| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 2 | | adverbTags | | 0 | "Gemma asked quietly [quietly]" | | 1 | "Gemma said eventually [eventually]" |
| | dialogueSentences | 60 | | tagDensity | 0.367 | | leniency | 0.733 | | rawRatio | 0.091 | | effectiveRatio | 0.067 | |
| 88.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2177 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "precisely" | | 1 | "quickly" | | 2 | "slightly" | | 3 | "slowly" | | 4 | "gently" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 74.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2177 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "scanning" | | 1 | "stomach" | | 2 | "warmth" | | 3 | "calculated" | | 4 | "silence" | | 5 | "flicker" | | 6 | "traced" | | 7 | "calculating" | | 8 | "could feel" | | 9 | "potential" |
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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 | 100 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 100 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 80 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2195 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 86 | | wordCount | 1610 | | uniqueNames | 24 | | maxNameDensity | 1.8 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Tuesday | 1 | | Raven | 2 | | Nest | 2 | | Rory | 29 | | Mayfair | 1 | | Pomeranian | 1 | | Soho | 1 | | Yu-Fei | 1 | | Saturday | 1 | | London | 2 | | Price | 1 | | Torts | 1 | | Silas | 5 | | Sauvignon | 1 | | Blanc | 1 | | Gemma | 27 | | Golden | 1 | | Empress | 1 | | City | 1 | | Bermondsey | 1 | | Oliver | 1 | | Evan | 2 | | Eva | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Price" | | 3 | "Silas" | | 4 | "Gemma" | | 5 | "Oliver" | | 6 | "Evan" | | 7 | "Eva" |
| | places | | 0 | "Mayfair" | | 1 | "Soho" | | 2 | "London" | | 3 | "City" | | 4 | "Bermondsey" | | 5 | "Cardiff" |
| | globalScore | 0.599 | | windowScore | 0.333 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like it cost more than Rory's rent" | | 1 | "smelled like expensive perfume and the par" | | 2 | "as though steadying herself" |
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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 | 2195 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 138 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 31.81 | | std | 29.13 | | cv | 0.916 | | sampleLengths | | 0 | 93 | | 1 | 62 | | 2 | 7 | | 3 | 4 | | 4 | 33 | | 5 | 58 | | 6 | 80 | | 7 | 12 | | 8 | 3 | | 9 | 50 | | 10 | 20 | | 11 | 51 | | 12 | 58 | | 13 | 35 | | 14 | 36 | | 15 | 5 | | 16 | 14 | | 17 | 11 | | 18 | 40 | | 19 | 13 | | 20 | 36 | | 21 | 36 | | 22 | 74 | | 23 | 19 | | 24 | 3 | | 25 | 8 | | 26 | 70 | | 27 | 37 | | 28 | 71 | | 29 | 9 | | 30 | 4 | | 31 | 9 | | 32 | 11 | | 33 | 39 | | 34 | 69 | | 35 | 16 | | 36 | 2 | | 37 | 27 | | 38 | 129 | | 39 | 5 | | 40 | 64 | | 41 | 3 | | 42 | 39 | | 43 | 1 | | 44 | 14 | | 45 | 3 | | 46 | 97 | | 47 | 53 | | 48 | 103 | | 49 | 26 |
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| 94.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 100 | | matches | | 0 | "being asked" | | 1 | "was pulled" | | 2 | "being served" |
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| 82.21% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 283 | | matches | | 0 | "was composing" | | 1 | "was scanning" | | 2 | "was already crossing" | | 3 | "were sitting" | | 4 | "was still calculating" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 1 | | flaggedSentences | 10 | | totalSentences | 138 | | ratio | 0.072 | | matches | | 0 | "Her dark hair was pulled back in a low chignon, and she carried herself with that particular London posture — shoulders back, chin steady, the body language of someone who'd learned to take up space in rooms full of men." | | 1 | "He had that instinct — the quiet triangulation of a man who'd once read rooms for a living." | | 2 | "Gemma laughed — a short, honest sound — and for a moment the years collapsed and they were sitting in the student union sharing a plate of chips, talking about nothing that mattered." | | 3 | "The scar was from childhood — she'd caught it on a fence climbing after a neighbour's cat — but the gesture had taken on new meaning in the years since, a touchstone she reached for when she needed to remind herself that not every hurt left a mark you could see." | | 4 | "Gemma was at a mid-size firm in the City — commercial litigation, she said, which meant she spent most of her days arguing about contract clauses and the rest of them trying to remember why she'd wanted to." | | 5 | "She thought of Evan — not the Evan she'd first met, the one who brought her coffee before lectures and learned her mother's birthday, but the other one." | | 6 | "Gemma watched her with those careful brown eyes, and Rory could see the questions stacking up behind them, organized and precise — Gemma had always been good at building a case from fragments." | | 7 | "The bar hummed around them — the low murmur of the couple by the window, a jazz track drifting from speakers mounted above the black-and-white photographs on the wall, Silas polishing glasses with quiet, deliberate strokes." | | 8 | "Time didn't erase people; it just laid new versions over the old ones like acetate on a map, so the original lines showed through if you held it to the light." | | 9 | "She stood there for a moment, and Rory could feel her wanting to say more — to argue the case, to build toward some closing statement about potential and waste and roads not taken." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1607 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 51 | | adverbRatio | 0.031736154324828875 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.01120099564405725 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 15.91 | | std | 13.37 | | cv | 0.84 | | sampleLengths | | 0 | 20 | | 1 | 29 | | 2 | 33 | | 3 | 3 | | 4 | 8 | | 5 | 31 | | 6 | 31 | | 7 | 7 | | 8 | 4 | | 9 | 33 | | 10 | 19 | | 11 | 6 | | 12 | 11 | | 13 | 22 | | 14 | 17 | | 15 | 40 | | 16 | 23 | | 17 | 12 | | 18 | 3 | | 19 | 2 | | 20 | 48 | | 21 | 3 | | 22 | 13 | | 23 | 4 | | 24 | 25 | | 25 | 26 | | 26 | 32 | | 27 | 26 | | 28 | 5 | | 29 | 11 | | 30 | 12 | | 31 | 7 | | 32 | 31 | | 33 | 5 | | 34 | 5 | | 35 | 14 | | 36 | 5 | | 37 | 6 | | 38 | 18 | | 39 | 1 | | 40 | 9 | | 41 | 12 | | 42 | 3 | | 43 | 10 | | 44 | 20 | | 45 | 16 | | 46 | 18 | | 47 | 18 | | 48 | 19 | | 49 | 55 |
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| 43.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3188405797101449 | | totalSentences | 138 | | uniqueOpeners | 44 | |
| 78.43% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 85 | | matches | | 0 | "Then a careful rearrangement into" | | 1 | "Then she saved the contact," |
| | ratio | 0.024 | |
| 92.94% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 85 | | matches | | 0 | "She sat at the far" | | 1 | "She'd walked back to Soho" | | 2 | "He moved behind the bar" | | 3 | "He draped a clean towel" | | 4 | "She was composing a reply" | | 5 | "Her dark hair was pulled" | | 6 | "She was scanning the bar" | | 7 | "She was already crossing the" | | 8 | "She stood, and the delivery" | | 9 | "She left it there." | | 10 | "She sat back down and" | | 11 | "He had that instinct —" | | 12 | "She'd made junior partner eight" | | 13 | "She had a flat in" | | 14 | "She thought of Evan —" | | 15 | "She thought of the night" | | 16 | "She thought of the three" | | 17 | "She'd be connecting the disappearance" | | 18 | "She'd trained herself out of" | | 19 | "She picked up her wine" |
| | ratio | 0.318 | |
| 7.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 85 | | matches | | 0 | "The Tuesday crowd at The" | | 1 | "She sat at the far" | | 2 | "The last run of the" | | 3 | "Rory had smiled." | | 4 | "She'd walked back to Soho" | | 5 | "Silas set a fresh gin" | | 6 | "He moved behind the bar" | | 7 | "He draped a clean towel" | | 8 | "Rory pressed the towel against" | | 9 | "A text from Yu-Fei asking" | | 10 | "She was composing a reply" | | 11 | "The woman who entered wore" | | 12 | "Her dark hair was pulled" | | 13 | "She was scanning the bar" | | 14 | "Rory felt the recognition hit" | | 15 | "She was already crossing the" | | 16 | "She stood, and the delivery" | | 17 | "She left it there." | | 18 | "Gemma pulled her into a" | | 19 | "Gemma slid onto the stool" |
| | ratio | 0.906 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 12.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 10 | | matches | | 0 | "He moved behind the bar with the quiet economy of a man who'd spent decades making himself unobtrusive in rooms, his limp barely noticeable unless you knew to l…" | | 1 | "The woman who entered wore a camel-colored coat that looked like it cost more than Rory's rent." | | 2 | "Her dark hair was pulled back in a low chignon, and she carried herself with that particular London posture — shoulders back, chin steady, the body language of …" | | 3 | "She was already crossing the room, heels clicking against the old hardwood, her face breaking into an expression of such unguarded surprise that for a moment sh…" | | 4 | "Gemma pulled her into a hug that smelled like expensive perfume and the particular warmth of someone who'd just come out of a heated car." | | 5 | "Gemma laughed — a short, honest sound — and for a moment the years collapsed and they were sitting in the student union sharing a plate of chips, talking about …" | | 6 | "Gemma was at a mid-size firm in the City — commercial litigation, she said, which meant she spent most of her days arguing about contract clauses and the rest o…" | | 7 | "A fiancé named Oliver who worked in sustainable architecture and owned too many succulents." | | 8 | "But she took a breath that went deeper than it needed to, and when she exhaled, the sound was steady in a way that was its own kind of answer." | | 9 | "The ordinary machinery of London grinding forward without regard for the small, enormous things that happened in bars after dark." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.217 | | leniency | 0.433 | | rawRatio | 0 | | effectiveRatio | 0 | |