| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 33 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0.056 | | effectiveRatio | 0.056 | |
| 87.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1214 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "loosely" | | 1 | "softly" | | 2 | "really" |
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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) | |
| 67.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1214 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "measured" | | 1 | "fluttered" | | 2 | "stark" | | 3 | "scanning" | | 4 | "perfect" | | 5 | "etched" | | 6 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 64 | | matches | (empty) | |
| 75.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 80 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1214 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 75.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1002 | | uniqueNames | 17 | | maxNameDensity | 1.5 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Veil | 2 | | Market | 2 | | Transport | 1 | | London | 1 | | Tube | 1 | | Camden | 1 | | Eva | 10 | | Kowalski | 1 | | Hackney | 1 | | You | 2 | | British | 1 | | Museum | 1 | | Shoreditch | 1 | | Morris | 3 | | Station | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "You" | | 6 | "Morris" | | 7 | "Station" |
| | places | | | globalScore | 0.751 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.824 | | wordCount | 1214 | | matches | | 0 | "not north, but straight down at the body" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 31.13 | | std | 23.27 | | cv | 0.748 | | sampleLengths | | 0 | 61 | | 1 | 84 | | 2 | 54 | | 3 | 58 | | 4 | 6 | | 5 | 80 | | 6 | 4 | | 7 | 12 | | 8 | 33 | | 9 | 61 | | 10 | 9 | | 11 | 15 | | 12 | 38 | | 13 | 13 | | 14 | 4 | | 15 | 55 | | 16 | 16 | | 17 | 3 | | 18 | 76 | | 19 | 21 | | 20 | 42 | | 21 | 5 | | 22 | 29 | | 23 | 41 | | 24 | 18 | | 25 | 57 | | 26 | 30 | | 27 | 12 | | 28 | 9 | | 29 | 6 | | 30 | 39 | | 31 | 7 | | 32 | 45 | | 33 | 55 | | 34 | 18 | | 35 | 32 | | 36 | 8 | | 37 | 40 | | 38 | 18 |
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| 94.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 64 | | matches | | 0 | "was cordoned" | | 1 | "got filed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 177 | | matches | | 0 | "were escaping" | | 1 | "were beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 409 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.017114914425427872 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.009779951100244499 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 17.1 | | std | 18.11 | | cv | 1.059 | | sampleLengths | | 0 | 18 | | 1 | 30 | | 2 | 1 | | 3 | 12 | | 4 | 23 | | 5 | 34 | | 6 | 27 | | 7 | 22 | | 8 | 10 | | 9 | 17 | | 10 | 5 | | 11 | 58 | | 12 | 6 | | 13 | 80 | | 14 | 4 | | 15 | 3 | | 16 | 9 | | 17 | 5 | | 18 | 25 | | 19 | 3 | | 20 | 61 | | 21 | 9 | | 22 | 5 | | 23 | 3 | | 24 | 3 | | 25 | 4 | | 26 | 38 | | 27 | 13 | | 28 | 4 | | 29 | 3 | | 30 | 8 | | 31 | 29 | | 32 | 9 | | 33 | 6 | | 34 | 13 | | 35 | 3 | | 36 | 3 | | 37 | 76 | | 38 | 21 | | 39 | 42 | | 40 | 5 | | 41 | 29 | | 42 | 41 | | 43 | 18 | | 44 | 57 | | 45 | 30 | | 46 | 12 | | 47 | 1 | | 48 | 2 | | 49 | 6 |
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| 61.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.42857142857142855 | | totalSentences | 70 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 52 | | matches | | 0 | "Her boots struck each step" | | 1 | "She had the bone token" | | 2 | "It had let her past" | | 3 | "She stepped under the tape." | | 4 | "she said quietly" | | 5 | "You’re shouldn’t be" | | 6 | "She crouched again, examined the" | | 7 | "She stood, eyes moving from" | | 8 | "He was a regular." | | 9 | "He had the" | | 10 | "She reached out with a" | | 11 | "she said to the sergeant" | | 12 | "I want names, addresses, the" | | 13 | "She’d been looking for a" |
| | ratio | 0.269 | |
| 17.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 52 | | matches | | 0 | "The stairwell reeked of damp" | | 1 | "Her boots struck each step" | | 2 | "The full moon was high," | | 3 | "She had the bone token" | | 4 | "It had let her past" | | 5 | "The crime scene was cordoned" | | 6 | "A body lay on the" | | 7 | "She stepped under the tape." | | 8 | "she said quietly" | | 9 | "Eva Kowalski was already there," | | 10 | "You’re shouldn’t be" | | 11 | "The body has a British" | | 12 | "Quinn didn’t answer. She pulled" | | 13 | "Quinn asked the SOCO sergeant" | | 14 | "The evidence didn’t add up." | | 15 | "Eva stood, brushing dust from" | | 16 | "Quinn looked at her." | | 17 | "The cut is too clean" | | 18 | "Shade artisans use it to" | | 19 | "The Veil Market trades in" |
| | ratio | 0.885 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 52 | | matches | | | ratio | 0.019 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 8 | | matches | | 0 | "Now she stood in the abandoned Tube station beneath Camden, platforms cracked and slick with condensation, stalls shuttered with canvas that fluttered though th…" | | 1 | "The crime scene was cordoned with yellow tape that looked absurd against the arches and the low, humming lights strung between stalls." | | 2 | "Quinn didn’t answer. She pulled on gloves, knelt, and lifted the sheet an inch. The man was in his thirties, coat expensive, throat opened in a neat line. No sp…" | | 3 | "Quinn stood, scanning the stalls. Brass lanterns, jars of black liquid, a table of bone tokens arranged like coins. The place was a black market for enchanted g…" | | 4 | "She crouched again, examined the halo of blood. It was too perfect. The tile beneath it was cold, but the tile two feet away was colder, frosted with a thin rim…" | | 5 | "She stood, eyes moving from the body to the archway, to the stalls, to the constables who were beginning to look uneasy in a place that made their radios hiss." | | 6 | "Quinn stared at the corpse, then at the compass needle still quivering. She thought of Morris, of the report that said cardiac arrest, of the strange burn on hi…" | | 7 | "Quinn didn’t smile back. She watched the needle on the brass compass, etched with sigils, point unwaveringly at the body that wasn’t really a body. The evidence…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |