| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1982 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 41.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1982 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "measured" | | 1 | "footsteps" | | 2 | "pounding" | | 3 | "determined" | | 4 | "tracing" | | 5 | "flickered" | | 6 | "could feel" | | 7 | "weight" | | 8 | "pumping" | | 9 | "depths" | | 10 | "echoes" | | 11 | "streaming" | | 12 | "flicked" | | 13 | "electric" | | 14 | "wavering" | | 15 | "velvet" | | 16 | "silk" | | 17 | "shimmered" | | 18 | "scanned" | | 19 | "intricate" | | 20 | "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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 145 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 23 | | totalWords | 2006 | | ratio | 0.011 | | matches | | 0 | "So you always know exactly how long you've been waiting for the truth" | | 1 | "Bone token gets you into the market. Camden. Full moon." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 1 | | matches | | 0 | "The Saint Christopher medallion, Quinn guessed." |
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| 96.56% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 1965 | | uniqueNames | 27 | | maxNameDensity | 1.07 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 21 | | Tomás | 1 | | Herrera | 17 | | Raven | 1 | | Nest | 4 | | London | 2 | | Greek | 1 | | Street | 2 | | Morris | 5 | | Saint | 1 | | Christopher | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Camden | 2 | | High | 1 | | Regent | 1 | | Canal | 1 | | Tube | 3 | | Tonight | 2 | | Met | 1 | | Central | 1 | | Division | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Regent" | | 9 | "Tube" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Greek" | | 3 | "Street" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Camden" | | 8 | "High" | | 9 | "Met" | | 10 | "Market" |
| | globalScore | 0.966 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 112 | | glossingSentenceCount | 2 | | matches | | 0 | "quite match any language she spoke" | | 1 | "looked like people — moved through the ai" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.997 | | wordCount | 2006 | | matches | | 0 | "not echoes, but voices" | | 1 | "not electric but amber" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 148 | | matches | | 0 | "confirming that Herrera knew that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 54.22 | | std | 40.49 | | cv | 0.747 | | sampleLengths | | 0 | 79 | | 1 | 133 | | 2 | 61 | | 3 | 112 | | 4 | 72 | | 5 | 85 | | 6 | 50 | | 7 | 85 | | 8 | 90 | | 9 | 125 | | 10 | 5 | | 11 | 34 | | 12 | 106 | | 13 | 63 | | 14 | 17 | | 15 | 107 | | 16 | 34 | | 17 | 13 | | 18 | 15 | | 19 | 77 | | 20 | 12 | | 21 | 3 | | 22 | 152 | | 23 | 57 | | 24 | 68 | | 25 | 58 | | 26 | 39 | | 27 | 25 | | 28 | 5 | | 29 | 13 | | 30 | 44 | | 31 | 35 | | 32 | 22 | | 33 | 6 | | 34 | 84 | | 35 | 9 | | 36 | 11 |
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| 85.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 145 | | matches | | 0 | "was scratched" | | 1 | "was plastered" | | 2 | "been closed" | | 3 | "was connected" | | 4 | "been covered" | | 5 | "was forgotten" | | 6 | "were built " | | 7 | "was connected" |
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| 17.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 9 | | totalVerbs | 328 | | matches | | 0 | "were closing" | | 1 | "was slowing" | | 2 | "was heading" | | 3 | "was leaving" | | 4 | "was sprinting" | | 5 | "was heading" | | 6 | "wasn't looking" | | 7 | "was overwhelming " | | 8 | "was getting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 24 | | semicolonCount | 0 | | flaggedSentences | 21 | | totalSentences | 148 | | ratio | 0.142 | | matches | | 0 | "Detective Harlow Quinn kept her stride measured even as her quarry's footsteps slapped wet pavement fifty yards ahead — a metronome of panic against the hiss of the downpour." | | 1 | "Three weeks of surveillance had confirmed what her instincts had whispered since she'd first seen his name cross a coroner's report — Herrera moved in circles that bent the ordinary rules of London's underworld until they snapped." | | 2 | "Tonight, when she'd stepped out of the doorway across the street and called his name — just once, clear and sharp above the drumming rain — he'd bolted." | | 3 | "His dark curly hair was plastered to his skull, and his left hand kept going to his chest — clutching something beneath his shirt." | | 4 | "If it was her informant at the Nest, confirming that Herrera had left behind a medical bag with bottles of iridescent fluid that definitely weren't saline — well, she already knew that much." | | 5 | "The beam caught a smear of blood on a rusted pipe at hip height — his scarred forearm, she thought." | | 6 | "No firearm tonight — not for surveillance outside a bar." | | 7 | "He was heading toward the old Tube station — the one that had been closed since the seventies, sealed off behind iron grates and municipal neglect." | | 8 | "It swung open — no padlock, no chain." | | 9 | "It was ornate, older than the station itself, with a pattern that looked almost deliberate — spirals within spirals, like a fingerprint carved in metal." | | 10 | "She'd seen similar marks in case files she wasn't supposed to have access to — the ones Morris had been building before he died, the ones that had been quietly removed from the archive after his funeral." | | 11 | "A faint sound rose from the depths — not echoes, but voices." | | 12 | "Herrera was connected to the clique — the word her informant used for the network of people who moved through London's ordinary streets while doing things that ordinary people couldn't explain." | | 13 | "The air changed immediately — warmer, heavier, carrying scents that didn't belong in a sealed Tube station." | | 14 | "Hundreds of people — or things that looked like people — moved through the aisles, their faces lit by lanterns that floated without chains, hovering at shoulder height." | | 15 | "The noise was overwhelming — a babel of haggling and laughter and hissed negotiations." | | 16 | "He passed a vendor selling bones arranged in intricate patterns — small tokens, Quinn realized, each one carved with a symbol that matched the iron hand on the grate above." | | 17 | "The man — or the being — behind the stall had skin the color of wet slate and eyes with no whites at all, just black pools ringed with amber." | | 18 | "The streets she'd chased him through belonged to another world — a world where rules made sense and warrants carried weight." | | 19 | "Return to the surface and build her case the way cases were built — with paper and patience and procedure." | | 20 | "She reached into her memory and offered up the smallest thing she could spare — the face of a witness from a case six years ago, a woman whose name Quinn could no longer remember, though she knew she'd once written it in her notebook a dozen times." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 164 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.006097560975609756 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.006097560975609756 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 13.55 | | std | 9.33 | | cv | 0.689 | | sampleLengths | | 0 | 22 | | 1 | 29 | | 2 | 19 | | 3 | 9 | | 4 | 33 | | 5 | 22 | | 6 | 9 | | 7 | 32 | | 8 | 37 | | 9 | 28 | | 10 | 12 | | 11 | 21 | | 12 | 19 | | 13 | 9 | | 14 | 18 | | 15 | 11 | | 16 | 20 | | 17 | 35 | | 18 | 10 | | 19 | 24 | | 20 | 6 | | 21 | 6 | | 22 | 5 | | 23 | 15 | | 24 | 6 | | 25 | 18 | | 26 | 6 | | 27 | 4 | | 28 | 20 | | 29 | 24 | | 30 | 4 | | 31 | 9 | | 32 | 6 | | 33 | 3 | | 34 | 8 | | 35 | 33 | | 36 | 14 | | 37 | 11 | | 38 | 8 | | 39 | 20 | | 40 | 25 | | 41 | 7 | | 42 | 8 | | 43 | 12 | | 44 | 18 | | 45 | 4 | | 46 | 10 | | 47 | 23 | | 48 | 10 | | 49 | 5 |
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| 41.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3108108108108108 | | totalSentences | 148 | | uniqueOpeners | 46 | |
| 24.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 138 | | matches | | 0 | "Then at Herrera, who was" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 138 | | matches | | 0 | "She'd learned long ago that" | | 1 | "She wasn't going to let" | | 2 | "She'd spotted him leaving The" | | 3 | "He didn't come to" | | 4 | "He came to stitch wounds" | | 5 | "She'd been wearing the same" | | 6 | "She pressed the thought down," | | 7 | "His dark curly hair was" | | 8 | "She'd noted it in her" | | 9 | "She'd memorized this stretch of" | | 10 | "He was heading north." | | 11 | "Her phone buzzed against her" | | 12 | "She ignored it." | | 13 | "She followed it out onto" | | 14 | "Her jacket, soaked through and" | | 15 | "She could feel the weight" | | 16 | "She filed the regret away." | | 17 | "He was heading toward the" | | 18 | "It swung open — no" | | 19 | "He squeezed through the gap" |
| | ratio | 0.297 | |
| 83.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 138 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "She'd learned long ago that" | | 3 | "She wasn't going to let" | | 4 | "She'd spotted him leaving The" | | 5 | "He didn't come to" | | 6 | "He came to stitch wounds" | | 7 | "Tonight, when she'd stepped out" | | 8 | "The explosive, heel-pounding flight of" | | 9 | "Quinn's worn leather watch caught" | | 10 | "She'd been wearing the same" | | 11 | "The face was scratched, the" | | 12 | "Morris had given it to" | | 13 | "She pressed the thought down," | | 14 | "Herrera was fast, but the" | | 15 | "His dark curly hair was" | | 16 | "The Saint Christopher medallion, Quinn" | | 17 | "She'd noted it in her" | | 18 | "The man was a believer." | | 19 | "That was what tonight was" |
| | ratio | 0.754 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 138 | | matches | | 0 | "*So you always know exactly" | | 1 | "Whether in saints or in" | | 2 | "If it was the station," | | 3 | "If it was her informant" | | 4 | "Either way, he was leaving" |
| | ratio | 0.036 | |
| 32.31% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 84 | | technicalSentenceCount | 13 | | matches | | 0 | "She'd spotted him leaving The Raven's Nest at half past eleven, pulling up the collar of his leather jacket against the weather with the casual ease of someone …" | | 1 | "He came to stitch wounds that didn't come from kitchen knives, to administer remedies that no NHS pharmacist would recognize, and to collect payments in envelop…" | | 2 | "His dark curly hair was plastered to his skull, and his left hand kept going to his chest — clutching something beneath his shirt." | | 3 | "The alley connected to a narrow passage behind the old bookshops, which emptied onto a service road that ran parallel to Camden High Street." | | 4 | "The passage opened onto a narrow footpath that ran alongside the Regent's Canal, black water barely visible through the rain." | | 5 | "It was ornate, older than the station itself, with a pattern that looked almost deliberate — spirals within spirals, like a fingerprint carved in metal." | | 6 | "A solve rate that had made her the youngest detective sergeant in the Met's Central Division." | | 7 | "Herrera was connected to the clique — the word her informant used for the network of people who moved through London's ordinary streets while doing things that …" | | 8 | "The air changed immediately — warmer, heavier, carrying scents that didn't belong in a sealed Tube station." | | 9 | "Hundreds of people — or things that looked like people — moved through the aisles, their faces lit by lanterns that floated without chains, hovering at shoulder…" | | 10 | "Quinn's gaze snagged on details: glass jars filled with luminescent insects, bundles of feathers that shimmered between colors, a rack of masks whose expression…" | | 11 | "He passed a vendor selling bones arranged in intricate patterns — small tokens, Quinn realized, each one carved with a symbol that matched the iron hand on the …" | | 12 | "Then at Herrera, who was getting farther away, slipping toward a curtained alcove at the far end of the platform." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |