| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1017 | | 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) | |
| 80.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1017 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "pulse" | | 2 | "flicker" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 5 | | totalWords | 1007 | | ratio | 0.005 | | matches | | 0 | "unresolved" | | 1 | "in the wind" | | 2 | "unresolved" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 939 | | uniqueNames | 20 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Dean | 1 | | Street | 1 | | Tomás | 1 | | Herrera | 5 | | Berwick | 1 | | Wardour | 1 | | Quinn | 7 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Met | 2 | | Hendon | 1 | | Morris | 4 | | Friday | 1 | | Talisker | 1 | | Tube | 1 | | Underground | 1 | | Town | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Wardour" | | 3 | "Quinn" | | 4 | "Nest" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Berwick" | | 3 | "Raven" | | 4 | "Soho" | | 5 | "Met" | | 6 | "Hendon" | | 7 | "Town" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite any colour she owned a word for" |
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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 | 1007 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 29.62 | | std | 25 | | cv | 0.844 | | sampleLengths | | 0 | 52 | | 1 | 2 | | 2 | 49 | | 3 | 25 | | 4 | 15 | | 5 | 10 | | 6 | 50 | | 7 | 6 | | 8 | 85 | | 9 | 73 | | 10 | 5 | | 11 | 6 | | 12 | 57 | | 13 | 1 | | 14 | 67 | | 15 | 11 | | 16 | 1 | | 17 | 35 | | 18 | 29 | | 19 | 3 | | 20 | 54 | | 21 | 27 | | 22 | 21 | | 23 | 57 | | 24 | 5 | | 25 | 75 | | 26 | 49 | | 27 | 23 | | 28 | 44 | | 29 | 4 | | 30 | 43 | | 31 | 8 | | 32 | 3 | | 33 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 171 | | matches | | 0 | "was telling" | | 1 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 90 | | ratio | 0.067 | | matches | | 0 | "Three times in the last month she'd had him cornered—once outside the clinic on Berwick, once in a café off Wardour, once standing ten feet away in a crowd she couldn't legally part—and three times he'd slipped her." | | 1 | "\"—peat, Quinn, say—gain—\"" | | 2 | "The green neon from the Raven's Nest washed his face for half a second—olive skin, the flash of the medallion at his throat, eyes too wide for a man who simply didn't want to answer questions." | | 3 | "Below, footsteps on stone, and beneath the footsteps a hum—voices, maybe, or a generator, or neither." | | 4 | "Quinn drew her baton, not her weapon—she was not yet so far from the Met as to forget herself—and went down." | | 5 | "Not because of what was in it—she refused, for now, to look too closely at what was in it—but because every face turned, at once, toward the archway where she stood." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 164 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.036585365853658534 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.006097560975609756 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 11.19 | | std | 10.24 | | cv | 0.915 | | sampleLengths | | 0 | 17 | | 1 | 7 | | 2 | 28 | | 3 | 2 | | 4 | 3 | | 5 | 3 | | 6 | 38 | | 7 | 5 | | 8 | 7 | | 9 | 18 | | 10 | 3 | | 11 | 9 | | 12 | 3 | | 13 | 10 | | 14 | 5 | | 15 | 36 | | 16 | 3 | | 17 | 3 | | 18 | 3 | | 19 | 6 | | 20 | 15 | | 21 | 6 | | 22 | 28 | | 23 | 1 | | 24 | 9 | | 25 | 26 | | 26 | 6 | | 27 | 7 | | 28 | 14 | | 29 | 8 | | 30 | 38 | | 31 | 2 | | 32 | 2 | | 33 | 1 | | 34 | 6 | | 35 | 25 | | 36 | 17 | | 37 | 1 | | 38 | 1 | | 39 | 13 | | 40 | 1 | | 41 | 7 | | 42 | 39 | | 43 | 2 | | 44 | 2 | | 45 | 17 | | 46 | 11 | | 47 | 1 | | 48 | 2 | | 49 | 16 |
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| 80.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5111111111111111 | | totalSentences | 90 | | uniqueOpeners | 46 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 73 | | matches | | 0 | "Then a voice, broken up," | | 1 | "Somewhere a fiddle played something" | | 2 | "Then he ran." |
| | ratio | 0.041 | |
| 99.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 73 | | matches | | 0 | "Her coat slapped at her" | | 1 | "He didn't stop." | | 2 | "He never did." | | 3 | "She keyed her radio as" | | 4 | "She pushed the radio back" | | 5 | "He was frightened." | | 6 | "He banked hard left into" | | 7 | "She catalogued each detail as" | | 8 | "She pushed harder, legs burning," | | 9 | "He was looking for a" | | 10 | "She watched him find it:" | | 11 | "He paused at the top" | | 12 | "He closed his fist around" | | 13 | "She stopped halfway down and" | | 14 | "She drew her radio again." | | 15 | "She looked at the stairwell" | | 16 | "She could go up." | | 17 | "She could put Herrera down" | | 18 | "She'd bought them as a" | | 19 | "It was not closed now." |
| | ratio | 0.301 | |
| 55.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 73 | | matches | | 0 | "Rain came sideways down Dean" | | 1 | "Quinn ran through it without" | | 2 | "Her coat slapped at her" | | 3 | "He didn't stop." | | 4 | "He never did." | | 5 | "Tonight would not make four." | | 6 | "She keyed her radio as" | | 7 | "Static answered her." | | 8 | "\"—peat, Quinn, say—" | | 9 | "She pushed the radio back" | | 10 | "Herrera glanced over his shoulder." | | 11 | "The green neon from the" | | 12 | "He was frightened." | | 13 | "That was what kept her" | | 14 | "He banked hard left into" | | 15 | "A fire escape clanged somewhere" | | 16 | "Water poured from a broken" | | 17 | "She catalogued each detail as" | | 18 | "Herrera cut right at the" | | 19 | "She pushed harder, legs burning," |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "Wrought-iron lamps that shouldn't have been here in Soho." | | 1 | "The stairs went down further than they ought to, past a level of rusted rail and a second of cracked tile, and then further still into a dark that smelled of we…" |
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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 | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |