| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1229 | | 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) | |
| 87.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1229 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pumping" | | 1 | "stark" | | 2 | "footsteps" |
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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 | 164 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 178 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1229 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1089 | | uniqueNames | 19 | | maxNameDensity | 1.65 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 18 | | Herrera | 16 | | Saint | 2 | | Christopher | 2 | | Berwick | 1 | | St | 1 | | Anne | 1 | | Court | 1 | | Morris | 1 | | London | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Rain | 4 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Anne" | | 7 | "Morris" | | 8 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Berwick" | | 2 | "St" | | 3 | "London" |
| | globalScore | 0.674 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like teeth strung on wire" |
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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 | 1229 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 178 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 16.61 | | std | 13.66 | | cv | 0.823 | | sampleLengths | | 0 | 5 | | 1 | 30 | | 2 | 36 | | 3 | 3 | | 4 | 38 | | 5 | 5 | | 6 | 11 | | 7 | 16 | | 8 | 2 | | 9 | 45 | | 10 | 5 | | 11 | 13 | | 12 | 8 | | 13 | 15 | | 14 | 3 | | 15 | 10 | | 16 | 18 | | 17 | 31 | | 18 | 12 | | 19 | 3 | | 20 | 10 | | 21 | 14 | | 22 | 6 | | 23 | 24 | | 24 | 38 | | 25 | 12 | | 26 | 4 | | 27 | 15 | | 28 | 2 | | 29 | 29 | | 30 | 12 | | 31 | 2 | | 32 | 12 | | 33 | 3 | | 34 | 36 | | 35 | 10 | | 36 | 13 | | 37 | 3 | | 38 | 8 | | 39 | 21 | | 40 | 21 | | 41 | 36 | | 42 | 5 | | 43 | 6 | | 44 | 12 | | 45 | 5 | | 46 | 67 | | 47 | 2 | | 48 | 59 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 164 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 201 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 178 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1091 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.021998166819431713 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 178 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 178 | | mean | 6.9 | | std | 5.8 | | cv | 0.839 | | sampleLengths | | 0 | 5 | | 1 | 7 | | 2 | 23 | | 3 | 14 | | 4 | 11 | | 5 | 5 | | 6 | 6 | | 7 | 3 | | 8 | 4 | | 9 | 20 | | 10 | 14 | | 11 | 5 | | 12 | 3 | | 13 | 8 | | 14 | 6 | | 15 | 7 | | 16 | 3 | | 17 | 2 | | 18 | 9 | | 19 | 10 | | 20 | 14 | | 21 | 12 | | 22 | 5 | | 23 | 3 | | 24 | 7 | | 25 | 3 | | 26 | 8 | | 27 | 15 | | 28 | 3 | | 29 | 4 | | 30 | 6 | | 31 | 8 | | 32 | 6 | | 33 | 4 | | 34 | 5 | | 35 | 12 | | 36 | 8 | | 37 | 6 | | 38 | 6 | | 39 | 4 | | 40 | 2 | | 41 | 3 | | 42 | 6 | | 43 | 2 | | 44 | 2 | | 45 | 3 | | 46 | 11 | | 47 | 6 | | 48 | 2 | | 49 | 8 |
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| 42.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.30337078651685395 | | totalSentences | 178 | | uniqueOpeners | 54 | |
| 70.42% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 142 | | matches | | 0 | "Then Herrera bolted." | | 1 | "Somewhere below, a market noise" | | 2 | "Too many teeth." |
| | ratio | 0.021 | |
| 90.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 142 | | matches | | 0 | "It turned the pavement to" | | 1 | "It turned the green neon" | | 2 | "Her cropped salt-and-pepper hair dripped." | | 3 | "He hunched against the weather," | | 4 | "His olive skin went pale" | | 5 | "Her boots hit the wet" | | 6 | "He cut left down Berwick," | | 7 | "He didn't stop." | | 8 | "He vaulted a stack of" | | 9 | "She cut the call." | | 10 | "She rounded it hard, shoulder" | | 11 | "Her palm slapped brick for" | | 12 | "He wasn't on the street." | | 13 | "He was halfway down the" | | 14 | "It clattered and spilled." | | 15 | "Her left ankle twisted on" | | 16 | "His eyes showed wide and" | | 17 | "His foot lost grip on" | | 18 | "He went down to one" | | 19 | "He was stronger than he" |
| | ratio | 0.324 | |
| 40.99% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 142 | | matches | | 0 | "It turned the pavement to" | | 1 | "It turned the green neon" | | 2 | "Detective Harlow Quinn stood under" | | 3 | "The leather of her watch" | | 4 | "Her cropped salt-and-pepper hair dripped." | | 5 | "A brown eye tracked the" | | 6 | "The door opened." | | 7 | "Tomás Herrera stepped out." | | 8 | "He hunched against the weather," | | 9 | "The Saint Christopher medallion at" | | 10 | "Quinn pushed off the wall." | | 11 | "Herrera saw her." | | 12 | "His olive skin went pale" | | 13 | "The rain filled the space" | | 14 | "Her boots hit the wet" | | 15 | "Herrera had thirty feet and" | | 16 | "He cut left down Berwick," | | 17 | "Quinn kept her jaw tight," | | 18 | "He didn't stop." | | 19 | "He vaulted a stack of" |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "He was halfway down the alley that fed into St Anne's Court." | | 1 | "This was lower, a hum of voices in languages that hurt her ears, the clink of glass that shouldn't exist." |
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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 | |