| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 29 | | tagDensity | 0.241 | | leniency | 0.483 | | rawRatio | 0.143 | | effectiveRatio | 0.069 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 959 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 27.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 959 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "familiar" | | 2 | "scanning" | | 3 | "pulse" | | 4 | "whisper" | | 5 | "etched" | | 6 | "trembled" | | 7 | "flicked" | | 8 | "racing" | | 9 | "stomach" | | 10 | "database" | | 11 | "echoing" | | 12 | "unreadable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 951 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 761 | | uniqueNames | 14 | | maxNameDensity | 1.58 | | worstName | "Cole" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Cole" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 8 | | Camden | 1 | | Town | 1 | | Detective | 2 | | Sergeant | 1 | | Cole | 12 | | Italian | 1 | | Morris | 3 | | British | 1 | | Museum | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Cole" | | 4 | "Morris" | | 5 | "Market" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "British" | | 3 | "Veil" |
| | globalScore | 0.712 | | windowScore | 0.833 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd been *digging* at someth" | | 1 | "quite human traded in things that weren't quite legal" |
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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 | 951 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 27.97 | | std | 21.75 | | cv | 0.778 | | sampleLengths | | 0 | 80 | | 1 | 39 | | 2 | 7 | | 3 | 47 | | 4 | 58 | | 5 | 4 | | 6 | 42 | | 7 | 6 | | 8 | 41 | | 9 | 19 | | 10 | 61 | | 11 | 14 | | 12 | 33 | | 13 | 8 | | 14 | 13 | | 15 | 18 | | 16 | 67 | | 17 | 17 | | 18 | 22 | | 19 | 56 | | 20 | 9 | | 21 | 10 | | 22 | 62 | | 23 | 60 | | 24 | 8 | | 25 | 35 | | 26 | 11 | | 27 | 36 | | 28 | 5 | | 29 | 21 | | 30 | 18 | | 31 | 9 | | 32 | 10 | | 33 | 5 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 82 | | matches | | 0 | "was frozen" | | 1 | "been stretched" | | 2 | "been *sucked" | | 3 | "was torn" | | 4 | "been caught" | | 5 | "been obsessed" |
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| 93.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 125 | | matches | | 0 | "were trying" | | 1 | "was going" |
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| 30.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 102 | | ratio | 0.039 | | matches | | 0 | "His clothes were expensive—tailored wool coat, Italian leather shoes—but his hands were raw, knuckles split like he'd been clawing at something." | | 1 | "The usual detritus of a Tube station was absent—no discarded tickets, no chewing gum stains, no lost gloves." | | 2 | "She stepped closer to the tunnel, her pulse kicking up a notch as she caught the faintest whisper of something—like wind through a crack in a window." | | 3 | "She thought of the stories Morris used to tell her—whispers of a black market hidden beneath the city, a place where things that weren't quite human traded in things that weren't quite legal." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 767 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.035202086049543675 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005215123859191656 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 9.32 | | std | 7.04 | | cv | 0.755 | | sampleLengths | | 0 | 26 | | 1 | 23 | | 2 | 16 | | 3 | 15 | | 4 | 18 | | 5 | 17 | | 6 | 4 | | 7 | 4 | | 8 | 3 | | 9 | 20 | | 10 | 22 | | 11 | 5 | | 12 | 14 | | 13 | 21 | | 14 | 21 | | 15 | 2 | | 16 | 4 | | 17 | 13 | | 18 | 16 | | 19 | 6 | | 20 | 2 | | 21 | 1 | | 22 | 4 | | 23 | 6 | | 24 | 5 | | 25 | 18 | | 26 | 15 | | 27 | 3 | | 28 | 2 | | 29 | 17 | | 30 | 5 | | 31 | 8 | | 32 | 4 | | 33 | 10 | | 34 | 27 | | 35 | 5 | | 36 | 2 | | 37 | 6 | | 38 | 8 | | 39 | 3 | | 40 | 21 | | 41 | 9 | | 42 | 3 | | 43 | 5 | | 44 | 10 | | 45 | 3 | | 46 | 9 | | 47 | 9 | | 48 | 4 | | 49 | 6 |
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| 50.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3627450980392157 | | totalSentences | 102 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 70 | | matches | | 0 | "Just more of that ash," | | 1 | "Too many blind spots." | | 2 | "Too many ways for things" | | 3 | "Instead, she reached into her" |
| | ratio | 0.057 | |
| 88.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 70 | | matches | | 0 | "Her leather watch creaked as" | | 1 | "She ignored the jab." | | 2 | "He hesitated, rubbing the back" | | 3 | "His clothes were expensive—tailored wool" | | 4 | "She rubbed it between her" | | 5 | "She stood, scanning the platform." | | 6 | "She stepped closer to the" | | 7 | "She didn't answer." | | 8 | "She snapped the compass shut" | | 9 | "It wasn't a question." | | 10 | "She forced herself to focus." | | 11 | "She thought of the stories" | | 12 | "She thought of the bone" | | 13 | "She cut him off with" | | 14 | "She turned back to the" | | 15 | "They didn't leave trails of" | | 16 | "She crouched again, this time" | | 17 | "She peeled it back, revealing" | | 18 | "She'd seen that symbol before." | | 19 | "She stood abruptly, her military" |
| | ratio | 0.329 | |
| 52.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 70 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The overhead lights flickered, casting" | | 3 | "Her leather watch creaked as" | | 4 | "The voice came from the" | | 5 | "Detective Sergeant Cole stood near" | | 6 | "She ignored the jab." | | 7 | "Cole jerked his chin toward" | | 8 | "He hesitated, rubbing the back" | | 9 | "Quinn crouched beside the body," | | 10 | "The victim's face was frozen" | | 11 | "His clothes were expensive—tailored wool" | | 12 | "Quinn ran her fingers along" | | 13 | "A fine layer of dust" | | 14 | "She rubbed it between her" | | 15 | "She stood, scanning the platform." | | 16 | "The usual detritus of a" | | 17 | "Quinn exhaled through her nose." | | 18 | "That was the problem with" | | 19 | "She stepped closer to the" |
| | ratio | 0.814 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "The abandoned Tube station smelled of damp concrete and rust, the kind of scent that clung to the back of your throat like a bad memory." | | 1 | "The overhead lights flickered, casting long shadows that stretched like grasping fingers across the graffiti-scarred walls." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "She turned back, her mind racing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 29 | | tagDensity | 0.034 | | leniency | 0.069 | | rawRatio | 1 | | effectiveRatio | 0.069 | |