| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn walked slowly [slowly]" |
| | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0.143 | | effectiveRatio | 0.074 | |
| 81.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1063 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | adverb | "reluctantly" | | count | 1 |
| | 3 | |
| | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "reluctantly" | | 3 | "carefully" |
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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) | |
| 62.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1063 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "echoing" | | 1 | "quivered" | | 2 | "stark" | | 3 | "navigate" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "trembled" |
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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 | 53 | | matches | (empty) | |
| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1058 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.80% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 772 | | uniqueNames | 10 | | maxNameDensity | 1.68 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Camden | 1 | | Tube | 1 | | London | 3 | | Sergeant | 1 | | Mills | 5 | | Morrison | 4 | | Morris | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Mills" | | 4 | "Morrison" | | 5 | "Morris" | | 6 | "Kowalski" |
| | places | | | globalScore | 0.658 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1058 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 34.13 | | std | 19.89 | | cv | 0.583 | | sampleLengths | | 0 | 68 | | 1 | 63 | | 2 | 30 | | 3 | 46 | | 4 | 45 | | 5 | 50 | | 6 | 5 | | 7 | 39 | | 8 | 51 | | 9 | 13 | | 10 | 10 | | 11 | 59 | | 12 | 4 | | 13 | 23 | | 14 | 38 | | 15 | 5 | | 16 | 16 | | 17 | 33 | | 18 | 41 | | 19 | 50 | | 20 | 4 | | 21 | 30 | | 22 | 30 | | 23 | 9 | | 24 | 49 | | 25 | 73 | | 26 | 45 | | 27 | 12 | | 28 | 46 | | 29 | 49 | | 30 | 22 |
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| 85.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 53 | | matches | | 0 | "being swallowed" | | 1 | "being absorbed" | | 2 | "was connected" |
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| 41.27% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 126 | | matches | | 0 | "was already moving" | | 1 | "were being" | | 2 | "was watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 71 | | ratio | 0.056 | | matches | | 0 | "Strange etchings covered the face—symbols she didn't recognize but that seemed to writhe in the shifting light." | | 1 | "Its needle didn't point north—instead, it quivered toward the far end of the platform where the tunnel mouth gaped like an open wound." | | 2 | "The air flowing from the gap carried scents that didn't belong in any abandoned subway—incense, old parchment, and something else she couldn't identify." | | 3 | "She checked her watch—7:43 AM." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 778 | | adjectiveStacks | 1 | | stackExamples | | 0 | "revealing graffiti-stained walls" |
| | adverbCount | 30 | | adverbRatio | 0.038560411311053984 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.017994858611825194 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 14.9 | | std | 7.75 | | cv | 0.52 | | sampleLengths | | 0 | 27 | | 1 | 19 | | 2 | 22 | | 3 | 28 | | 4 | 20 | | 5 | 15 | | 6 | 18 | | 7 | 12 | | 8 | 9 | | 9 | 9 | | 10 | 20 | | 11 | 8 | | 12 | 27 | | 13 | 18 | | 14 | 10 | | 15 | 9 | | 16 | 14 | | 17 | 17 | | 18 | 5 | | 19 | 12 | | 20 | 27 | | 21 | 6 | | 22 | 23 | | 23 | 22 | | 24 | 13 | | 25 | 7 | | 26 | 3 | | 27 | 27 | | 28 | 32 | | 29 | 4 | | 30 | 12 | | 31 | 11 | | 32 | 11 | | 33 | 15 | | 34 | 12 | | 35 | 5 | | 36 | 3 | | 37 | 13 | | 38 | 16 | | 39 | 17 | | 40 | 11 | | 41 | 18 | | 42 | 12 | | 43 | 10 | | 44 | 17 | | 45 | 23 | | 46 | 4 | | 47 | 5 | | 48 | 16 | | 49 | 9 |
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| 98.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5915492957746479 | | totalSentences | 71 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 53 | | matches | | 0 | "She bent closer, careful not" | | 1 | "Her colleague ambled over, shining" | | 2 | "Its needle didn't point north—instead," | | 3 | "She'd seen enough strange evidence" | | 4 | "she said, standing and directing" | | 5 | "She moved toward the tunnel" | | 6 | "Her torch beam penetrated only" | | 7 | "She was already moving toward" | | 8 | "Her colleague's footsteps approached reluctantly." | | 9 | "She played her torch over" | | 10 | "She glanced back at the" | | 11 | "She checked her watch—7:43 AM." | | 12 | "She needed to move fast." | | 13 | "she said, already planning her" |
| | ratio | 0.264 | |
| 73.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 53 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The beam of her torch" | | 2 | "The body lay crumpled beside" | | 3 | "Blood pooled beneath his head" | | 4 | "Quinn crouched beside the corpse," | | 5 | "Detective Sergeant Mills appeared from" | | 6 | "Quinn's fingers hovered over the" | | 7 | "Designer wool, barely a wrinkle" | | 8 | "Mills pulled out his notebook," | | 9 | "Something metallic caught Quinn's torchlight" | | 10 | "She bent closer, careful not" | | 11 | "A small brass compass lay" | | 12 | "Her colleague ambled over, shining" | | 13 | "Quinn studied the compass more" | | 14 | "Its needle didn't point north—instead," | | 15 | "She'd seen enough strange evidence" | | 16 | "she said, standing and directing" | | 17 | "Mills squinted at Morrison's expensive" | | 18 | "Quinn walked slowly around the" | | 19 | "Quinn gestured toward the victim's" |
| | ratio | 0.774 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 37.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 5 | | matches | | 0 | "The body lay crumpled beside the defunct ticket booth, a young man in expensive clothes that seemed wildly out of place in this forgotten corner of London's und…" | | 1 | "Quinn's footsteps echoed strangely, as if the sound were being absorbed and reflected by surfaces that shouldn't exist." | | 2 | "The air flowing from the gap carried scents that didn't belong in any abandoned subway—incense, old parchment, and something else she couldn't identify." | | 3 | "But Quinn remembered the strange symbols carved into the warehouse wall, the witnesses who spoke of shadows moving independently of their owners." | | 4 | "In a few hours, the crime scene would be crawling with technicians and photographers, all of whom would focus on the obvious evidence while missing the patterns…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn walked slowly, her torch painting stark shadows on the curved walls" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |