| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1057 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 66.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1057 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "measured" | | 1 | "pulsed" | | 2 | "throbbed" | | 3 | "velvet" | | 4 | "pulse" | | 5 | "flicked" |
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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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1057 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.94% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 999 | | uniqueNames | 12 | | maxNameDensity | 1.1 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 9 | | Camden | 3 | | Met | 1 | | Saint | 2 | | Christopher | 2 | | London | 1 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Morris | 1 | | Underground | 1 | | Quinn | 11 |
| | persons | | 0 | "Herrera" | | 1 | "Camden" | | 2 | "Met" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Quinn" |
| | places | | | globalScore | 0.949 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1057 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 31.09 | | std | 25.59 | | cv | 0.823 | | sampleLengths | | 0 | 23 | | 1 | 41 | | 2 | 73 | | 3 | 68 | | 4 | 81 | | 5 | 9 | | 6 | 41 | | 7 | 6 | | 8 | 36 | | 9 | 44 | | 10 | 49 | | 11 | 47 | | 12 | 30 | | 13 | 13 | | 14 | 37 | | 15 | 104 | | 16 | 43 | | 17 | 79 | | 18 | 4 | | 19 | 5 | | 20 | 9 | | 21 | 19 | | 22 | 11 | | 23 | 13 | | 24 | 15 | | 25 | 9 | | 26 | 11 | | 27 | 29 | | 28 | 42 | | 29 | 3 | | 30 | 5 | | 31 | 5 | | 32 | 35 | | 33 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 91 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 184 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 98 | | ratio | 0.01 | | matches | | 0 | "Eighteen years on the Met had taught her how to run in the rain; the army had taught her how to run in worse." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1007 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.012909632571996028 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0029791459781529296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 10.79 | | std | 5.67 | | cv | 0.526 | | sampleLengths | | 0 | 14 | | 1 | 9 | | 2 | 5 | | 3 | 20 | | 4 | 16 | | 5 | 8 | | 6 | 24 | | 7 | 13 | | 8 | 18 | | 9 | 10 | | 10 | 8 | | 11 | 20 | | 12 | 14 | | 13 | 11 | | 14 | 10 | | 15 | 5 | | 16 | 18 | | 17 | 15 | | 18 | 12 | | 19 | 9 | | 20 | 3 | | 21 | 4 | | 22 | 20 | | 23 | 9 | | 24 | 8 | | 25 | 16 | | 26 | 17 | | 27 | 6 | | 28 | 4 | | 29 | 21 | | 30 | 5 | | 31 | 3 | | 32 | 3 | | 33 | 5 | | 34 | 4 | | 35 | 8 | | 36 | 27 | | 37 | 14 | | 38 | 23 | | 39 | 7 | | 40 | 5 | | 41 | 27 | | 42 | 6 | | 43 | 14 | | 44 | 7 | | 45 | 14 | | 46 | 9 | | 47 | 13 | | 48 | 19 | | 49 | 13 |
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| 55.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.37755102040816324 | | totalSentences | 98 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 90 | | matches | | 0 | "Somewhere, a harp played itself," | | 1 | "Once, that bag would have" | | 2 | "Then the lights went out." |
| | ratio | 0.033 | |
| 95.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 90 | | matches | | 0 | "She lunged, hooked his collar," | | 1 | "He ran with the frantic" | | 2 | "She kept her breathing measured," | | 3 | "His left forearm, scarred from" | | 4 | "He was twenty-nine, fit from" | | 5 | "Her target didn't slow." | | 6 | "He grabbed the gate, hauled" | | 7 | "She slid to a halt" | | 8 | "She knew the brief." | | 9 | "She did not." | | 10 | "Her radio had died." | | 11 | "She had no warrant for" | | 12 | "It was yellowed, carved with" | | 13 | "It smelt of old graves." | | 14 | "She could drop it, walk" | | 15 | "She could pretend she'd lost" | | 16 | "She stepped over the threshold" | | 17 | "She wasn't here to shop." | | 18 | "She tracked the bobbing Saint" | | 19 | "He stopped at a stall" |
| | ratio | 0.311 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 90 | | matches | | 0 | "Quinn's boots slammed wet tarmac" | | 1 | "She lunged, hooked his collar," | | 2 | "Tomás Herrera didn't look back." | | 3 | "Rain slicked the Camden backstreet" | | 4 | "He ran with the frantic" | | 5 | "Quinn's worn leather watch flashed" | | 6 | "She kept her breathing measured," | | 7 | "A night bus hissed past" | | 8 | "The city stank of petrol," | | 9 | "The Saint Christopher medallion bounced" | | 10 | "His left forearm, scarred from" | | 11 | "He was twenty-nine, fit from" | | 12 | "Quinn was forty-one, sharp-jawed, and" | | 13 | "The clique had taken something" | | 14 | "Tonight, she wanted it back." | | 15 | "Herrera cut left, boots skidding" | | 16 | "Quinn followed, her shoulder clipping" | | 17 | "Graffiti crawled over the brick," | | 18 | "Camden's disused station." | | 19 | "Her target didn't slow." |
| | ratio | 0.889 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "Now it held whatever the" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn followed, her shoulder clipping a brick wall, the sting flaring down to her elbow." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0 | | effectiveRatio | 0 | |