| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1354 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 59.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1354 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "constructed" | | 1 | "crystal" | | 2 | "etched" | | 3 | "swept away" | | 4 | "perfect" | | 5 | "pristine" | | 6 | "racing" | | 7 | "scanned" | | 8 | "reminder" |
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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 | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1352 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 32.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 809 | | uniqueNames | 15 | | maxNameDensity | 2.35 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Town | 1 | | Tube | 1 | | Harlow | 19 | | Quinn | 1 | | Veil | 1 | | Market | 1 | | London | 1 | | Specialist | 1 | | Crime | 1 | | Directorate | 1 | | Eva | 13 | | Kowalski | 1 | | Kael | 3 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Directorate" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Kael" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Tube" | | 3 | "London" | | 4 | "Specialist" |
| | globalScore | 0.326 | | windowScore | 0.5 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 3 | | matches | | 0 | "seemed selective" | | 1 | "seemed muted less granular, as if it had been subjected to a sudden surge of energy" | | 2 | "It was as if the aging process had reversed in that one spot" |
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| 52.07% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.479 | | wordCount | 1352 | | matches | | 0 | "not for the obvious, but for the contradiction" | | 1 | "not to block a blow, but against the blinding, nauseating light" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 33.8 | | std | 22.03 | | cv | 0.652 | | sampleLengths | | 0 | 60 | | 1 | 64 | | 2 | 56 | | 3 | 35 | | 4 | 1 | | 5 | 8 | | 6 | 47 | | 7 | 38 | | 8 | 3 | | 9 | 34 | | 10 | 6 | | 11 | 25 | | 12 | 10 | | 13 | 30 | | 14 | 14 | | 15 | 47 | | 16 | 6 | | 17 | 43 | | 18 | 44 | | 19 | 55 | | 20 | 10 | | 21 | 28 | | 22 | 21 | | 23 | 71 | | 24 | 4 | | 25 | 16 | | 26 | 36 | | 27 | 65 | | 28 | 86 | | 29 | 36 | | 30 | 13 | | 31 | 51 | | 32 | 47 | | 33 | 52 | | 34 | 56 | | 35 | 4 | | 36 | 24 | | 37 | 8 | | 38 | 57 | | 39 | 41 |
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| 69.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 89 | | matches | | 0 | "been defaced" | | 1 | "was crouched" | | 2 | "were overturned" | | 3 | "was focused" | | 4 | "been opened" | | 5 | "been found" | | 6 | "were covered" | | 7 | "been subjected" | | 8 | "been found" |
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| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 132 | | matches | | 0 | "was studying" | | 1 | "was racing" | | 2 | "was starting" |
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| 92.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 113 | | ratio | 0.018 | | matches | | 0 | "The abandoned Camden Town Tube station smelled of wet stone, ozone, and something else—burnt cinnamon and cold iron." | | 1 | "The evidence on the surface didn’t add up—a violent theft with no perpetrator access." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 814 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 9 | | adverbRatio | 0.011056511056511056 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006142506142506142 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 11.96 | | std | 10.1 | | cv | 0.844 | | sampleLengths | | 0 | 18 | | 1 | 24 | | 2 | 18 | | 3 | 10 | | 4 | 16 | | 5 | 11 | | 6 | 18 | | 7 | 9 | | 8 | 24 | | 9 | 9 | | 10 | 23 | | 11 | 6 | | 12 | 18 | | 13 | 11 | | 14 | 1 | | 15 | 8 | | 16 | 18 | | 17 | 29 | | 18 | 4 | | 19 | 8 | | 20 | 9 | | 21 | 8 | | 22 | 9 | | 23 | 3 | | 24 | 18 | | 25 | 16 | | 26 | 5 | | 27 | 1 | | 28 | 25 | | 29 | 10 | | 30 | 23 | | 31 | 7 | | 32 | 4 | | 33 | 5 | | 34 | 5 | | 35 | 16 | | 36 | 14 | | 37 | 3 | | 38 | 5 | | 39 | 9 | | 40 | 6 | | 41 | 8 | | 42 | 11 | | 43 | 24 | | 44 | 20 | | 45 | 6 | | 46 | 4 | | 47 | 2 | | 48 | 6 | | 49 | 6 |
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| 51.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.36283185840707965 | | totalSentences | 113 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 81 | | matches | | 0 | "They nodded, their faces shadowed" | | 1 | "Her worn leather watch felt" | | 2 | "She tucked a curl of" | | 3 | "Her worn leather satchel, bulging" | | 4 | "She pulled on nitrile gloves." | | 5 | "It had been opened with" | | 6 | "She was studying the floor," | | 7 | "She crouched, mirroring Eva’s earlier" | | 8 | "She looked back at the" | | 9 | "She stood, frowning" | | 10 | "It wasn’t swept away." | | 11 | "It seemed muted, less granular," | | 12 | "It was as if the" | | 13 | "She thought of Morris, her" | | 14 | "She shook her head, a" | | 15 | "Her eyes scanned, not for" | | 16 | "She looked at Eva." | | 17 | "She pictured it." | | 18 | "It hadn’t saved him." | | 19 | "She looked at the strange" |
| | ratio | 0.247 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 81 | | matches | | 0 | "The abandoned Camden Town Tube" | | 1 | "Detective Harlow Quinn flashed the" | | 2 | "They nodded, their faces shadowed" | | 3 | "The Veil Market sprawled across" | | 4 | "Stalls constructed from salvaged wood" | | 5 | "The air hummed with low" | | 6 | "Harlow adjusted the strap of" | | 7 | "Her worn leather watch felt" | | 8 | "The crime scene was at" | | 9 | "A stall lay in disarray," | | 10 | "A uniformed officer from the" | | 11 | "Eva looked up as Harlow" | | 12 | "She tucked a curl of" | | 13 | "Her worn leather satchel, bulging" | | 14 | "Eva stood, brushing dust from" | | 15 | "Harlow surveyed the stall." | | 16 | "Shelves were overturned, but the" | | 17 | "A display case of delicate" | | 18 | "A rack of dried, unidentifiable" | | 19 | "The violence was focused on" |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "A uniformed officer from the Specialist Crime Directorate stood awkwardly next to Eva Kowalski, who was crouched, her nose almost touching the ground." | | 1 | "It seemed muted, less granular, as if it had been subjected to a sudden surge of energy." | | 2 | "A thief who bypassed easy valuables for one specific, enchanted item." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "Harlow said, but her mind was racing" | | 1 | "Harlow corrected, her voice tight" |
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| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow murmured (murmur)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow corrected (correct)" |
| | dialogueSentences | 40 | | tagDensity | 0.175 | | leniency | 0.35 | | rawRatio | 0.429 | | effectiveRatio | 0.15 | |