| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1555 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 1 |
| | 3 | |
| | highlights | | 0 | "precisely" | | 1 | "slowly" | | 2 | "deliberately" | | 3 | "completely" |
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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) | |
| 51.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1555 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "practiced ease" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "churned" | | 1 | "tracing" | | 2 | "echoing" | | 3 | "practiced ease" | | 4 | "navigating" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "electric" | | 8 | "pulsed" | | 9 | "silence" | | 10 | "weight" | | 11 | "perfect" | | 12 | "framework" | | 13 | "unravel" | | 14 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 122 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 122 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1576 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1576 | | uniqueNames | 12 | | maxNameDensity | 0.7 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Dean | 1 | | Street | 2 | | Morris | 4 | | Oxford | 1 | | Tube | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Tube" |
| | places | | 0 | "Soho" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Oxford" | | 4 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 32.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 4 | | matches | | 0 | "quite keep the rain out" | | 1 | "seemed designed to vanish" | | 2 | "looked like carved stone" | | 3 | "quite match the flames" |
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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 | 1576 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 122 | | matches | | 0 | "read that report" | | 1 | "learning that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 42.59 | | std | 28.06 | | cv | 0.659 | | sampleLengths | | 0 | 33 | | 1 | 83 | | 2 | 1 | | 3 | 76 | | 4 | 23 | | 5 | 38 | | 6 | 85 | | 7 | 77 | | 8 | 5 | | 9 | 31 | | 10 | 5 | | 11 | 58 | | 12 | 71 | | 13 | 52 | | 14 | 7 | | 15 | 74 | | 16 | 42 | | 17 | 51 | | 18 | 58 | | 19 | 39 | | 20 | 11 | | 21 | 84 | | 22 | 5 | | 23 | 100 | | 24 | 32 | | 25 | 33 | | 26 | 72 | | 27 | 63 | | 28 | 44 | | 29 | 5 | | 30 | 47 | | 31 | 57 | | 32 | 26 | | 33 | 11 | | 34 | 60 | | 35 | 14 | | 36 | 3 |
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| 99.51% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 122 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 262 | | matches | | 0 | "were being" | | 1 | "was doing" | | 2 | "was tracing" | | 3 | "was still walking" | | 4 | "was barking" | | 5 | "was navigating" | | 6 | "was watching" | | 7 | "was waiting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 21 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 122 | | ratio | 0.131 | | matches | | 0 | "She had spotted him half an hour ago — a wiry figure in a dark coat slipping out of The Raven's Nest through the side entrance, moving with the particular urgency of someone who knew they were being watched." | | 1 | "The streets of Soho churned with the usual late-evening crowd — clusters of smokers under awnings, delivery riders cutting through gaps in traffic, couples sharing umbrellas that couldn't quite keep the rain out." | | 2 | "The memory of DS Morris surfaced briefly — a rainy night not unlike this one, three years ago, when he'd walked into a situation neither of them understood and never walked out." | | 3 | "Her shoes were wrong for this — hard-soled, designed for interrogation rooms and courthouse corridors — but she'd chased men in worse footwear." | | 4 | "He bolted left down a side street, and Quinn broke into a sprint, her military training taking over — controlled breathing, arms driving, eyes fixed on the target even as rain streaked her vision." | | 5 | "Her landing was less graceful — her right ankle turned on uneven ground — but she caught her balance and kept going." | | 6 | "A fenced-off lot at the end of the street bore a faded sign she couldn't read from this distance, but the architecture behind it — that distinctive half-moon entrance, the rusted ironwork — was unmistakable." | | 7 | "It stood open a crack — her suspect had gone through without closing it fully." | | 8 | "Electric sconces — not wired to anything she could see — cast flickering amber light across the walls, and the shadows they threw moved in ways that didn't quite match the flames." | | 9 | "Traders moved between them — some human, some decidedly not." | | 10 | "The air was thick with competing smells — herbs, ozone, copper, smoke." | | 11 | "This was something else entirely, and the rules she knew — the rules she relied on — did not apply here." | | 12 | "He was watching her with an expression she couldn't read — not triumph, not fear." | | 13 | "The tension in the room didn't break, but it shifted — some of the hands she'd seen moving toward waistbands relaxed, though not all of them." | | 14 | "Then she looked at the market around her — the impossible goods, the impossible vendors, the entire hidden economy operating beneath the city's feet like a second circulatory system." | | 15 | "The crowd parted around her, not welcoming, not hostile — just yielding, the way water yields to a stone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1563 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.03134996801023673 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.010236724248240563 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 12.92 | | std | 9.65 | | cv | 0.747 | | sampleLengths | | 0 | 33 | | 1 | 39 | | 2 | 21 | | 3 | 23 | | 4 | 1 | | 5 | 26 | | 6 | 33 | | 7 | 17 | | 8 | 11 | | 9 | 1 | | 10 | 11 | | 11 | 14 | | 12 | 11 | | 13 | 13 | | 14 | 5 | | 15 | 30 | | 16 | 3 | | 17 | 4 | | 18 | 32 | | 19 | 11 | | 20 | 11 | | 21 | 2 | | 22 | 11 | | 23 | 23 | | 24 | 30 | | 25 | 5 | | 26 | 14 | | 27 | 7 | | 28 | 7 | | 29 | 3 | | 30 | 5 | | 31 | 34 | | 32 | 4 | | 33 | 5 | | 34 | 15 | | 35 | 21 | | 36 | 13 | | 37 | 15 | | 38 | 22 | | 39 | 11 | | 40 | 1 | | 41 | 1 | | 42 | 19 | | 43 | 11 | | 44 | 3 | | 45 | 3 | | 46 | 3 | | 47 | 7 | | 48 | 35 | | 49 | 4 |
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| 44.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3305785123966942 | | totalSentences | 121 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 109 | | matches | | 0 | "Somewhere a dog was barking." | | 1 | "Then she saw where he" | | 2 | "Maybe a hundred." | | 3 | "Somewhere behind the nearest stall," | | 4 | "Then she looked at the" |
| | ratio | 0.046 | |
| 69.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 109 | | matches | | 0 | "She had spotted him half" | | 1 | "She'd been sitting in her" | | 2 | "She keyed the ignition and" | | 3 | "Her suspect moved through all" | | 4 | "He turned north on Dean" | | 5 | "She killed the headlights, pocketed" | | 6 | "Her closely cropped salt-and-pepper hair" | | 7 | "She ignored it." | | 8 | "She'd worked worse nights." | | 9 | "She pushed the thought down" | | 10 | "Her shoes were wrong for" | | 11 | "He glanced over his shoulder." | | 12 | "She counted to five, then" | | 13 | "He was still walking, but" | | 14 | "He'd made her." | | 15 | "He bolted left down a" | | 16 | "She rounded it two seconds" | | 17 | "He was already over it," | | 18 | "She hit the fence climbing," | | 19 | "Her landing was less graceful" |
| | ratio | 0.376 | |
| 28.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 109 | | matches | | 0 | "The rain came down like" | | 1 | "She had spotted him half" | | 2 | "She'd been sitting in her" | | 3 | "The green neon sign above" | | 4 | "She keyed the ignition and" | | 5 | "The streets of Soho churned" | | 6 | "Her suspect moved through all" | | 7 | "Quinn glanced at the worn" | | 8 | "Whatever he was doing, he" | | 9 | "He turned north on Dean" | | 10 | "She killed the headlights, pocketed" | | 11 | "The cold hit her immediately." | | 12 | "Her closely cropped salt-and-pepper hair" | | 13 | "She ignored it." | | 14 | "She'd worked worse nights." | | 15 | "The memory of DS Morris" | | 16 | "She pushed the thought down" | | 17 | "The suspect was fifty meters" | | 18 | "Quinn matched his pace, keeping" | | 19 | "Her shoes were wrong for" |
| | ratio | 0.862 | |
| 45.87% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 109 | | matches | | | ratio | 0.009 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 8 | | matches | | 0 | "She had spotted him half an hour ago — a wiry figure in a dark coat slipping out of The Raven's Nest through the side entrance, moving with the particular urgen…" | | 1 | "The streets of Soho churned with the usual late-evening crowd — clusters of smokers under awnings, delivery riders cutting through gaps in traffic, couples shar…" | | 2 | "It was set into a wall that should have been solid, disguised behind a panel that matched the surrounding tile so precisely it seemed designed to vanish." | | 3 | "She could smell incense and something metallic beneath it, something that made the hair on her arms stand up." | | 4 | "Through the door, a narrow corridor descended in a tight spiral, the walls shifting from cracked station tile to older brickwork, then to something that looked …" | | 5 | "Stalls lined a vast platform where Tube trains had once run, their canvases and wooden frames clustered beneath a vaulted ceiling that vanished into darkness ov…" | | 6 | "The night he died, his partner had described the scene in language that didn't make sense, details that contradicted physics and common sense." | | 7 | "A case that could blow open everything she'd been building for months, maybe everything Morris had stumbled into before he died." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |