| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1245 | | 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) | |
| 47.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1245 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "streaming" | | 2 | "maw" | | 3 | "vibrated" | | 4 | "depths" | | 5 | "mechanical" | | 6 | "weight" | | 7 | "etched" | | 8 | "whisper" | | 9 | "racing" | | 10 | "aligned" | | 11 | "could feel" |
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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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1245 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1191 | | uniqueNames | 12 | | maxNameDensity | 1.51 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 18 | | Northern | 1 | | Line | 1 | | Morris | 4 | | Veil | 2 | | Market | 2 | | Camden | 2 | | Swiss | 1 | | Cottage | 1 | | Met | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Line" | | 3 | "Morris" | | 4 | "Market" |
| | places | | | globalScore | 0.744 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "spirals that seemed to shift in the strobe flashes of distant neon" |
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| 39.36% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.606 | | wordCount | 1245 | | matches | | 0 | "not darkness, but a yawning maw of damp stairwel1ing down into blackness" | | 1 | "Not the cold of the rain, but the absolute zero" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 137 | | matches | | 0 | "seen that vibration" | | 1 | "like that warehouse" |
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| 95.87% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 44.46 | | std | 21.59 | | cv | 0.486 | | sampleLengths | | 0 | 70 | | 1 | 61 | | 2 | 2 | | 3 | 50 | | 4 | 78 | | 5 | 43 | | 6 | 56 | | 7 | 31 | | 8 | 25 | | 9 | 78 | | 10 | 10 | | 11 | 61 | | 12 | 14 | | 13 | 36 | | 14 | 34 | | 15 | 57 | | 16 | 25 | | 17 | 72 | | 18 | 53 | | 19 | 64 | | 20 | 53 | | 21 | 7 | | 22 | 24 | | 23 | 65 | | 24 | 26 | | 25 | 60 | | 26 | 37 | | 27 | 53 |
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| 97.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 131 | | matches | | 0 | "been shot" | | 1 | "was redacted" | | 2 | "was scrubbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 220 | | matches | | 0 | "was diving" | | 1 | "wasn't running" |
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| 80.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 137 | | ratio | 0.022 | | matches | | 0 | "She checked her leather watch without glancing; three minutes to intercept." | | 1 | "He wasn't running from her; he was diving for the hole." | | 2 | "The Veil Market moved; it hid from those who didn't belong." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 559 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.007155635062611807 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 9.09 | | std | 5.49 | | cv | 0.605 | | sampleLengths | | 0 | 12 | | 1 | 18 | | 2 | 20 | | 3 | 20 | | 4 | 4 | | 5 | 16 | | 6 | 12 | | 7 | 11 | | 8 | 18 | | 9 | 2 | | 10 | 13 | | 11 | 5 | | 12 | 8 | | 13 | 24 | | 14 | 4 | | 15 | 14 | | 16 | 8 | | 17 | 15 | | 18 | 6 | | 19 | 10 | | 20 | 8 | | 21 | 13 | | 22 | 17 | | 23 | 8 | | 24 | 4 | | 25 | 14 | | 26 | 4 | | 27 | 11 | | 28 | 2 | | 29 | 19 | | 30 | 5 | | 31 | 15 | | 32 | 8 | | 33 | 11 | | 34 | 7 | | 35 | 5 | | 36 | 8 | | 37 | 6 | | 38 | 6 | | 39 | 5 | | 40 | 3 | | 41 | 26 | | 42 | 6 | | 43 | 4 | | 44 | 5 | | 45 | 21 | | 46 | 13 | | 47 | 8 | | 48 | 2 | | 49 | 7 |
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| 37.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2773722627737226 | | totalSentences | 137 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 93.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 123 | | matches | | 0 | "She vaulted the bin in" | | 1 | "She checked her leather watch" | | 2 | "She took the knife-edge of" | | 3 | "Her heel caught a fissure" | | 4 | "She rolled through the impact," | | 5 | "He threw himself at the" | | 6 | "She lunged, her fingers closing" | | 7 | "She slammed him against the" | | 8 | "She pressed her hip hard" | | 9 | "His voice dropped an octave," | | 10 | "She pried the suspect's fingers" | | 11 | "His pocket was empty." | | 12 | "She'd seen that vibration in" | | 13 | "He wasn't running from her;" | | 14 | "She dragged him back, but" | | 15 | "Her vision swam." | | 16 | "She lunged at the gate," | | 17 | "She slammed her palm against" | | 18 | "Her gaze snagged on the" | | 19 | "She stared at the spirals." |
| | ratio | 0.317 | |
| 12.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 123 | | matches | | 0 | "Detective Harlow Quinn's boots struck" | | 1 | "The target moved with a" | | 2 | "Quinn didn't break stride." | | 3 | "She vaulted the bin in" | | 4 | "Salt-and-pepper hair plastered to her" | | 5 | "She checked her leather watch" | | 6 | "The suspect veered left, sprinting" | | 7 | "The words tore from her" | | 8 | "The figure hesitated, glancing back." | | 9 | "Quinn saw the widening of" | | 10 | "The suspect clutched a satchel" | | 11 | "Quinn cut the angle." | | 12 | "She took the knife-edge of" | | 13 | "Her heel caught a fissure" | | 14 | "She rolled through the impact," | | 15 | "Military discipline kept the pain" | | 16 | "This kid, this courier with" | | 17 | "The alley dead-ended at a" | | 18 | "An iron gate, rusted solid," | | 19 | "The suspect didn't slow." |
| | ratio | 0.894 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "She stepped up to the threshold, her boot hovering over the rusted grating of the first step." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn snarled (snarl)" | | 1 | "he wheezed (wheeze)" | | 2 | "Quinn ordered (order)" |
| | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 1 | | effectiveRatio | 0.6 | |