| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | 0 | "the way he'd always [always]" |
| | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 815 | | 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) | |
| 63.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 815 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silk" | | 1 | "measured" | | 2 | "vibrated" | | 3 | "maw" | | 4 | "reminder" | | 5 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "without second thought" | | count | 1 |
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| | highlights | | 0 | "without a second thought" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 88 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 815 | | 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 | 20 | | wordCount | 801 | | uniqueNames | 11 | | maxNameDensity | 0.5 | | worstName | "Harlow" | | maxWindowNameDensity | 1 | | worstWindowName | "Herrera" | | discoveredNames | | Quinn | 2 | | Herrera | 3 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Morris | 3 | | Met | 1 | | Veil | 1 | | Market | 1 | | Tomás | 2 | | Harlow | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Met" | | 5 | "Tomás" | | 6 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "something like ozone and old parchment that" |
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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 | 815 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 88 | | matches | | |
| 62.29% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 67.92 | | std | 24.99 | | cv | 0.368 | | sampleLengths | | 0 | 89 | | 1 | 72 | | 2 | 102 | | 3 | 62 | | 4 | 91 | | 5 | 86 | | 6 | 64 | | 7 | 37 | | 8 | 93 | | 9 | 43 | | 10 | 60 | | 11 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 115 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 88 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 557 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.008976660682226212 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0017953321364452424 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 9.26 | | std | 6.65 | | cv | 0.719 | | sampleLengths | | 0 | 14 | | 1 | 27 | | 2 | 23 | | 3 | 2 | | 4 | 15 | | 5 | 3 | | 6 | 5 | | 7 | 14 | | 8 | 3 | | 9 | 4 | | 10 | 22 | | 11 | 8 | | 12 | 21 | | 13 | 6 | | 14 | 22 | | 15 | 5 | | 16 | 10 | | 17 | 15 | | 18 | 18 | | 19 | 4 | | 20 | 3 | | 21 | 1 | | 22 | 12 | | 23 | 4 | | 24 | 2 | | 25 | 8 | | 26 | 9 | | 27 | 4 | | 28 | 2 | | 29 | 6 | | 30 | 14 | | 31 | 12 | | 32 | 7 | | 33 | 6 | | 34 | 13 | | 35 | 5 | | 36 | 9 | | 37 | 3 | | 38 | 25 | | 39 | 2 | | 40 | 2 | | 41 | 8 | | 42 | 3 | | 43 | 9 | | 44 | 6 | | 45 | 6 | | 46 | 11 | | 47 | 14 | | 48 | 9 | | 49 | 19 |
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| 35.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2727272727272727 | | totalSentences | 88 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 57.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 79 | | matches | | 0 | "Her quarry, a wiry blur" | | 1 | "She'd been tailing him for" | | 2 | "He glanced back, eyes wide" | | 3 | "She moved with a measured," | | 4 | "Her boots splashed through a" | | 5 | "He vaulted a low brick" | | 6 | "She took it at a" | | 7 | "It looked ancient, out of" | | 8 | "Her instincts, honed by eighteen" | | 9 | "He pulled something out, his" | | 10 | "He pressed it against the" | | 11 | "He vanished into the black" | | 12 | "She'd known better." | | 13 | "She'd seen the thing in" | | 14 | "Her fingers brushed the iron" | | 15 | "It was cold, colder than" | | 16 | "She could radio it in." | | 17 | "They'd come with torches and" | | 18 | "She knew that with a" | | 19 | "They'd laugh at her." |
| | ratio | 0.405 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 79 | | matches | | 0 | "Harlow Quinn moved through the" | | 1 | "Her quarry, a wiry blur" | | 2 | "She'd been tailing him for" | | 3 | "The Raven's Nest." | | 4 | "A known nest of vipers." | | 5 | "He glanced back, eyes wide" | | 6 | "Harlow didn't run." | | 7 | "Running was for rookies." | | 8 | "She moved with a measured," | | 9 | "The alley stank of rot" | | 10 | "Her boots splashed through a" | | 11 | "He vaulted a low brick" | | 12 | "She took it at a" | | 13 | "A door, heavy and iron," | | 14 | "It looked ancient, out of" | | 15 | "A single green bulb buzzed" | | 16 | "A raven in flight." | | 17 | "Her instincts, honed by eighteen" | | 18 | "This was a threshold." | | 19 | "Herrera shoved a hand into" |
| | ratio | 0.924 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "She moved with a measured, ground-eating stride that ate the distance between them, her breath a steady plume in the cold air." | | 1 | "A cold that seeped into the bone, a promise of something ancient and indifferent." | | 2 | "The stories whispered in the canteen, the rumours passed between constables who'd seen too much and drunk too little." | | 3 | "She took a breath, filling her lungs with the smell of rain and decay and something else, something like ozone and old parchment that drifted from the open door…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |