| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1218 | | 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) | |
| 83.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1218 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "pulsed" | | 2 | "clandestine" | | 3 | "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 | 91 | | matches | (empty) | |
| 95.76% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | 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 | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 81 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1207 | | uniqueNames | 22 | | maxNameDensity | 0.66 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Quinn | 8 | | Raven | 3 | | Nest | 3 | | Seville | 1 | | London | 1 | | Saint | 2 | | Christopher | 2 | | Herrera | 2 | | Blackfriars | 1 | | Bridge | 1 | | Camden | 4 | | Bethnal | 1 | | Green | 1 | | Morris | 3 | | Metropolitan | 1 | | Police | 1 | | Spanish | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Tomás | 7 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Market" | | 7 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Seville" | | 2 | "London" | | 3 | "Blackfriars" | | 4 | "Bridge" | | 5 | "Camden" | | 6 | "Bethnal" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1218 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 39.29 | | std | 24.19 | | cv | 0.616 | | sampleLengths | | 0 | 42 | | 1 | 45 | | 2 | 65 | | 3 | 50 | | 4 | 84 | | 5 | 42 | | 6 | 44 | | 7 | 47 | | 8 | 81 | | 9 | 4 | | 10 | 36 | | 11 | 49 | | 12 | 23 | | 13 | 4 | | 14 | 53 | | 15 | 60 | | 16 | 8 | | 17 | 11 | | 18 | 13 | | 19 | 54 | | 20 | 4 | | 21 | 87 | | 22 | 52 | | 23 | 20 | | 24 | 28 | | 25 | 67 | | 26 | 18 | | 27 | 13 | | 28 | 60 | | 29 | 11 | | 30 | 43 |
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| 85.98% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 91 | | matches | | 0 | "been told" | | 1 | "been painted" | | 2 | "was blocked" | | 3 | "been reopened" | | 4 | "been redacted" |
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| 95.83% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 192 | | matches | | 0 | "was going" | | 1 | "was running" | | 2 | "wasn’t running" |
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| 82.07% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 94 | | ratio | 0.021 | | matches | | 0 | "Tomás wasn’t running now; he was moving with purpose, as if he knew the route." | | 1 | "The market shifted every full moon; by morning it could be gone, and any evidence with it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1212 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.028052805280528052 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007425742574257425 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 12.96 | | std | 10.51 | | cv | 0.811 | | sampleLengths | | 0 | 23 | | 1 | 19 | | 2 | 23 | | 3 | 22 | | 4 | 11 | | 5 | 16 | | 6 | 15 | | 7 | 23 | | 8 | 4 | | 9 | 16 | | 10 | 22 | | 11 | 8 | | 12 | 2 | | 13 | 1 | | 14 | 81 | | 15 | 16 | | 16 | 26 | | 17 | 12 | | 18 | 9 | | 19 | 10 | | 20 | 8 | | 21 | 5 | | 22 | 17 | | 23 | 9 | | 24 | 16 | | 25 | 5 | | 26 | 6 | | 27 | 8 | | 28 | 25 | | 29 | 21 | | 30 | 18 | | 31 | 3 | | 32 | 4 | | 33 | 5 | | 34 | 15 | | 35 | 16 | | 36 | 19 | | 37 | 10 | | 38 | 5 | | 39 | 15 | | 40 | 23 | | 41 | 4 | | 42 | 9 | | 43 | 15 | | 44 | 10 | | 45 | 19 | | 46 | 6 | | 47 | 15 | | 48 | 9 | | 49 | 8 |
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| 38.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3191489361702128 | | totalSentences | 94 | | uniqueOpeners | 30 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Somewhere a tube train rattled." |
| | ratio | 0.011 | |
| 65.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 88 | | matches | | 0 | "He was half a head" | | 1 | "Her watch, worn leather cracked" | | 2 | "He knew the alleys." | | 3 | "He shouldn’t, not a man" | | 4 | "She’d lost Morris three years" | | 5 | "She still didn’t understand what" | | 6 | "He pressed his back to" | | 7 | "He moved again, faster, down" | | 8 | "She’d put a warrant in" | | 9 | "She knew better." | | 10 | "She could call it in." | | 11 | "She could wait for backup," | | 12 | "She could do the by-the-book" | | 13 | "He’d administered unauthorized treatments to" | | 14 | "She passed a graffiti tag" | | 15 | "He paused at a junction" | | 16 | "She could hear him breathe." | | 17 | "He wasn’t alone." | | 18 | "He answered in the same" | | 19 | "She’d read the chatter in" |
| | ratio | 0.386 | |
| 33.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 88 | | matches | | 0 | "Harlow Quinn pulled her coat" | | 1 | "He was half a head" | | 2 | "The medallion at his throat" | | 3 | "Quinn’s boots struck the wet" | | 4 | "Military precision had kept her" | | 5 | "Her watch, worn leather cracked" | | 6 | "He knew the alleys." | | 7 | "He shouldn’t, not a man" | | 8 | "The Saint Christopher medallion swung" | | 9 | "The name had surfaced in" | | 10 | "She’d lost Morris three years" | | 11 | "She still didn’t understand what" | | 12 | "Tomás stopped abruptly at the" | | 13 | "He pressed his back to" | | 14 | "Quinn slowed, pistol still holstered," | | 15 | "The rain drummed on corrugated" | | 16 | "He moved again, faster, down" | | 17 | "The stairwell smelled of damp" | | 18 | "Quinn hesitated at the top" | | 19 | "The rain slicked the concrete" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 7 | | matches | | 0 | "She’d lost Morris three years ago on a case that had no business having unexplained circumstances." | | 1 | "She could do the by-the-book thing that would keep her pension clean and her superiors happy." | | 2 | "She passed a graffiti tag that had been painted over and painted over again, the old layers ghosting through." | | 3 | "She’d read the chatter in the intelligence files, the rumors of an underground market that sold enchanted goods, banned alchemical substances, information." | | 4 | "The roof arched high above, supported by iron girders draped in hanging moss that glowed faintly blue." | | 5 | "Stalls lined the walls, makeshift tables covered with jars that pulsed, vials that steamed despite the cold, and bundles of herbs that smelled wrong, too sweet." | | 6 | "She thought of the bookshelf behind The Raven’s Nest and the clandestine meetings that happened there, and the fact that Tomás Herrera kept showing up at every …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |