| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.441 | | leniency | 0.882 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1426 | | 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) | |
| 64.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1426 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "dancing" | | 1 | "etched" | | 2 | "silence" | | 3 | "perfect" | | 4 | "shattered" | | 5 | "echoes" | | 6 | "whisper" | | 7 | "chilled" | | 8 | "echoed" | | 9 | "glint" |
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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 | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | |
| 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 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1419 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1086 | | uniqueNames | 22 | | maxNameDensity | 1.84 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 2 | | York | 1 | | Way | 1 | | London | 2 | | North | 1 | | Camden | 1 | | Miller | 11 | | Department | 1 | | Victorian | 1 | | Veil | 1 | | Market | 1 | | Harlow | 20 | | Morris | 2 | | Oxford | 1 | | Shadow | 1 | | Compass | 1 | | Shade | 1 | | Underground | 1 | | Eva | 3 | | Kowalski | 2 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Miller" | | 2 | "Department" | | 3 | "Harlow" | | 4 | "Morris" | | 5 | "Compass" | | 6 | "Eva" | | 7 | "Kowalski" |
| | places | | 0 | "York" | | 1 | "Way" | | 2 | "London" | | 3 | "Camden" | | 4 | "Victorian" | | 5 | "Oxford" |
| | globalScore | 0.579 | | windowScore | 0.667 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 4 | | matches | | 0 | "felt like wool in her ears" | | 1 | "looked like a subterranean plaza" | | 2 | "looked like a cluster of ramshackle stall" | | 3 | "looked like a crime scene she’d seen once" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.705 | | wordCount | 1419 | | matches | | 0 | "Not blood, but an ink-black ichor" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 27.82 | | std | 19.95 | | cv | 0.717 | | sampleLengths | | 0 | 66 | | 1 | 11 | | 2 | 64 | | 3 | 37 | | 4 | 46 | | 5 | 54 | | 6 | 36 | | 7 | 61 | | 8 | 44 | | 9 | 53 | | 10 | 5 | | 11 | 8 | | 12 | 69 | | 13 | 41 | | 14 | 39 | | 15 | 10 | | 16 | 11 | | 17 | 11 | | 18 | 45 | | 19 | 10 | | 20 | 24 | | 21 | 33 | | 22 | 34 | | 23 | 4 | | 24 | 36 | | 25 | 11 | | 26 | 59 | | 27 | 24 | | 28 | 63 | | 29 | 20 | | 30 | 4 | | 31 | 5 | | 32 | 46 | | 33 | 5 | | 34 | 16 | | 35 | 40 | | 36 | 24 | | 37 | 6 | | 38 | 26 | | 39 | 14 | | 40 | 7 | | 41 | 11 | | 42 | 42 | | 43 | 18 | | 44 | 5 | | 45 | 45 | | 46 | 36 | | 47 | 2 | | 48 | 25 | | 49 | 4 |
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| 95.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 103 | | matches | | 0 | "was etched" | | 1 | "was disturbed" | | 2 | "was backed" |
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| 45.86% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 173 | | matches | | 0 | "was turning" | | 1 | "was clutching" | | 2 | "was running was closing" |
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| 72.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 122 | | ratio | 0.025 | | matches | | 0 | "It wasn't broken; it was overloaded." | | 1 | "The man had been a Shade artisan—she recognised the elongated finger bones." | | 2 | "Deep in the tunnel, a high-pitched metallic screech echoed—the sound of a train that wasn't on the schedule." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 1 | | stackExamples | | 0 | "single, curly red hair" |
| | adverbCount | 13 | | adverbRatio | 0.013131313131313131 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005050505050505051 | |
