| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1466 | | totalAiIsmAdverbs | 2 | | 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) | |
| 48.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1466 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "chill" | | 1 | "gloom" | | 2 | "scanned" | | 3 | "flicker" | | 4 | "measured" | | 5 | "familiar" | | 6 | "echoing" | | 7 | "footsteps" | | 8 | "shimmered" | | 9 | "gleaming" | | 10 | "whisper" | | 11 | "tracing" | | 12 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 138 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 138 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "begin to" | | 2 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 14 | | totalWords | 1465 | | ratio | 0.01 | | matches | | 0 | "Fall back. Call it in. Secure the perimeter." | | 1 | "DS Morris, deceased. Circumstances under review." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1465 | | uniqueNames | 21 | | maxNameDensity | 0.89 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 13 | | London | 1 | | Tomás | 3 | | Herrera | 11 | | Soho | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Camden-bound | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 2 | | Regent | 1 | | Canal | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 2 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Regent" | | 6 | "Market" | | 7 | "Morris" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "Camden-bound" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like captured balls of light float" | | 1 | "looked like a piece of carved bone, white" | | 2 | "felt like the source of that loose thre" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.683 | | wordCount | 1465 | | matches | | 0 | "not by harsh fluorescent strips, but by the flickering, unnatural glow of what looked like captur" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 79.29% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 61.04 | | std | 26.1 | | cv | 0.428 | | sampleLengths | | 0 | 106 | | 1 | 54 | | 2 | 5 | | 3 | 83 | | 4 | 38 | | 5 | 66 | | 6 | 73 | | 7 | 59 | | 8 | 88 | | 9 | 9 | | 10 | 79 | | 11 | 94 | | 12 | 66 | | 13 | 69 | | 14 | 112 | | 15 | 60 | | 16 | 69 | | 17 | 40 | | 18 | 62 | | 19 | 33 | | 20 | 71 | | 21 | 48 | | 22 | 43 | | 23 | 38 |
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| 95.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 138 | | matches | | 0 | "was spooked" | | 1 | "was fuelled" | | 2 | "was lost" | | 3 | "was lit" | | 4 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 226 | | matches | | 0 | "was heading" | | 1 | "was flagging" | | 2 | "were standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 138 | | ratio | 0.007 | | matches | | 0 | "The single, inexplicable clue left at the scene—an object that felt cold to the touch even in the summer heat, an object that the forensics lab couldn’t identify." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1475 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.01830508474576271 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006779661016949152 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 10.62 | | std | 7.04 | | cv | 0.663 | | sampleLengths | | 0 | 22 | | 1 | 19 | | 2 | 21 | | 3 | 12 | | 4 | 16 | | 5 | 4 | | 6 | 5 | | 7 | 7 | | 8 | 20 | | 9 | 14 | | 10 | 2 | | 11 | 15 | | 12 | 3 | | 13 | 4 | | 14 | 1 | | 15 | 22 | | 16 | 21 | | 17 | 22 | | 18 | 3 | | 19 | 2 | | 20 | 4 | | 21 | 9 | | 22 | 9 | | 23 | 14 | | 24 | 11 | | 25 | 4 | | 26 | 20 | | 27 | 14 | | 28 | 19 | | 29 | 4 | | 30 | 4 | | 31 | 5 | | 32 | 23 | | 33 | 9 | | 34 | 3 | | 35 | 16 | | 36 | 12 | | 37 | 10 | | 38 | 24 | | 39 | 5 | | 40 | 9 | | 41 | 4 | | 42 | 17 | | 43 | 7 | | 44 | 19 | | 45 | 15 | | 46 | 18 | | 47 | 23 | | 48 | 4 | | 49 | 2 |
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| 41.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3115942028985507 | | totalSentences | 138 | | uniqueOpeners | 43 | |
| 52.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 127 | | matches | | 0 | "Just her and him in" | | 1 | "Then he stumbled through the" |
| | ratio | 0.016 | |
| 84.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 127 | | matches | | 0 | "He scanned the Soho street," | | 1 | "He was spooked." | | 2 | "She gave him a twenty-yard" | | 3 | "She pulled up the collar" | | 4 | "She didn't run." | | 5 | "She preferred the silent, inexorable" | | 6 | "Her own feet pounded the" | | 7 | "He was younger, faster." | | 8 | "She knew this city's veins." | | 9 | "He weaved through a warren" | | 10 | "She cut left, taking a" | | 11 | "She emerged from the passage" | | 12 | "He'd expected to be free." | | 13 | "He reversed course, skidding on" | | 14 | "He glanced over his shoulder," | | 15 | "He was heading north." | | 16 | "He had a destination." | | 17 | "They ran past shuttered storefronts" | | 18 | "She focused on his rhythm," | | 19 | "He was fuelled by fear," |
| | ratio | 0.339 | |
| 50.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 127 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn drummed her" | | 2 | "The worn leather of her" | | 3 | "The intel was good." | | 4 | "The clique used this place." | | 5 | "A figure finally pushed through" | | 6 | "He scanned the Soho street," | | 7 | "He was spooked." | | 8 | "Quinn killed the engine." | | 9 | "She gave him a twenty-yard" | | 10 | "The air was cold and" | | 11 | "She pulled up the collar" | | 12 | "She didn't run." | | 13 | "Running announced the chase." | | 14 | "She preferred the silent, inexorable" | | 15 | "Herrera cut down a narrow" | | 16 | "The last of the theatre" | | 17 | "Quinn rounded the corner just" | | 18 | "The game was on." | | 19 | "Her own feet pounded the" |
| | ratio | 0.819 | |
| 39.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 127 | | matches | | 0 | "Even in the gloom, Quinn" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 3 | | matches | | 0 | "She focused on his rhythm, the cadence of his feet, matching it, waiting for him to tire." | | 1 | "Two broad-shouldered figures stood before an archway that shimmered, the air within it seeming to warp and twist." | | 2 | "The single, inexplicable clue left at the scene—an object that felt cold to the touch even in the summer heat, an object that the forensics lab couldn’t identif…" |
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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 | |