| 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 | 2171 | | 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) | |
| 81.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2171 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "clandestine" | | 1 | "loomed" | | 2 | "pulse" | | 3 | "scanned" | | 4 | "traced" | | 5 | "warmth" |
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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 | 220 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 220 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 265 | | 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 | 0 | | markdownWords | 0 | | totalWords | 2171 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.07% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1919 | | uniqueNames | 19 | | maxNameDensity | 1.2 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 4 | | High | 1 | | Street | 1 | | Tomás | 13 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Lock | 1 | | Quinn | 23 | | Tube | 1 | | Morris | 2 | | Northern | 1 | | Rain | 3 | | Water | 3 | | Bone | 3 | | One | 3 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Lock" | | 5 | "Quinn" | | 6 | "Morris" | | 7 | "Rain" | | 8 | "Water" | | 9 | "Bone" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Soho" |
| | globalScore | 0.901 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 138 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.461 | | wordCount | 2171 | | matches | | 0 | "not from memory this time but from the old reflex" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 265 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 132 | | mean | 16.45 | | std | 19.81 | | cv | 1.205 | | sampleLengths | | 0 | 72 | | 1 | 28 | | 2 | 2 | | 3 | 4 | | 4 | 83 | | 5 | 43 | | 6 | 1 | | 7 | 10 | | 8 | 3 | | 9 | 61 | | 10 | 5 | | 11 | 5 | | 12 | 9 | | 13 | 5 | | 14 | 6 | | 15 | 25 | | 16 | 22 | | 17 | 7 | | 18 | 63 | | 19 | 5 | | 20 | 4 | | 21 | 55 | | 22 | 1 | | 23 | 1 | | 24 | 21 | | 25 | 3 | | 26 | 21 | | 27 | 25 | | 28 | 1 | | 29 | 3 | | 30 | 5 | | 31 | 15 | | 32 | 2 | | 33 | 25 | | 34 | 3 | | 35 | 2 | | 36 | 10 | | 37 | 3 | | 38 | 28 | | 39 | 71 | | 40 | 5 | | 41 | 4 | | 42 | 4 | | 43 | 15 | | 44 | 5 | | 45 | 3 | | 46 | 4 | | 47 | 2 | | 48 | 5 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 220 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 311 | | matches | | 0 | "was carrying" | | 1 | "was carrying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 265 | | ratio | 0.004 | | matches | | 0 | "If he was carrying something worse—" |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1925 | | adjectiveStacks | 2 | | stackExamples | | 0 | "white against wet skin," | | 1 | "worn smooth over years" |
| | adverbCount | 42 | | adverbRatio | 0.02181818181818182 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0015584415584415584 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 265 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 265 | | mean | 8.19 | | std | 6.57 | | cv | 0.802 | | sampleLengths | | 0 | 17 | | 1 | 19 | | 2 | 6 | | 3 | 7 | | 4 | 23 | | 5 | 4 | | 6 | 4 | | 7 | 2 | | 8 | 7 | | 9 | 11 | | 10 | 2 | | 11 | 4 | | 12 | 5 | | 13 | 44 | | 14 | 4 | | 15 | 6 | | 16 | 24 | | 17 | 11 | | 18 | 4 | | 19 | 12 | | 20 | 16 | | 21 | 1 | | 22 | 10 | | 23 | 3 | | 24 | 23 | | 25 | 24 | | 26 | 6 | | 27 | 8 | | 28 | 5 | | 29 | 5 | | 30 | 6 | | 31 | 3 | | 32 | 5 | | 33 | 6 | | 34 | 25 | | 35 | 4 | | 36 | 10 | | 37 | 5 | | 38 | 2 | | 39 | 1 | | 40 | 7 | | 41 | 3 | | 42 | 3 | | 43 | 13 | | 44 | 12 | | 45 | 5 | | 46 | 6 | | 47 | 8 | | 48 | 13 | | 49 | 5 |
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| 51.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.35471698113207545 | | totalSentences | 265 | | uniqueOpeners | 94 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 188 | | matches | | 0 | "Too many voices for a" | | 1 | "Just a thumb-sized recess, polished" | | 2 | "Too light for stone." | | 3 | "Somewhere below, a train announcement" | | 4 | "Too many teeth." | | 5 | "Just wrong somehow, all of" | | 6 | "Somewhere metal rang on metal," | | 7 | "Then she went after Tomás" |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 188 | | matches | | 0 | "Her coat clung to her" | | 1 | "Her leather watch stuck to" | | 2 | "He looked back once." | | 3 | "She’d watched him for three" | | 4 | "He hadn’t gone home." | | 5 | "He hadn’t gone to a" | | 6 | "He’d cut north, doubled back" | | 7 | "He vaulted a crate of" | | 8 | "He skidded round a corner," | | 9 | "He slammed his palm against" | | 10 | "She hit the turn two" | | 11 | "His voice cracked against the" | | 12 | "He kept running." | | 13 | "He glanced back again." | | 14 | "Her lungs burned." | | 15 | "Her knee twinged from the" | | 16 | "She’d gone over borough maps" | | 17 | "He fished inside his coat," | | 18 | "She hit it with her" | | 19 | "She flashed her warrant card" |
| | ratio | 0.207 | |
| 74.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 145 | | totalSentences | 188 | | matches | | 0 | "Quinn cut between a black" | | 1 | "Her coat clung to her" | | 2 | "Her leather watch stuck to" | | 3 | "He looked back once." | | 4 | "Streetlight caught his face." | | 5 | "The Saint Christopher medallion at" | | 6 | "Tomás didn’t break stride." | | 7 | "Quinn swore and pushed harder." | | 8 | "She’d watched him for three" | | 9 | "He hadn’t gone home." | | 10 | "He hadn’t gone to a" | | 11 | "He’d cut north, doubled back" | | 12 | "Canvas awnings snapped overhead." | | 13 | "Rain drummed on tin roofs" | | 14 | "He vaulted a crate of" | | 15 | "He skidded round a corner," | | 16 | "He slammed his palm against" | | 17 | "She hit the turn two" | | 18 | "Water cascaded off them in" | | 19 | "Tomás was twenty feet ahead." |
| | ratio | 0.771 | |
| 79.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 188 | | matches | | 0 | "Now he tore down a" | | 1 | "If he was carrying drugs," | | 2 | "If he was carrying something" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 78 | | technicalSentenceCount | 3 | | matches | | 0 | "The old ticket hall had been gutted and reborn under hanging bulbs, salvaged lanterns and strings of bare wire that hissed blue where rainwater touched them." | | 1 | "A huge man in a butcher’s apron carrying a crate that dripped black onto the tiles." | | 2 | "Tomás, shoving through the crowd near a shuttered platform sign, his medallion flashing once between shoulders and smoke." |
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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 | |