| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1453 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "suddenly" | | 1 | "slowly" | | 2 | "softly" |
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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) | |
| 48.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1453 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "weight" | | 1 | "dance" | | 2 | "flickered" | | 3 | "pulse" | | 4 | "echoed" | | 5 | "scanning" | | 6 | "vibrated" | | 7 | "pulsed" | | 8 | "etched" | | 9 | "velvet" | | 10 | "familiar" | | 11 | "silence" | | 12 | "footsteps" | | 13 | "measured" |
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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 | 83 | | matches | (empty) | |
| 91.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1453 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1424 | | uniqueNames | 13 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Camden | 1 | | Raven | 1 | | Nest | 1 | | Morris | 2 | | Brixton | 1 | | Hackney | 1 | | Veil | 1 | | Market | 1 | | Met | 1 | | London | 2 | | Tube | 1 | | Quinn | 12 |
| | persons | | 0 | "Raven" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Brixton" | | 2 | "Hackney" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 3 | | matches | | 0 | "velvet that seemed to drink the light" | | 1 | "for her entry, apparently, for the mechanism" | | 2 | "edges that seemed to cut the air itself" |
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| 62.35% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.376 | | wordCount | 1453 | | matches | | 0 | "not sewer exactly, but old electricity" | | 1 | "not from exertion now, but from the wrongness of the air, thick with magic" |
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| 11.63% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 4 | | totalSentences | 86 | | matches | | 0 | "screaming that she apply, that the" | | 1 | "screaming that she hidden, that eyes" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 42.74 | | std | 30.87 | | cv | 0.722 | | sampleLengths | | 0 | 47 | | 1 | 3 | | 2 | 93 | | 3 | 97 | | 4 | 4 | | 5 | 55 | | 6 | 20 | | 7 | 87 | | 8 | 8 | | 9 | 23 | | 10 | 73 | | 11 | 75 | | 12 | 48 | | 13 | 39 | | 14 | 3 | | 15 | 58 | | 16 | 110 | | 17 | 51 | | 18 | 77 | | 19 | 22 | | 20 | 84 | | 21 | 39 | | 22 | 3 | | 23 | 44 | | 24 | 3 | | 25 | 79 | | 26 | 19 | | 27 | 29 | | 28 | 20 | | 29 | 27 | | 30 | 51 | | 31 | 36 | | 32 | 12 | | 33 | 14 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "been forged" | | 1 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 258 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 86 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1435 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.032752613240418116 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.008362369337979094 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 16.9 | | std | 9.72 | | cv | 0.575 | | sampleLengths | | 0 | 7 | | 1 | 15 | | 2 | 25 | | 3 | 3 | | 4 | 17 | | 5 | 19 | | 6 | 3 | | 7 | 33 | | 8 | 21 | | 9 | 16 | | 10 | 6 | | 11 | 18 | | 12 | 37 | | 13 | 13 | | 14 | 7 | | 15 | 4 | | 16 | 19 | | 17 | 18 | | 18 | 3 | | 19 | 15 | | 20 | 18 | | 21 | 2 | | 22 | 4 | | 23 | 18 | | 24 | 21 | | 25 | 18 | | 26 | 26 | | 27 | 1 | | 28 | 7 | | 29 | 4 | | 30 | 19 | | 31 | 9 | | 32 | 17 | | 33 | 17 | | 34 | 30 | | 35 | 4 | | 36 | 13 | | 37 | 23 | | 38 | 35 | | 39 | 28 | | 40 | 20 | | 41 | 22 | | 42 | 17 | | 43 | 3 | | 44 | 6 | | 45 | 32 | | 46 | 20 | | 47 | 18 | | 48 | 14 | | 49 | 11 |
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| 57.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3953488372093023 | | totalSentences | 86 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 80 | | matches | | 0 | "Only the rain tapping on" | | 1 | "Then she heard it." | | 2 | "Then the door behind her" | | 3 | "Then a voice, smooth and" |
