| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1108 | | totalAiIsmAdverbs | 2 | | 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) | |
| 90.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1108 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1103 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1023 | | uniqueNames | 14 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Wardour | 1 | | Street | 3 | | Soho | 2 | | Saturday | 1 | | Raven | 1 | | Nest | 1 | | Friday | 1 | | Berwick | 2 | | Morris | 2 | | Gouda | 1 | | London | 1 | | Quinn | 8 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Raven" | | 1 | "Morris" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Berwick" | | 4 | "Gouda" | | 5 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a derelict station entrance" | | 1 | "looked like" | | 2 | "sounded like four different languages simu" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.907 | | wordCount | 1103 | | matches | | 0 | "Not the murmur of a crowd but the full dense noise of a marketplace at work, vendors calli" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 29.81 | | std | 29.18 | | cv | 0.979 | | sampleLengths | | 0 | 10 | | 1 | 39 | | 2 | 81 | | 3 | 6 | | 4 | 2 | | 5 | 102 | | 6 | 4 | | 7 | 19 | | 8 | 3 | | 9 | 61 | | 10 | 15 | | 11 | 48 | | 12 | 59 | | 13 | 33 | | 14 | 2 | | 15 | 64 | | 16 | 3 | | 17 | 59 | | 18 | 7 | | 19 | 54 | | 20 | 4 | | 21 | 110 | | 22 | 13 | | 23 | 27 | | 24 | 63 | | 25 | 31 | | 26 | 2 | | 27 | 8 | | 28 | 54 | | 29 | 7 | | 30 | 3 | | 31 | 25 | | 32 | 3 | | 33 | 35 | | 34 | 5 | | 35 | 10 | | 36 | 32 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 95 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 178 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 100 | | ratio | 0.01 | | matches | | 0 | "Underground access points existed all over London; the city ran on secrets buried beneath its feet." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1028 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.03501945525291829 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.010700389105058366 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 11.03 | | std | 9.42 | | cv | 0.854 | | sampleLengths | | 0 | 10 | | 1 | 4 | | 2 | 24 | | 3 | 4 | | 4 | 7 | | 5 | 18 | | 6 | 26 | | 7 | 11 | | 8 | 26 | | 9 | 6 | | 10 | 2 | | 11 | 17 | | 12 | 5 | | 13 | 14 | | 14 | 6 | | 15 | 27 | | 16 | 27 | | 17 | 6 | | 18 | 4 | | 19 | 19 | | 20 | 3 | | 21 | 14 | | 22 | 2 | | 23 | 5 | | 24 | 7 | | 25 | 11 | | 26 | 2 | | 27 | 1 | | 28 | 19 | | 29 | 9 | | 30 | 6 | | 31 | 14 | | 32 | 10 | | 33 | 20 | | 34 | 4 | | 35 | 19 | | 36 | 1 | | 37 | 18 | | 38 | 11 | | 39 | 10 | | 40 | 5 | | 41 | 28 | | 42 | 2 | | 43 | 6 | | 44 | 4 | | 45 | 27 | | 46 | 2 | | 47 | 2 | | 48 | 5 | | 49 | 1 |
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| 75.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.48 | | totalSentences | 100 | | uniqueOpeners | 48 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Entirely dark, no white visible," |
| | ratio | 0.013 | |
| 71.28% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 78 | | matches | | 0 | "It was the collar." | | 1 | "She pushed off the doorframe" | | 2 | "He was mid-thirties, dark jacket," | | 3 | "She'd been watching him leave" | | 4 | "He turned north onto Berwick" | | 5 | "Her shoes found every puddle." | | 6 | "She ignored the cold water" | | 7 | "She was already reading a" | | 8 | "He kept walking." | | 9 | "He cut through a narrow" | | 10 | "She counted to three and" | | 11 | "She'd walked them with Morris," | | 12 | "She couldn't read it in" | | 13 | "She could read him though." | | 14 | "He pulled something from his" | | 15 | "Her warrant card felt thin" | | 16 | "Her radio felt inadequate." | | 17 | "She stood in the rain" | | 18 | "She went in." | | 19 | "She heard it before she" |
| | ratio | 0.372 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 78 | | matches | | 0 | "Quinn spotted him the moment" | | 1 | "That was the tell." | | 2 | "It was the collar." | | 3 | "She pushed off the doorframe" | | 4 | "The Soho pavements were slick," | | 5 | "He was mid-thirties, dark jacket," | | 6 | "She'd been watching him leave" | | 7 | "He turned north onto Berwick" | | 8 | "The market stalls were long" | | 9 | "Her shoes found every puddle." | | 10 | "She ignored the cold water" | | 11 | "DS Morris had died three" | | 12 | "Quinn hadn't stopped opening doors" | | 13 | "The man glanced back." | | 14 | "She was already reading a" | | 15 | "He kept walking." | | 16 | "Running meant panic, meant attention." | | 17 | "Quinn matched it, hand drifting" | | 18 | "Neither would help her much" | | 19 | "He cut through a narrow" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 4 | | matches | | 0 | "Three weeks she'd spent building a case around that bar, around its patrons, around the strange parade of people who arrived looking frightened and left looking…" | | 1 | "The air changed as she descended, warmer, thicker, carrying the smell of tallow and something chemical beneath it, sharp and sweet in a way that caught at the b…" | | 2 | "It had been decommissioned long enough that the signage was illegible, the old platform tiles cracked and colonised by something that glowed faintly blue-green …" | | 3 | "Counted the exits she could see, four, counted the number of people between her and the suspect, too many, assessed the sight lines, poor, assessed her ability …" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "He glanced, a reflex rather than a gesture" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |