| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 2 | | adverbTags | | 0 | "that she had nowhere [nowhere]" | | 1 | "He smiled then [then]" |
| | dialogueSentences | 49 | | tagDensity | 0.469 | | leniency | 0.939 | | rawRatio | 0.087 | | effectiveRatio | 0.082 | |
| 82.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1949 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "perfectly" | | 1 | "slightly" | | 2 | "very" | | 3 | "really" |
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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) | |
| 61.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1949 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "carried the weight" | | count | 1 |
| | 7 | |
| | highlights | | 0 | "sanctuary" | | 1 | "pulse" | | 2 | "tracing" | | 3 | "weight" | | 4 | "silence" | | 5 | "traced" | | 6 | "carried the weight" | | 7 | "pulsed" |
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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 | 104 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 104 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "began to" | | 2 | "tried to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1972 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1574 | | uniqueNames | 16 | | maxNameDensity | 1.14 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 5 | | Rory | 18 | | Brick | 2 | | Lane | 2 | | Frida | 1 | | Kahlo | 1 | | Ptolemy | 7 | | Moreau | 2 | | June | 1 | | French | 2 | | Lucien | 9 | | Whitechapel | 1 | | Golden | 1 | | Empress | 1 | | London | 2 | | Shoreditch | 1 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Frida" | | 3 | "Kahlo" | | 4 | "Ptolemy" | | 5 | "Moreau" | | 6 | "Lucien" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Whitechapel" | | 3 | "London" | | 4 | "Shoreditch" |
| | globalScore | 0.928 | | windowScore | 1 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like whiskey and broken glass" | | 1 | "not quite a smile" |
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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 | 1972 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 130 | | matches | | 0 | "say that the" | | 1 | "say that she'd" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 36.52 | | std | 29.1 | | cv | 0.797 | | sampleLengths | | 0 | 116 | | 1 | 33 | | 2 | 61 | | 3 | 45 | | 4 | 4 | | 5 | 66 | | 6 | 15 | | 7 | 68 | | 8 | 22 | | 9 | 31 | | 10 | 5 | | 11 | 61 | | 12 | 12 | | 13 | 49 | | 14 | 6 | | 15 | 84 | | 16 | 4 | | 17 | 48 | | 18 | 68 | | 19 | 12 | | 20 | 49 | | 21 | 26 | | 22 | 20 | | 23 | 18 | | 24 | 100 | | 25 | 10 | | 26 | 34 | | 27 | 6 | | 28 | 14 | | 29 | 58 | | 30 | 104 | | 31 | 12 | | 32 | 5 | | 33 | 3 | | 34 | 41 | | 35 | 4 | | 36 | 41 | | 37 | 8 | | 38 | 72 | | 39 | 2 | | 40 | 59 | | 41 | 3 | | 42 | 9 | | 43 | 83 | | 44 | 11 | | 45 | 36 | | 46 | 42 | | 47 | 34 | | 48 | 7 | | 49 | 64 |
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| 95.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 104 | | matches | | 0 | "was slicked" | | 1 | "was gone" | | 2 | "been stolen" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 265 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 23 | | semicolonCount | 1 | | flaggedSentences | 18 | | totalSentences | 130 | | ratio | 0.138 | | matches | | 0 | "That, and the fact that she had nowhere else to go — Silas' bar was being fumigated, her own flat above it inhospitable with chemical fog, and the delivery route for the Golden Empress didn't start until Thursday." | | 1 | "The first thing she noticed was the cane — ivory-handled, elegant, its silver tip catching the lamplight." | | 2 | "Then the face: sharp cheekbones, a mouth that always seemed to be on the verge of a smirk, and those impossible eyes — one amber, one black, catching the dim light of the hallway like a dare." | | 3 | "His platinum blond hair was slicked back as always, not a strand out of place, though a faint smudge of something dark — soot?" | | 4 | "— marked his left cuff." | | 5 | "Lucien's free hand — the one not wrapped around the ivory handle — dropped to scratch behind the tabby's ears without looking, as though he'd done it a hundred times before." | | 6 | "\"I know.\" Lucien's gaze swept the flat — the books stacked in precarious columns on every surface, the scroll unfurled across the kitchen table, the open laptop surrounded by sticky notes and empty mugs." | | 7 | "\"I did. Twice.\" He reached into the inner pocket of his jacket and produced her phone — her actual phone — and set it on the coffee table beside her book." | | 8 | "She hadn't thought — hadn't wanted to think — about who might have picked it up." | | 9 | "\"Four months, eleven days.\" The corner of his mouth twitched — not quite a smile." | | 10 | "She wanted to say that she'd spent the weeks after that replaying every word, dissecting every inflection, trying to determine exactly where the conversation had curdled — and whether she was the one who'd poisoned it or whether it had been him." | | 11 | "Something shifted in his expression — the faintest crack in that composed, tailored surface." | | 12 | "She scratched absently behind his ears — the same motion she'd watched Lucien perform moments ago — and felt the scar on her wrist catch against the rough fabric of her jeans." | | 13 | "It had taken her weeks of working alongside him — delivering packages for the Golden Empress that turned out to be something other than food, running routes through parts of London that didn't appear on any map — before she could meet both eyes at once without flinching." | | 14 | "\"She underestimates.\" Lucien's amber eye caught the light; his black eye gave nothing back." | | 15 | "That silence had been cold, a wall between them built brick by brick over the course of a conversation that had started innocently enough — dinner at a bistro in Shoreditch, a bottle of wine, the easy banter that had always come so naturally between them before it didn't." | | 16 | "She should thank him for returning her phone, ask him to pass along whatever intelligence he'd gathered, and show him to the door — all three deadbolts, locked and bolted behind him." | | 17 | "It was simply a space — narrow and warm and surrounded by the chaos of a life in progress — left open for someone to fill." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 292 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.017123287671232876 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.00684931506849315 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 15.17 | | std | 13.05 | | cv | 0.86 | | sampleLengths | | 0 | 17 | | 1 | 38 | | 2 | 61 | | 3 | 33 | | 4 | 13 | | 5 | 21 | | 6 | 4 | | 7 | 23 | | 8 | 5 | | 9 | 8 | | 10 | 18 | | 11 | 14 | | 12 | 4 | | 13 | 17 | | 14 | 12 | | 15 | 37 | | 16 | 15 | | 17 | 38 | | 18 | 24 | | 19 | 1 | | 20 | 5 | | 21 | 3 | | 22 | 14 | | 23 | 5 | | 24 | 3 | | 25 | 15 | | 26 | 1 | | 27 | 1 | | 28 | 6 | | 29 | 5 | | 30 | 3 | | 31 | 2 | | 32 | 20 | | 33 | 31 | | 34 | 10 | | 35 | 12 | | 36 | 34 | | 37 | 15 | | 38 | 2 | | 39 | 4 | | 40 | 35 | | 41 | 22 | | 42 | 15 | | 43 | 12 | | 44 | 4 | | 45 | 31 | | 46 | 17 | | 47 | 5 | | 48 | 3 | | 49 | 22 |
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| 38.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2846153846153846 | | totalSentences | 130 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 92 | | matches | | 0 | "Then the third, each one" | | 1 | "Then the suit, charcoal and" | | 2 | "Then the face: sharp cheekbones," | | 3 | "Instead, she said:" | | 4 | "Instead, Rory shifted on the" |
| | ratio | 0.054 | |
| 46.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 92 | | matches | | 0 | "It was nearly eleven on" | | 1 | "Her pulse climbed, but her" | | 2 | "She'd trained herself out of" | | 3 | "Her fingers found the crescent-shaped" | | 4 | "He paused in the narrow" | | 5 | "His platinum blond hair was" | | 6 | "His French accent thickened the" | | 7 | "Her heart was doing something" | | 8 | "She'd learned to weaponize it." | | 9 | "He said her name the" | | 10 | "He moved further into the" | | 11 | "He reached into the inner" | | 12 | "She remembered now." | | 13 | "She'd borrowed Eva's landline to" | | 14 | "She hadn't thought — hadn't" | | 15 | "He didn't sit down" | | 16 | "He stood in the middle" | | 17 | "She wanted to say something" | | 18 | "She wanted to say that" | | 19 | "She wanted to say that" |
| | ratio | 0.435 | |
| 8.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 92 | | matches | | 0 | "The triple deadbolts on Eva's" | | 1 | "That, and the fact that" | | 2 | "It was nearly eleven on" | | 3 | "Rory's head came up from" | | 4 | "The tabby cat, Ptolemy, lifted" | | 5 | "The second deadbolt followed." | | 6 | "Rory set the book down." | | 7 | "Her pulse climbed, but her" | | 8 | "She'd trained herself out of" | | 9 | "Her fingers found the crescent-shaped" | | 10 | "The door swung inward." | | 11 | "The first thing she noticed" | | 12 | "Lucien Moreau stepped inside and" | | 13 | "He paused in the narrow" | | 14 | "His platinum blond hair was" | | 15 | "His French accent thickened the" | | 16 | "Rory didn't move." | | 17 | "Her heart was doing something" | | 18 | "That's what people said about" | | 19 | "She'd learned to weaponize it." |
| | ratio | 0.902 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Now she met them easily," |
| | ratio | 0.011 | |
| 14.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 9 | | matches | | 0 | "He paused in the narrow entryway, just past the coat rack where Eva's three winter coats still hung despite it being June, and looked at Rory on the pull-out so…" | | 1 | "Lucien's free hand — the one not wrapped around the ivory handle — dropped to scratch behind the tabby's ears without looking, as though he'd done it a hundred …" | | 2 | "He stood in the middle of Eva's living room, surrounded by the cluttered archaeology of her research, and looked at Rory with both mismatched eyes and a stillne…" | | 3 | "He set his cane across his knees, one elegant hand resting on the ivory handle, and the lamplight caught the heterochromaticismatch of his eyes in a way that ma…" | | 4 | "It had taken her weeks of working alongside him — delivering packages for the Golden Empress that turned out to be something other than food, running routes thr…" | | 5 | "That silence had been cold, a wall between them built brick by brick over the course of a conversation that had started innocently enough — dinner at a bistro i…" | | 6 | "This silence was warm and heavy, saturated with everything that had come before and everything that might come after." | | 7 | "She felt the scar on her wrist pulse with her heartbeat, a small crescent of old pain that had nothing to do with the sharp, bright thing blooming in her chest." | | 8 | "She knew that with the same cool-headed clarity that had gotten her through university, through London, through everything that had tried to break her." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 88.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "she agreed (agree)" | | 1 | "Rory added (add)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 49 | | tagDensity | 0.204 | | leniency | 0.408 | | rawRatio | 0.3 | | effectiveRatio | 0.122 | |