| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1257 | | 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) | |
| 16.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1257 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | word | "moths to flame" | | count | 1 |
| | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "fractured" | | 1 | "navigate" | | 2 | "quickened" | | 3 | "lurched" | | 4 | "flickered" | | 5 | "glinting" | | 6 | "charm" | | 7 | "unreadable" | | 8 | "weight" | | 9 | "loomed" | | 10 | "pulse" | | 11 | "footsteps" | | 12 | "echoed" | | 13 | "trembled" | | 14 | "silence" | | 15 | "moths to flame" | | 16 | "raced" | | 17 | "echo" | | 18 | "glint" |
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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 | 97 | | matches | (empty) | |
| 98.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 33 | | totalWords | 1246 | | ratio | 0.026 | | matches | | 0 | "Keep moving. Stay sharp." | | 1 | "Not normal." | | 2 | "Before it meant nothing." | | 3 | "Herrera" | | 4 | "Unexplained circumstances. Something they didn’t understand." | | 5 | "He’s not just hiding. He’s hurt." | | 6 | "not official infrastructure" | | 7 | "unauthorized administrations, patient files with impossible injuries." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1151 | | uniqueNames | 20 | | maxNameDensity | 1.39 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Wardour | 1 | | Street | 1 | | Raven | 1 | | Nest | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 4 | | Tomás | 8 | | Spanish | 1 | | Soho | 1 | | Tube | 1 | | Footsteps | 1 | | Camden | 1 | | Town | 1 | | Veil | 1 | | Market | 3 | | Warm | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Tomás" | | 9 | "Footsteps" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Tube" | | 4 | "Camden" | | 5 | "Town" | | 6 | "Market" |
| | globalScore | 0.805 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 1 | | matches | | 0 | "as if testing the depth of the water underfoot" |
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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 | 1246 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 59.33 | | std | 34.06 | | cv | 0.574 | | sampleLengths | | 0 | 128 | | 1 | 108 | | 2 | 118 | | 3 | 69 | | 4 | 18 | | 5 | 55 | | 6 | 43 | | 7 | 88 | | 8 | 6 | | 9 | 84 | | 10 | 85 | | 11 | 75 | | 12 | 72 | | 13 | 51 | | 14 | 29 | | 15 | 35 | | 16 | 61 | | 17 | 22 | | 18 | 31 | | 19 | 60 | | 20 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 97 | | matches | | |
| 35.80% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 203 | | matches | | 0 | "was heading" | | 1 | "was hobbling" | | 2 | "was weaving" | | 3 | "was haggling" | | 4 | "was thrumming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 101 | | ratio | 0.089 | | matches | | 0 | "She didn’t recognize the man ahead—lean, in a drenched olive-green jacket, a smear of blood blooming on his sleeve—but his hurried gait mirrored the same jittery urgency she’d seen in the other members of the clique that had consumed her case." | | 1 | "A glimpse of a flash—something silver—caught her eye where he’d leaned against the wall to catch his breath." | | 2 | "The bar’s door was ajar, and she pressed herself into the shadows, drawing her watch back to her mouth for a quick puff of bourbon—leftover vial in the compartment, a habit from late-night stakeouts." | | 3 | "Quinn’s partner—Morris—had once worn the same kind of charm." | | 4 | "His eyes met hers—warm brown, unreadable—and he tilted his head." | | 5 | "Quinn crouched, catching the faint glow from a station—Camden Town’s abandoned platform long since sealed off, but someone had dug in here." | | 6 | "Her flashlight skipped over damp brick—*not official infrastructure*—until the air thickened with smoke and something sweeter, like charred amber." | | 7 | "“Your treatments didn’t work before.” Quinn’s voice was steady, but her mind raced through the dossier—*unauthorized administrations, patient files with impossible injuries.