| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 601 | | 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) | |
| 41.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 601 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "flickered" | | 3 | "echoes" | | 4 | "standard" | | 5 | "familiar" | | 6 | "glinting" |
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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 | 43 | | matches | (empty) | |
| 76.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 43 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 605 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 580 | | uniqueNames | 10 | | maxNameDensity | 1.72 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 10 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Camden | 1 | | Town | 1 | | Morris | 2 | | Herrera | 3 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Camden" | | 4 | "Town" |
| | globalScore | 0.638 | | windowScore | 0.833 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like bone, worn smooth from handli" |
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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 | 605 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 71.56% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 37.81 | | std | 15.14 | | cv | 0.4 | | sampleLengths | | 0 | 47 | | 1 | 43 | | 2 | 16 | | 3 | 50 | | 4 | 47 | | 5 | 47 | | 6 | 33 | | 7 | 44 | | 8 | 51 | | 9 | 58 | | 10 | 15 | | 11 | 53 | | 12 | 20 | | 13 | 21 | | 14 | 12 | | 15 | 48 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 43 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 106 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 45 | | ratio | 0.089 | | matches | | 0 | "The figure glanced back – just a flash of pale skin in the darkness – before ducking into a narrow alley." | | 1 | "The stale underground air carried unfamiliar scents – incense, exotic spices, something metallic that made her nose twitch." | | 2 | "Voices drifted from further along the platform – not the usual echoes of maintenance workers or urban explorers." | | 3 | "Following her suspect meant stepping into whatever this was – an underground network that might have answers about Morris, but could just as easily get her killed." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 576 | | adjectiveStacks | 1 | | stackExamples | | 0 | "open, revealing concrete stairs" |
| | adverbCount | 18 | | adverbRatio | 0.03125 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.013888888888888888 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 81.03% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 13.44 | | std | 4.74 | | cv | 0.353 | | sampleLengths | | 0 | 16 | | 1 | 14 | | 2 | 17 | | 3 | 11 | | 4 | 11 | | 5 | 21 | | 6 | 16 | | 7 | 12 | | 8 | 15 | | 9 | 13 | | 10 | 10 | | 11 | 5 | | 12 | 18 | | 13 | 19 | | 14 | 5 | | 15 | 10 | | 16 | 7 | | 17 | 15 | | 18 | 15 | | 19 | 18 | | 20 | 15 | | 21 | 20 | | 22 | 12 | | 23 | 12 | | 24 | 8 | | 25 | 12 | | 26 | 22 | | 27 | 9 | | 28 | 10 | | 29 | 14 | | 30 | 13 | | 31 | 21 | | 32 | 10 | | 33 | 5 | | 34 | 10 | | 35 | 27 | | 36 | 16 | | 37 | 8 | | 38 | 12 | | 39 | 14 | | 40 | 7 | | 41 | 12 | | 42 | 15 | | 43 | 16 | | 44 | 17 |
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| 74.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.4444444444444444 | | totalSentences | 45 | | uniqueOpeners | 20 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 99.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 43 | | matches | | 0 | "Her worn leather watch thumped" | | 1 | "She drew her torch, beam" | | 2 | "Her instincts screamed danger, but" | | 3 | "They usually led to answers." | | 4 | "She started down the stairs," | | 5 | "Her footsteps echoed in the" | | 6 | "She'd assumed it was evidence" | | 7 | "Her suspect weaved through the" | | 8 | "He worked his way between" | | 9 | "Her military training screamed at" | | 10 | "They'd shut this place down" | | 11 | "She pulled out the token," | | 12 | "Her suspect was nearly at" |
| | ratio | 0.302 | |
| 6.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 43 | | matches | | 0 | "Detective Quinn's boots splashed through" | | 1 | "Rain pelted her face, blurring" | | 2 | "The suspect darted between late-night" | | 3 | "Quinn's breath came in controlled" | | 4 | "Her worn leather watch thumped" | | 5 | "The figure glanced back –" | | 6 | "Quinn's voice carried over the" | | 7 | "The alley opened onto a" | | 8 | "Quinn's target disappeared behind a" | | 9 | "She drew her torch, beam" | | 10 | "The door hung open, revealing" | | 11 | "Quinn paused at the threshold." | | 12 | "The stale underground air carried" | | 13 | "Her instincts screamed danger, but" | | 14 | "They usually led to answers." | | 15 | "She started down the stairs," | | 16 | "Her footsteps echoed in the" | | 17 | "The temperature dropped with each" | | 18 | "Voices drifted from further along" | | 19 | "These spoke in languages she" |
| | ratio | 0.907 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "The suspect darted between late-night pedestrians, knocking over a rubbish bin that clattered across the wet pavement." | | 1 | "Quinn's target disappeared behind a maintenance door that led to the old Camden Town station." | | 2 | "Following her suspect meant stepping into whatever this was – an underground network that might have answers about Morris, but could just as easily get her kill…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |