| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1247 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "completely" | | 1 | "sharply" | | 2 | "precisely" |
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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) | |
| 31.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1247 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "shattered" | | 2 | "tension" | | 3 | "tracing" | | 4 | "fractured" | | 5 | "chill" | | 6 | "standard" | | 7 | "crystalline" | | 8 | "pulsed" | | 9 | "familiar" | | 10 | "output" | | 11 | "footsteps" | | 12 | "echoed" | | 13 | "solitary" | | 14 | "gloom" |
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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 | 63 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | 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 | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1247 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.36% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 887 | | uniqueNames | 10 | | maxNameDensity | 1.35 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Northern | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 12 | | Detective | 2 | | Constable | 1 | | Miller | 12 | | Victorian | 1 | | Morris | 2 | | Shoreditch | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Constable" | | 4 | "Miller" | | 5 | "Morris" |
| | places | | | globalScore | 0.824 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.802 | | wordCount | 1247 | | matches | | 0 | "neither scorch marks nor" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 21.88 | | std | 17.58 | | cv | 0.803 | | sampleLengths | | 0 | 22 | | 1 | 53 | | 2 | 41 | | 3 | 4 | | 4 | 18 | | 5 | 80 | | 6 | 8 | | 7 | 8 | | 8 | 4 | | 9 | 3 | | 10 | 12 | | 11 | 37 | | 12 | 25 | | 13 | 10 | | 14 | 6 | | 15 | 63 | | 16 | 27 | | 17 | 4 | | 18 | 9 | | 19 | 21 | | 20 | 25 | | 21 | 31 | | 22 | 33 | | 23 | 14 | | 24 | 13 | | 25 | 25 | | 26 | 8 | | 27 | 13 | | 28 | 16 | | 29 | 60 | | 30 | 13 | | 31 | 10 | | 32 | 14 | | 33 | 39 | | 34 | 48 | | 35 | 9 | | 36 | 10 | | 37 | 4 | | 38 | 29 | | 39 | 23 | | 40 | 39 | | 41 | 9 | | 42 | 62 | | 43 | 41 | | 44 | 19 | | 45 | 26 | | 46 | 7 | | 47 | 4 | | 48 | 12 | | 49 | 9 |
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| 88.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 63 | | matches | | 0 | "been carved" | | 1 | "been stretched" | | 2 | "been dropped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 128 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 88 | | ratio | 0.011 | | matches | | 0 | "The links were not clipped by bolt cutters; the steel loops had been stretched until they snapped under tension, the metal necked down to needle-fine points." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 899 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.012235817575083427 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00778642936596218 | |
| 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 | 14.17 | | std | 7.22 | | cv | 0.509 | | sampleLengths | | 0 | 22 | | 1 | 11 | | 2 | 15 | | 3 | 10 | | 4 | 17 | | 5 | 14 | | 6 | 27 | | 7 | 4 | | 8 | 18 | | 9 | 10 | | 10 | 10 | | 11 | 23 | | 12 | 20 | | 13 | 17 | | 14 | 8 | | 15 | 8 | | 16 | 4 | | 17 | 3 | | 18 | 12 | | 19 | 37 | | 20 | 15 | | 21 | 10 | | 22 | 10 | | 23 | 6 | | 24 | 7 | | 25 | 16 | | 26 | 12 | | 27 | 28 | | 28 | 8 | | 29 | 19 | | 30 | 4 | | 31 | 9 | | 32 | 21 | | 33 | 18 | | 34 | 7 | | 35 | 31 | | 36 | 10 | | 37 | 23 | | 38 | 14 | | 39 | 13 | | 40 | 25 | | 41 | 8 | | 42 | 13 | | 43 | 16 | | 44 | 10 | | 45 | 22 | | 46 | 9 | | 47 | 19 | | 48 | 13 | | 49 | 10 |
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| 76.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.48863636363636365 | | totalSentences | 88 | | uniqueOpeners | 43 | |
| 52.91% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 63 | | matches | | 0 | "Instead, the weave ended in" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 63 | | matches | | 0 | "Her boots crunched on century-old" | | 1 | "She glanced at the worn" | | 2 | "Her brown eyes swept the" | | 3 | "His spine curved upward, supported" | | 4 | "She hovered her palm over" | | 5 | "She stood, sweeping her gaze" | | 6 | "Her boot stopped beside an" | | 7 | "She crouched again, bringing her" | | 8 | "She turned toward the mouth" | | 9 | "She took a high-output lantern" | | 10 | "She touched the metal residue" | | 11 | "She pointed to the track" |
| | ratio | 0.19 | |
| 39.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 63 | | matches | | 0 | "Water dripped from the vaulted" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "Halogen floodlights threw hard white" | | 3 | "Her boots crunched on century-old" | | 4 | "She glanced at the worn" | | 5 | "Miller clicked his pen." | | 6 | "Quinn crouched beside the body" | | 7 | "Her brown eyes swept the" | | 8 | "The dead man wore an" | | 9 | "His spine curved upward, supported" | | 10 | "Rigor mortis had set in" | | 11 | "Miller shone his torch down" | | 12 | "Quinn gestured with a gloved" | | 13 | "Miller tilted his head back," | | 14 | "Quinn leaned closer to the" | | 15 | "The man’s jacket was split" | | 16 | "The edges of the wool" | | 17 | "She hovered her palm over" | | 18 | "The flesh was chalk-white, drained" | | 19 | "Miller pointed to the crushed" |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 1 | | matches | | 0 | "Instead, the weave ended in a translucent, glassy bead along the seam, as though high heat had cauterized the fiber in a microsecond without heating the adjacen…" |
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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 | |