* Tomás didn’t answer." | | 8 | "Quinn felt the weight of hers—hidden in her jacket pocket, stolen from a crime scene last week." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1174 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.019591141396933562 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0034071550255536627 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.34 | | std | 7.18 | | cv | 0.582 | | sampleLengths | | 0 | 24 | | 1 | 22 | | 2 | 41 | | 3 | 18 | | 4 | 19 | | 5 | 2 | | 6 | 2 | | 7 | 13 | | 8 | 20 | | 9 | 12 | | 10 | 18 | | 11 | 4 | | 12 | 9 | | 13 | 16 | | 14 | 16 | | 15 | 7 | | 16 | 23 | | 17 | 34 | | 18 | 7 | | 19 | 17 | | 20 | 17 | | 21 | 9 | | 22 | 4 | | 23 | 8 | | 24 | 26 | | 25 | 11 | | 26 | 14 | | 27 | 10 | | 28 | 10 | | 29 | 8 | | 30 | 20 | | 31 | 16 | | 32 | 4 | | 33 | 15 | | 34 | 14 | | 35 | 18 | | 36 | 11 | | 37 | 2 | | 38 | 14 | | 39 | 2 | | 40 | 9 | | 41 | 14 | | 42 | 16 | | 43 | 12 | | 44 | 19 | | 45 | 6 | | 46 | 14 | | 47 | 28 | | 48 | 5 | | 49 | 3 |
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| 47.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3069306930693069 | | totalSentences | 101 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 95.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 90 | | matches | | 0 | "She didn’t recognize the man" | | 1 | "He’d taken a wrong turn" | | 2 | "She quickened her pace." | | 3 | "He was heading for The" | | 4 | "Her focus snapped back to" | | 5 | "He said something to the" | | 6 | "He didn’t look like someone" | | 7 | "she said, her voice dry" | | 8 | "His eyes met hers—warm brown," | | 9 | "He nodded toward the door" | | 10 | "She kicked it open, not" | | 11 | "He’d gone back into the" | | 12 | "Her pulse flickered." | | 13 | "*He’s not just hiding." | | 14 | "He pressed a bone-shaped key" | | 15 | "She counted backward." | | 16 | "He didn’t look at her," | | 17 | "Her flashlight skipped over damp" | | 18 | "She gripped her watch tighter," | | 19 | "She stepped forward, but a" |
| | ratio | 0.311 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 90 | | matches | | 0 | "Detective Harlow Quinn’s boots slammed" | | 1 | "The rain had started as" | | 2 | "She didn’t recognize the man" | | 3 | "He’d taken a wrong turn" | | 4 | "Quinn’s fingers brushed the worn" | | 5 | "The suspect ducked into an" | | 6 | "Quinn followed, her military-cut frame" | | 7 | "The alley reeked of mildew" | | 8 | "A glimpse of a flash—something" | | 9 | "She quickened her pace." | | 10 | "A gash there, the blood" | | 11 | "He was heading for The" | | 12 | "Quinn slowed her step as" | | 13 | "The bar’s door was ajar," | | 14 | "Her focus snapped back to" | | 15 | "Tomás Herrera emerged from the" | | 16 | "He said something to the" | | 17 | "Quinn’s partner—Morris—had once worn the" | | 18 | "The suspect reappeared moments later," | | 19 | "He didn’t look like someone" |
| | ratio | 0.9 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "*Before it meant nothing.*" |
| | ratio | 0.011 | |
| 88.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 4 | | matches | | 0 | "She didn’t recognize the man ahead—lean, in a drenched olive-green jacket, a smear of blood blooming on his sleeve—but his hurried gait mirrored the same jitter…" | | 1 | "The suspect ducked into an alley without slowing, his silhouette vanishing into shadows." | | 2 | "Quinn slowed her step as she neared the entrance, watching him slip beneath the green neon sign that flickered like a dying heartbeat." | | 3 | "The suspect was weaving through the crowd now, their faces turning toward him like moths to flame." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "Tomás shrugged, his accent thickening in the rain" | | 1 | "She reached, fingers brushing the bone token the suspect had used" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0 | | effectiveRatio | 0 | |