| 53.66% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 3 | | adverbTags | | 0 | "She paused then [then]" | | 1 | "she said flatly [flatly]" | | 2 | "she said slowly [slowly]" |
| | dialogueSentences | 41 | | tagDensity | 0.488 | | leniency | 0.976 | | rawRatio | 0.15 | | effectiveRatio | 0.146 | |
| 91.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1242 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | |
| | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 43.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1242 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "echoing" | | 2 | "silence" | | 3 | "etched" | | 4 | "standard" | | 5 | "furrowed" | | 6 | "quivered" | | 7 | "warmth" | | 8 | "pulse" | | 9 | "quickened" | | 10 | "grave" | | 11 | "whisper" | | 12 | "familiar" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "The air was thick with" | | 2 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 75 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 16 | | markdownWords | 25 | | totalWords | 1234 | | ratio | 0.02 | | matches | | 0 | "classified" | | 1 | "The Veil Market." | | 2 | "HE ALWAYS KNOWS." | | 3 | "moving" | | 4 | "rift" | | 5 | "his" | | 6 | "researcher" | | 7 | "moves" | | 8 | "twitching" | | 9 | "alive" | | 10 | "use" | | 11 | "HE ALWAYS KNOWS." | | 12 | "something" | | 13 | "RUN." | | 14 | "He always knows." | | 15 | "YOU’RE NEXT." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 841 | | uniqueNames | 13 | | maxNameDensity | 2.14 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 18 | | Kowalski | 1 | | Veil | 2 | | Eva | 10 | | Morris | 2 | | Camden | 1 | | Whitechapel | 1 | | Market | 2 | | Bethnal | 1 | | Green | 1 | | Marcus | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Marcus" |
| | places | | | globalScore | 0.43 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it before, in a case file mar" |
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| 37.93% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.621 | | wordCount | 1234 | | matches | | 0 | "no longer pointing north but" | | 1 | "not a believer,” Quinn interrupted, “but I’m not a fool" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 38.56 | | std | 23.65 | | cv | 0.613 | | sampleLengths | | 0 | 91 | | 1 | 52 | | 2 | 70 | | 3 | 36 | | 4 | 34 | | 5 | 57 | | 6 | 36 | | 7 | 63 | | 8 | 8 | | 9 | 57 | | 10 | 23 | | 11 | 46 | | 12 | 18 | | 13 | 37 | | 14 | 82 | | 15 | 22 | | 16 | 60 | | 17 | 10 | | 18 | 4 | | 19 | 57 | | 20 | 45 | | 21 | 17 | | 22 | 40 | | 23 | 44 | | 24 | 50 | | 25 | 9 | | 26 | 8 | | 27 | 53 | | 28 | 8 | | 29 | 26 | | 30 | 69 | | 31 | 2 |
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| 86.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 75 | | matches | | 0 | "been identified" | | 1 | "was singed" | | 2 | "been positioned" | | 3 | "was gone" |
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| 70.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 155 | | matches | | 0 | "was already cataloging" | | 1 | "was holding" | | 2 | "were climbing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 95 | | ratio | 0.074 | | matches | | 0 | "The air was thick with the scent of damp earth and something else—something metallic and faintly sweet, like old pennies left too long in a child’s pocket." | | 1 | "The victim—a young man in his twenties, based on the clothes—hadn’t been identified yet." | | 2 | "The body had been positioned deliberately, she noticed—a message scrawled in the dirt with chalk, the letters jagged and hurried: *HE ALWAYS KNOWS.* She frowned." | | 3 | "She’d felt it before—on DS Morris’s last case, the night the warehouse exploded." | | 4 | "Quinn’s eyes drifted to the message on the ground: *HE ALWAYS KNOWS.* Whoever wrote it had believed someone—*something*—knew too much." | | 5 | "The compass needle jerked, spinning wildly before settling on a new direction—one that pointed directly at the far end of the platform, where the tunnel walls curved into darkness." | | 6 | "But as they turned toward the exit, Quinn heard it—the faintest whisper, so close to her ear it might have been the wind." |
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| 94.81% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 849 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.045936395759717315 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.016489988221436984 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 12.99 | | std | 9.21 | | cv | 0.709 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 27 | | 3 | 6 | | 4 | 20 | | 5 | 13 | | 6 | 24 | | 7 | 13 | | 8 | 2 | | 9 | 12 | | 10 | 13 | | 11 | 14 | | 12 | 15 | | 13 | 16 | | 14 | 2 | | 15 | 13 | | 16 | 18 | | 17 | 3 | | 18 | 10 | | 19 | 14 | | 20 | 10 | | 21 | 18 | | 22 | 25 | | 23 | 14 | | 24 | 9 | | 25 | 25 | | 26 | 2 | | 27 | 9 | | 28 | 31 | | 29 | 7 | | 30 | 16 | | 31 | 3 | | 32 | 5 | | 33 | 11 | | 34 | 30 | | 35 | 16 | | 36 | 3 | | 37 | 20 | | 38 | 6 | | 39 | 40 | | 40 | 2 | | 41 | 16 | | 42 | 3 | | 43 | 24 | | 44 | 10 | | 45 | 13 | | 46 | 3 | | 47 | 13 | | 48 | 12 | | 49 | 29 |
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| 54.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3368421052631579 | | totalSentences | 95 | | uniqueOpeners | 32 | |
| 50.51% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 66 | | matches | | 0 | "Indeed, the needle quivered, no" |
| | ratio | 0.015 | |
| 80.61% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 66 | | matches | | 0 | "She paused, glancing at her" | | 1 | "She stood a few paces" | | 2 | "Her satchel slung over one" | | 3 | "His throat had been slit" | | 4 | "She’d seen something like it" | | 5 | "Her voice carried a tremor" | | 6 | "She jabbed a finger at" | | 7 | "She paused, then added" | | 8 | "She trailed off, her gaze" | | 9 | "She’d felt it before—on DS" | | 10 | "She’d blamed the explosion on" | | 11 | "She’d felt warmth like this" | | 12 | "Her superiors had called it" | | 13 | "she said flatly" | | 14 | "she said, voice low" | | 15 | "Her colleague nodded rapidly, her" | | 16 | "She trailed off, staring at" | | 17 | "She’d seen those symbols before," | | 18 | "she said slowly" | | 19 | "She looked at Eva, her" |
| | ratio | 0.348 | |
| 20.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 66 | | matches | | 0 | "The fluorescent lights above flickered" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The air was thick with" | | 3 | "She paused, glancing at her" | | 4 | "The time read 2:17 a.m.," | | 5 | "Eva Kowalski’s voice cut through" | | 6 | "She stood a few paces" | | 7 | "Her satchel slung over one" | | 8 | "Quinn’s jaw tightened as she" | | 9 | "A man lay crumpled near" | | 10 | "The victim—a young man in" | | 11 | "His throat had been slit" | | 12 | "Brass, tarnished green with age," | | 13 | "She’d seen something like it" | | 14 | "*The Veil Market.*" | | 15 | "Quinn asked, crouching to examine" | | 16 | "The man’s shirt was singed" | | 17 | "Eva stepped closer, her freckled" | | 18 | "Her voice carried a tremor" | | 19 | "Quinn nodded, but her mind" |
| | ratio | 0.879 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "The time read 2:17 a.m., but the darkness felt impossibly thick, as if the station had swallowed the moon whole." | | 1 | "But it was the object beside him that made Quinn’s hand instinctively drift to her holster." | | 2 | "She’d blamed the explosion on faulty wiring, on corporate negligence, on a thousand things that didn’t involve shadows in the corners of rooms or compasses that…" | | 3 | "Someone who could trace the Veil Market’s movements, who understood its rules." |
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| 50.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn asked, crouching to examine the body" | | 1 | "She trailed, her gaze drifting to the compass" | | 2 | "she said, voice low" |
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| 76.83% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "Eva breathed (breathe)" | | 1 | "Quinn interrupted (interrupt)" | | 2 | "Quinn interrupted (interrupt)" |
| | dialogueSentences | 41 | | tagDensity | 0.22 | | leniency | 0.439 | | rawRatio | 0.333 | | effectiveRatio | 0.146 | |