| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1200 | | 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) | |
| 45.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1200 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "fractured" | | 1 | "streaming" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "wavered" | | 5 | "echoed" | | 6 | "depths" | | 7 | "footfall" | | 8 | "grave" | | 9 | "flicker" | | 10 | "electric" |
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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 | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1187 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1187 | | uniqueNames | 14 | | maxNameDensity | 1.35 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 16 | | Charing | 2 | | Cross | 2 | | Road | 1 | | Leicester | 1 | | Square | 1 | | Chinatown | 1 | | Metropolitan | 1 | | Railway | 1 | | Tube | 1 | | Morris | 3 | | Ovaltine | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Leicester" | | 5 | "Chinatown" |
| | globalScore | 0.826 | | windowScore | 0.833 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "something like it" | | 1 | "smelled like rust and decay and something" |
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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 | 1187 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 39.06% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 56.52 | | std | 16.21 | | cv | 0.287 | | sampleLengths | | 0 | 84 | | 1 | 56 | | 2 | 58 | | 3 | 84 | | 4 | 60 | | 5 | 65 | | 6 | 48 | | 7 | 56 | | 8 | 62 | | 9 | 80 | | 10 | 45 | | 11 | 47 | | 12 | 25 | | 13 | 89 | | 14 | 53 | | 15 | 48 | | 16 | 40 | | 17 | 45 | | 18 | 60 | | 19 | 39 | | 20 | 43 |
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| 99.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 119 | | matches | | 0 | "was sealed" | | 1 | "were connected" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 207 | | matches | | 0 | "was gaining" | | 1 | "were heading" | | 2 | "were heading" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 119 | | ratio | 0.059 | | matches | | 0 | "The alley opened onto Charing Cross Road, and there—fifty metres ahead—her suspect cut across traffic toward Leicester Square." | | 1 | "They were heading toward—" | | 2 | "The air changed immediately—became cooler, damper, carrying the metallic taste of old iron and something else she couldn't place." | | 3 | "A sound echoed from the depths—a footfall, or something like it." | | 4 | "The tunnel opened into a larger space—a platform." | | 5 | "Beyond the platform, the tunnel continued, and Quinn caught a flicker of movement—a shadow passing through her torch beam." | | 6 | "Quinn could see the glow of light from that direction now—not electric light, something warmer and greener." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1200 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.02666666666666667 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008333333333333333 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 9.97 | | std | 6.35 | | cv | 0.637 | | sampleLengths | | 0 | 25 | | 1 | 26 | | 2 | 12 | | 3 | 16 | | 4 | 2 | | 5 | 3 | | 6 | 24 | | 7 | 17 | | 8 | 2 | | 9 | 13 | | 10 | 18 | | 11 | 13 | | 12 | 18 | | 13 | 5 | | 14 | 4 | | 15 | 17 | | 16 | 17 | | 17 | 10 | | 18 | 3 | | 19 | 9 | | 20 | 5 | | 21 | 9 | | 22 | 3 | | 23 | 8 | | 24 | 3 | | 25 | 14 | | 26 | 12 | | 27 | 11 | | 28 | 17 | | 29 | 6 | | 30 | 3 | | 31 | 3 | | 32 | 15 | | 33 | 20 | | 34 | 9 | | 35 | 7 | | 36 | 2 | | 37 | 2 | | 38 | 4 | | 39 | 15 | | 40 | 19 | | 41 | 2 | | 42 | 2 | | 43 | 10 | | 44 | 10 | | 45 | 19 | | 46 | 16 | | 47 | 11 | | 48 | 9 | | 49 | 20 |
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| 63.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.42857142857142855 | | totalSentences | 119 | | uniqueOpeners | 51 | |
| 32.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 103 | | matches | | 0 | "Too late for civilians, which" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 103 | | matches | | 0 | "They got sloppy." | | 1 | "She rounded the corner into" | | 2 | "She'd run the face through" | | 3 | "They ran harder." | | 4 | "She was gaining." | | 5 | "She could see the shape" | | 6 | "They were heading toward Charing" | | 7 | "They were heading toward—" | | 8 | "She slipped through and stopped," | | 9 | "Her suspect was in there" | | 10 | "She'd been here once before," | | 11 | "She'd seen the terror there." | | 12 | "She stood at the mouth" | | 13 | "She took a breath." | | 14 | "She'd smelled it the night" | | 15 | "Her torch found tiles on" | | 16 | "Her suspect had come through" | | 17 | "She moved forward, her footsteps" | | 18 | "She could hear something now" | | 19 | "Her suspect had rounded a" |
| | ratio | 0.243 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 103 | | matches | | 0 | "The rain hammered the pavement" | | 1 | "Detective Harlow Quinn moved through" | | 2 | "Rain plastered her salt-and-pepper hair" | | 3 | "They got sloppy." | | 4 | "The figure ducked left into" | | 5 | "Quinn picked up her pace," | | 6 | "She rounded the corner into" | | 7 | "Quinn vaulted over him without" | | 8 | "The alley opened onto Charing" | | 9 | "A taxi blared its horn." | | 10 | "The figure didn't stop." | | 11 | "Quinn burst from the alley" | | 12 | "The suspect was fast, athletic," | | 13 | "She'd run the face through" | | 14 | "That alone made her suspicious." | | 15 | "Innocent people didn't run from" | | 16 | "They ran harder." | | 17 | "The figure veered south, abandoning" | | 18 | "The buildings here crowded close," | | 19 | "Puddles the size of small" |
| | ratio | 0.718 | |
| 97.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 103 | | matches | | 0 | "Now it was just a" | | 1 | "As if precision could protect" |
| | ratio | 0.019 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 4 | | matches | | 0 | "The rain hammered the pavement like machine-gun fire, turning the Soho streets into rivers of black glass that reflected the amber streetlights in fractured sha…" | | 1 | "Quinn vaulted over him without looking back, her breath coming hard and even." | | 2 | "The suspect was fast, athletic, moving with the desperation of someone who knew exactly what they'd done." | | 3 | "Her suspect was in there somewhere, in that underground maze that hadn't seen a passenger train in probably fifty years." |
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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 | |