| 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 | 1198 | | 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) | |
| 74.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1198 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flicker" | | 1 | "flickered" | | 2 | "weight" | | 3 | "perfect" | | 4 | "pulse" | | 5 | "measured" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1159 | | uniqueNames | 24 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 3 | | Harlow | 1 | | Quinn | 13 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Wardour | 1 | | MoD | 1 | | Soho | 1 | | Frith | 1 | | Italian | 1 | | Raven | 1 | | Nest | 1 | | Oxford | 1 | | Morris | 2 | | Camden | 2 | | Market | 3 | | Tube | 2 | | Veil | 2 | | Rafaele | 1 | | Rain | 3 | | Water | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Morris" | | 7 | "Camden" | | 8 | "Market" | | 9 | "Rain" | | 10 | "Water" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Frith" | | 5 | "Italian" | | 6 | "Oxford" | | 7 | "Veil" |
| | globalScore | 0.939 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1196 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 90.42% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 54.36 | | std | 25.36 | | cv | 0.466 | | sampleLengths | | 0 | 67 | | 1 | 10 | | 2 | 61 | | 3 | 55 | | 4 | 9 | | 5 | 80 | | 6 | 68 | | 7 | 36 | | 8 | 87 | | 9 | 9 | | 10 | 62 | | 11 | 46 | | 12 | 57 | | 13 | 82 | | 14 | 68 | | 15 | 71 | | 16 | 9 | | 17 | 91 | | 18 | 67 | | 19 | 41 | | 20 | 75 | | 21 | 45 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 192 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 110 | | ratio | 0.009 | | matches | | 0 | "The material facts of this chase—the stolen sample from the lab, the Rafaele ledger pages, Herrera’s off-book clinics—all pointed down those steps." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1171 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.025619128949615714 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004269854824935952 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 10.87 | | std | 6.29 | | cv | 0.579 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 17 | | 3 | 19 | | 4 | 10 | | 5 | 6 | | 6 | 11 | | 7 | 8 | | 8 | 19 | | 9 | 4 | | 10 | 13 | | 11 | 10 | | 12 | 2 | | 13 | 10 | | 14 | 9 | | 15 | 19 | | 16 | 5 | | 17 | 9 | | 18 | 4 | | 19 | 19 | | 20 | 15 | | 21 | 4 | | 22 | 7 | | 23 | 19 | | 24 | 12 | | 25 | 14 | | 26 | 2 | | 27 | 12 | | 28 | 21 | | 29 | 14 | | 30 | 5 | | 31 | 13 | | 32 | 7 | | 33 | 11 | | 34 | 5 | | 35 | 15 | | 36 | 3 | | 37 | 5 | | 38 | 8 | | 39 | 16 | | 40 | 12 | | 41 | 20 | | 42 | 2 | | 43 | 6 | | 44 | 9 | | 45 | 14 | | 46 | 13 | | 47 | 4 | | 48 | 2 | | 49 | 9 |
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| 79.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4818181818181818 | | totalSentences | 110 | | uniqueOpeners | 53 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 99 | | matches | | 0 | "Instead he shouldered through a" | | 1 | "Somewhere deeper a stringed instrument" | | 2 | "Then she took one deliberate" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 99 | | matches | | 0 | "His short curly dark brown" | | 1 | "He vaulted a tipped restaurant" | | 2 | "Her military bearing held her" | | 3 | "He burst onto Wardour and" | | 4 | "She snatched the door handle" | | 5 | "He glanced back once." | | 6 | "She seized the iron and" | | 7 | "He kicked free and dropped" | | 8 | "She cleared it in one" | | 9 | "She crushed the memory and" | | 10 | "Her lungs burned with iron" | | 11 | "He reached Frith Street and" | | 12 | "He laughed once, sharp and" | | 13 | "She spat water and ran" | | 14 | "She knew that now." | | 15 | "She had lost DS Morris" | | 16 | "They left the neon and" | | 17 | "She matched him stride for" | | 18 | "He reached the edge of" | | 19 | "He dropped into a stairwell" |
| | ratio | 0.283 | |
| 40.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 99 | | matches | | 0 | "Detective Harlow Quinn drove her" | | 1 | "Water sheeted from her closely" | | 2 | "The worn leather watch on" | | 3 | "Tomás Herrera cut left without" | | 4 | "His short curly dark brown" | | 5 | "The Saint Christopher medallion bounced" | | 6 | "He vaulted a tipped restaurant" | | 7 | "Quinn followed at once." | | 8 | "Her military bearing held her" | | 9 | "He burst onto Wardour and" | | 10 | "The cabbie’s horn blared and" | | 11 | "Quinn’s boot found the slick" | | 12 | "She snatched the door handle" | | 13 | "Water exploded around her ankles." | | 14 | "He glanced back once." | | 15 | "Steam rose from a basement" | | 16 | "Quinn plunged through it." | | 17 | "The heat kissed her face" | | 18 | "Brick walls closed in, layered" | | 19 | "Neon bled from upper windows" |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 6 | | matches | | 0 | "He vaulted a tipped restaurant bin and scattered paper napkins that stuck to the wet bricks like white wounds." | | 1 | "He kicked free and dropped to the far side of a low wall that divided the alley from a private courtyard." | | 2 | "For a heartbeat the light bathed Herrera’s left forearm and revealed the old scar that ran pale against olive flesh." | | 3 | "Instead he shouldered through a section of temporary hoarding that fronted an abandoned building site." | | 4 | "Faint market murmurs drifted up: voices speaking languages she could not name, the soft clink of glass on stone, a single distant cry that might have been a pea…" | | 5 | "Light spilled from beyond: stalls lit by jars of phosphor and oil lamps, racks of bottles that glowed like trapped fireflies, cages that held shapes too thin fo…" |
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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 | |