| 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 | 11.63 | | std | 7.07 | | cv | 0.608 | | sampleLengths | | 0 | 22 | | 1 | 25 | | 2 | 19 | | 3 | 11 | | 4 | 2 | | 5 | 16 | | 6 | 26 | | 7 | 5 | | 8 | 15 | | 9 | 7 | | 10 | 11 | | 11 | 6 | | 12 | 1 | | 13 | 12 | | 14 | 20 | | 15 | 11 | | 16 | 15 | | 17 | 6 | | 18 | 9 | | 19 | 18 | | 20 | 21 | | 21 | 6 | | 22 | 4 | | 23 | 26 | | 24 | 9 | | 25 | 14 | | 26 | 18 | | 27 | 20 | | 28 | 14 | | 29 | 30 | | 30 | 5 | | 31 | 9 | | 32 | 17 | | 33 | 4 | | 34 | 2 | | 35 | 5 | | 36 | 11 | | 37 | 5 | | 38 | 8 | | 39 | 22 | | 40 | 6 | | 41 | 4 | | 42 | 3 | | 43 | 21 | | 44 | 13 | | 45 | 7 | | 46 | 4 | | 47 | 13 | | 48 | 10 | | 49 | 7 |
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| 52.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.371900826446281 | | totalSentences | 121 | | uniqueOpeners | 45 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | 0 | "Somewhere in those tunnels, Eva" |
| | ratio | 0.011 | |
| 79.34% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 91 | | matches | | 0 | "She didn't look at the" | | 1 | "Her gaze dropped to a" | | 2 | "It spun in a slow," | | 3 | "Her voice was a low" | | 4 | "She checked her worn leather" | | 5 | "He held a tablet tucked" | | 6 | "He gestured toward a doorway" | | 7 | "It was a jagged hole" | | 8 | "They stood in what looked" | | 9 | "It wasn't a body so" | | 10 | "His eyes were wide, the" | | 11 | "She lifted the victim’s right" | | 12 | "They were translucent." | | 13 | "She could see the white" | | 14 | "She stood up, her sharp" | | 15 | "She surveyed the stalls." | | 16 | "It was an occult researcher's" | | 17 | "Her eyes caught a flash" | | 18 | "She stepped past a stall" | | 19 | "She reached down and picked" |
| | ratio | 0.352 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 91 | | matches | | 0 | "Harlow Quinn stepped over the" | | 1 | "This deep beneath the surface," | | 2 | "She didn't look at the" | | 3 | "Her gaze dropped to a" | | 4 | "The casing had a heavy" | | 5 | "The needle didn't point North." | | 6 | "It spun in a slow," | | 7 | "Her voice was a low" | | 8 | "She checked her worn leather" | | 9 | "The exact time the silent" | | 10 | "DS Miller emerged from the" | | 11 | "He held a tablet tucked" | | 12 | "Miller was young, eager, and" | | 13 | "He gestured toward a doorway" | | 14 | "It was a jagged hole" | | 15 | "Harlow followed him through the" | | 16 | "The transition was physical." | | 17 | "The temperature plummeted, and the" | | 18 | "They stood in what looked" | | 19 | "This was the Veil Market," |
| | ratio | 0.846 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 91 | | matches | | 0 | "To the uninitiated, it looked" | | 1 | "To Harlow, it looked like" |
| | ratio | 0.022 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "Harlow Quinn clicked her flashlight on and descended into the dark, following the faint scent of sandalwood and the lingering vibration of a needle that wouldn'…" | | 1 | "Deep in the tunnel, a high-pitched metallic screech echoed—the sound of a train that wasn't on the schedule." | | 2 | "Not blood, but an ink-black ichor that smelled of old graves." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 5 | | matches | | 0 | "Harlow pulled, the snap of the latex loud in the cavernous space" | | 1 | "Miller stepped, his curiosity finally overriding his adherence to the robbery theory" | | 2 | "Miller shouted, his voice jumping an octave" | | 3 | "Harlow said, her voice dropping to a whisper that chilled Miller's spine" | | 4 | "Harlow ordered, her hand resting on the hilt of her flashlight" |
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| 2.94% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 5 | | fancyTags | | 0 | "Harlow muttered (mutter)" | | 1 | "Miller shouted (shout)" | | 2 | "Miller noted (note)" | | 3 | "Harlow corrected (correct)" | | 4 | "Harlow ordered (order)" |
| | dialogueSentences | 34 | | tagDensity | 0.265 | | leniency | 0.529 | | rawRatio | 0.556 | | effectiveRatio | 0.294 | |