| | ratio | 0.05 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 80 | | matches | | 0 | "She threw her weight forward," | | 1 | "She pushed harder." | | 2 | "She ignored it." | | 3 | "Her right arm pumped, steady," | | 4 | "She had followed him out" | | 5 | "She rounded the corner." | | 6 | "He looked back." | | 7 | "Their eyes locked, brown against" | | 8 | "He did not stop." | | 9 | "He dove into a gap" | | 10 | "She squeezed through, elbows scraping" | | 11 | "She drew her weapon." | | 12 | "She had heard whispers, of" | | 13 | "Her heart hammered against her" | | 14 | "He moved through the crowd" | | 15 | "He approached a heavy iron" | | 16 | "She stood at the edge" | | 17 | "She thought of Morris, falling" | | 18 | "She thought of eighteen years" | | 19 | "She pushed through." |
| | ratio | 0.263 | |
| 78.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 80 | | matches | | 0 | "Quinn's boot heel skidded on" | | 1 | "She threw her weight forward," | | 2 | "The hooded figure ahead had" | | 3 | "She pushed harder." | | 4 | "Rain plastered her salt-and-pepper hair" | | 5 | "She ignored it." | | 6 | "The suspect, a wiry man" | | 7 | "Quinn's left hand found the" | | 8 | "Her right arm pumped, steady," | | 9 | "The worn leather watch on" | | 10 | "She had followed him out" | | 11 | "The clique moved drugs and" | | 12 | "The clique that might have" | | 13 | "She rounded the corner." | | 14 | "The alley narrowed between brick" | | 15 | "The suspect was twenty metres" | | 16 | "He looked back." | | 17 | "Their eyes locked, brown against" | | 18 | "Quinn's voice cracked sharp across" | | 19 | "He did not stop." |
| | ratio | 0.763 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 80 | | matches | | 0 | "Now they closed on Camden," | | 1 | "Now she stood at the" |
| | ratio | 0.025 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 20 | | matches | | 0 | "She threw her weight forward, shoulder clipping a rubbish bin that rattled like dry bones." | | 1 | "She had followed him out of The Raven's Nest two hours prior, through the green neon glare that reflected in puddles like toxic algae, convinced he belonged to …" | | 2 | "The suspect was twenty metres ahead, hood down now, revealing a shaved head that glistened like wet bone." | | 3 | "Quinn accelerated, her shoes splashing through a puddle that soaked her trousers to the knee with water that stank of diesel." | | 4 | "At the base, a rusted iron grate hung open, revealing stairs that plunged downward into absolute blackness." | | 5 | "By the tenth step she needed her torch, but the beam felt weak against the darkness, catching only wet concrete and peeling posters for bands that had died deca…" | | 6 | "At the bottom, a corridor stretched left and right, lined with cracked tiles that might once have been white but now wore stains of rust and something darker." | | 7 | "Every detective in the Met heard whispers of places that existed in the cracks of London, hidden beneath the known world like tumours beneath skin, but she had …" | | 8 | "Now she stood at the threshold of an abandoned Tube platform transformed into something that violated every law she understood." | | 9 | "Vendors hawked goods that glowed, smoked, or twitched inside glass jars etched with symbols." | | 10 | "Banned alchemical powders sat in neat rows like spices in a corner shop, each one labelled in languages that hurt her eyes to trace." | | 11 | "And people moved among them, some with skin like polished stone that reflected the stall-lights, others with eyes that caught illumination like cats, and still …" | | 12 | "He moved through the crowd with purpose, weaving past a stall selling bone tokens strung on leather cords that clicked together like teeth." | | 13 | "He approached a heavy iron door set into the tunnel wall, pulled a token from his pocket, and pressed it against a rusted mechanism that glowed red for a moment…" | | 14 | "Quinn studied the token in her mind, bone-white, carved with symbols that hurt to recall, like staring too long at the sun." | | 15 | "Ahead lay the door her suspect had entered, and whatever waited beyond in corridors that did not appear on any map she had ever studied." | | 16 | "The iron door stood ajar, exhaling cold air that made her skin tighten." | | 17 | "The walls were lined with maps, old and new, pinned with knives that served as territorial markers." | | 18 | "From the other side, she heard the heavy click of bolts sliding home, followed by silence that felt intentional." | | 19 | "The figure smiled, revealing teeth that were too sharp and too many." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |