12 | "Her gaze caught on a" | | 13 | "It wasn't oil." | | 14 | "It had a faint, iridescent" | | 15 | "It reminded her of something" | | 16 | "They didn't dump him here," | | 17 | "They brought him here to" | | 18 | "You've been staring at cold" | | 19 | "It was a brass compass" |
| | ratio | 0.325 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 77 | | matches | | 0 | "The air down in the" | | 1 | "Detective Harlow Quinn stood on" | | 2 | "Harlow didn't care about the" | | 3 | "She cared about the corpse" | | 4 | "She checked the time, her" | | 5 | "The victim was a young" | | 6 | "Sergeant Vance stepped up beside" | | 7 | "Vance was a good copper," | | 8 | "Vance muttered, blowing a plume" | | 9 | "Looks like a turf war" | | 10 | "Gang initiation, probably." | | 11 | "Some local thugs hauled him" | | 12 | "Harlow didn't look at him." | | 13 | "She kept her eyes fixed" | | 14 | "A sharp, calculated line defined" | | 15 | "she asked, her voice flat," | | 16 | "Harlow crouched, bringing her face" | | 17 | "The beam of her torch" | | 18 | "They weren't spent casings or" | | 19 | "They were knuckle bones, meticulously" |
| | ratio | 0.701 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn stood on the soot-stained platform where the tracks had long ago been ripped up, her heavy boots crunching on gravel that glittered oddly…" | | 1 | "It reminded her of something she’d tried to forget for three agonizing years—the damp chill that had settled into the air the night DS Morris vanished from a lo…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |