| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2086 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "lightly" | | 2 | "slowly" |
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| 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) | |
| 11.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2086 | | totalAiIsms | 37 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | |
| | highlights | | 0 | "gloom" | | 1 | "measured" | | 2 | "weight" | | 3 | "echoed" | | 4 | "pulse" | | 5 | "tension" | | 6 | "traced" | | 7 | "porcelain" | | 8 | "familiar" | | 9 | "silence" | | 10 | "fractured" | | 11 | "aligned" | | 12 | "synchronized" | | 13 | "shimmered" | | 14 | "warmth" | | 15 | "calculated" | | 16 | "resolve" | | 17 | "pulsed" | | 18 | "database" | | 19 | "unspoken" | | 20 | "echoing" | | 21 | "structure" | | 22 | "potential" | | 23 | "affection" |
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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 | 585 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 585 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 585 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2086 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 1 | | matches | | 0 | "You reek of damp wool, Aurora said." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 2086 | | uniqueNames | 15 | | maxNameDensity | 0.48 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "You" | | discoveredNames | | Aurora | 10 | | Thames | 1 | | Cardiff | 2 | | Grey | 1 | | Marseille | 3 | | You | 7 | | Lucien | 9 | | Close | 3 | | Distance | 3 | | Knuckles | 3 | | Breath | 3 | | Fingers | 3 | | Presence | 3 | | Reality | 3 | | Connection | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Grey" | | 2 | "You" | | 3 | "Lucien" | | 4 | "Distance" | | 5 | "Knuckles" | | 6 | "Breath" | | 7 | "Fingers" | | 8 | "Presence" | | 9 | "Reality" | | 10 | "Connection" |
| | places | | 0 | "Thames" | | 1 | "Cardiff" | | 2 | "Marseille" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2086 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 585 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 43.46 | | std | 48.25 | | cv | 1.11 | | sampleLengths | | 0 | 52 | | 1 | 36 | | 2 | 7 | | 3 | 19 | | 4 | 69 | | 5 | 6 | | 6 | 72 | | 7 | 6 | | 8 | 44 | | 9 | 15 | | 10 | 56 | | 11 | 38 | | 12 | 47 | | 13 | 48 | | 14 | 9 | | 15 | 59 | | 16 | 49 | | 17 | 20 | | 18 | 16 | | 19 | 64 | | 20 | 28 | | 21 | 32 | | 22 | 21 | | 23 | 44 | | 24 | 29 | | 25 | 36 | | 26 | 23 | | 27 | 31 | | 28 | 36 | | 29 | 39 | | 30 | 29 | | 31 | 6 | | 32 | 32 | | 33 | 41 | | 34 | 57 | | 35 | 25 | | 36 | 36 | | 37 | 36 | | 38 | 4 | | 39 | 33 | | 40 | 32 | | 41 | 36 | | 42 | 28 | | 43 | 35 | | 44 | 21 | | 45 | 71 | | 46 | 294 | | 47 | 219 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 585 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 542 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 585 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2089 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Effective against sharp memories." |
| | adverbCount | 30 | | adverbRatio | 0.014360938247965534 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.006223073240785065 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 585 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 585 | | mean | 3.57 | | std | 1.94 | | cv | 0.545 | | sampleLengths | | 0 | 4 | | 1 | 5 | | 2 | 5 | | 3 | 6 | | 4 | 8 | | 5 | 4 | | 6 | 8 | | 7 | 5 | | 8 | 3 | | 9 | 4 | | 10 | 4 | | 11 | 6 | | 12 | 7 | | 13 | 5 | | 14 | 3 | | 15 | 3 | | 16 | 6 | | 17 | 1 | | 18 | 1 | | 19 | 7 | | 20 | 3 | | 21 | 5 | | 22 | 5 | | 23 | 4 | | 24 | 2 | | 25 | 7 | | 26 | 5 | | 27 | 5 | | 28 | 7 | | 29 | 7 | | 30 | 2 | | 31 | 3 | | 32 | 2 | | 33 | 9 | | 34 | 22 | | 35 | 2 | | 36 | 2 | | 37 | 2 | | 38 | 4 | | 39 | 13 | | 40 | 4 | | 41 | 5 | | 42 | 6 | | 43 | 10 | | 44 | 9 | | 45 | 8 | | 46 | 6 | | 47 | 3 | | 48 | 2 | | 49 | 2 |
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| 96.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.7965811965811965 | | totalSentences | 585 | | uniqueOpeners | 466 | |
| 14.06% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 474 | | matches | | 0 | "Then the third." | | 1 | "Then by design." |
| | ratio | 0.004 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 474 | | matches | | 0 | "She dried them on a" | | 1 | "She unlocked the second bolt." | | 2 | "You reek of damp wool," | | 3 | "I asked for shelter." | | 4 | "She kept her stance rooted." | | 5 | "He removed his hat." | | 6 | "She crossed to the kitchenette." | | 7 | "You sound confident." | | 8 | "He moved past her." | | 9 | "You always prepared for betrayal." | | 10 | "We ran out of vocabulary." | | 11 | "She selected the chamomile." | | 12 | "You dealt in information." | | 13 | "I dealt in statutes." | | 14 | "She stirred the tea." | | 15 | "We operated adjacent lanes." | | 16 | "You told me to disappear." | | 17 | "It feels like self-preservation." | | 18 | "She set the mug down." | | 19 | "You sent operatives to monitor" |
| | ratio | 0.063 | |
| 65.49% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 374 | | totalSentences | 474 | | matches | | 0 | "The deadbolt slid back." | | 1 | "Metal scraped against rusted tracks." | | 2 | "Aurora turned from the stove." | | 3 | "Water droplets clung to her" | | 4 | "She dried them on a" | | 5 | "The peephole showed only warped" | | 6 | "She unlocked the second bolt." | | 7 | "Platinum hair plastered to his" | | 8 | "Ivory cane tapped the threshold" | | 9 | "You reek of damp wool," | | 10 | "Lucien stepped forward." | | 11 | "Boots claimed the entryway space." | | 12 | "Floorboards groaned under shifted weight." | | 13 | "I asked for shelter." | | 14 | "Cardiff weather ruins perfectly good" | | 15 | "She kept her stance rooted." | | 16 | "Back foot ready to retreat." | | 17 | "Front hand resting near the" | | 18 | "Marseille salt air." | | 19 | "The exact angle of his" |
| | ratio | 0.789 | |
| 31.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 474 | | matches | | 0 | "To vet my employers." | | 1 | "To intercept anyone approaching my" | | 2 | "Now confirms here." |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 8 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |