| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "His grip tightened barely [barely]" |
| | dialogueSentences | 59 | | tagDensity | 0.322 | | leniency | 0.644 | | rawRatio | 0.053 | | effectiveRatio | 0.034 | |
| 79.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1223 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "loosely" | | 1 | "slowly" | | 2 | "very" | | 3 | "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) | |
| 83.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1223 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "measured" | | 2 | "weight" |
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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 | 58 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1228 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 732 | | uniqueNames | 12 | | maxNameDensity | 0.96 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | London | 1 | | Moreau | 1 | | Eva | 3 | | Deptford | 1 | | Hackney | 1 | | Lucien | 7 | | Brick | 1 | | Lane | 1 | | Started | 1 | | Rory | 6 | | Six | 3 | | Ptolemy | 2 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Rory" | | 4 | "Ptolemy" |
| | places | | 0 | "London" | | 1 | "Deptford" | | 2 | "Hackney" | | 3 | "Brick" | | 4 | "Lane" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1228 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 21.54 | | std | 19.91 | | cv | 0.924 | | sampleLengths | | 0 | 49 | | 1 | 7 | | 2 | 38 | | 3 | 4 | | 4 | 4 | | 5 | 18 | | 6 | 5 | | 7 | 25 | | 8 | 9 | | 9 | 6 | | 10 | 45 | | 11 | 8 | | 12 | 10 | | 13 | 71 | | 14 | 7 | | 15 | 66 | | 16 | 16 | | 17 | 5 | | 18 | 3 | | 19 | 45 | | 20 | 18 | | 21 | 17 | | 22 | 22 | | 23 | 51 | | 24 | 22 | | 25 | 32 | | 26 | 4 | | 27 | 54 | | 28 | 5 | | 29 | 2 | | 30 | 1 | | 31 | 20 | | 32 | 5 | | 33 | 3 | | 34 | 35 | | 35 | 38 | | 36 | 18 | | 37 | 28 | | 38 | 69 | | 39 | 17 | | 40 | 7 | | 41 | 1 | | 42 | 3 | | 43 | 62 | | 44 | 26 | | 45 | 3 | | 46 | 3 | | 47 | 1 | | 48 | 38 | | 49 | 3 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | |
| 98.99% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 132 | | matches | | 0 | "was, dripping" | | 1 | "wasn't staying" |
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| 25.04% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 97 | | ratio | 0.041 | | matches | | 0 | "Rory worked them top to bottom, and by the second one she already knew who stood on the other side, because nobody else in London knocked like that — twice, unhurried, the sound of a man who'd never once wondered whether he'd be let in." | | 1 | "He came in the way he came into everywhere — a quick sweep, windows first, then exits, then the mess." | | 2 | "Not fast — Lucien never did anything fast unless there was a blade involved — but the flat was small and there wasn't much room for a man to cross it without ending up close." | | 3 | "Up close it was worse — the skin puckered where it should have been stitched and hadn't been." |
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| 97.74% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 728 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.042582417582417584 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008241758241758242 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 12.66 | | std | 10.91 | | cv | 0.862 | | sampleLengths | | 0 | 4 | | 1 | 45 | | 2 | 7 | | 3 | 20 | | 4 | 8 | | 5 | 10 | | 6 | 4 | | 7 | 4 | | 8 | 15 | | 9 | 3 | | 10 | 5 | | 11 | 16 | | 12 | 9 | | 13 | 9 | | 14 | 6 | | 15 | 9 | | 16 | 18 | | 17 | 18 | | 18 | 8 | | 19 | 10 | | 20 | 12 | | 21 | 2 | | 22 | 57 | | 23 | 7 | | 24 | 20 | | 25 | 11 | | 26 | 35 | | 27 | 16 | | 28 | 5 | | 29 | 3 | | 30 | 26 | | 31 | 3 | | 32 | 13 | | 33 | 3 | | 34 | 10 | | 35 | 8 | | 36 | 6 | | 37 | 5 | | 38 | 6 | | 39 | 22 | | 40 | 3 | | 41 | 11 | | 42 | 37 | | 43 | 3 | | 44 | 19 | | 45 | 17 | | 46 | 15 | | 47 | 4 | | 48 | 33 | | 49 | 3 |
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| 67.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4536082474226804 | | totalSentences | 97 | | uniqueOpeners | 44 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 52.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 50 | | matches | | 0 | "She opened the door on" | | 1 | "He held his cane loosely" | | 2 | "He tipped his head at" | | 3 | "He touched it like he'd" | | 4 | "She slid the chain and" | | 5 | "He came in the way" | | 6 | "He set the cane against" | | 7 | "He wasn't staying." | | 8 | "He never bothered with those." | | 9 | "She hadn't planned to say" | | 10 | "She'd planned a lot of" | | 11 | "He looked at the window," | | 12 | "She pressed her thumb into" | | 13 | "It came out flatter than" | | 14 | "He crossed the room." | | 15 | "He stopped a foot away." | | 16 | "His voice dropped, and the" | | 17 | "He caught her wrist when" | | 18 | "She stopped, because he hadn't" | | 19 | "His grip tightened, barely" |
| | ratio | 0.42 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 50 | | matches | | 0 | "Rory worked them top to" | | 1 | "She opened the door on" | | 2 | "Lucien Moreau filled the stairwell" | | 3 | "He held his cane loosely" | | 4 | "The ivory handle caught the" | | 5 | "He tipped his head at" | | 6 | "He touched it like he'd" | | 7 | "The amber eye stayed on" | | 8 | "The black one gave nothing," | | 9 | "Rory looked at him for" | | 10 | "She slid the chain and" | | 11 | "He came in the way" | | 12 | "Eva's flat gave him a" | | 13 | "Books stacked in towers that" | | 14 | "Ptolemy detached himself from the" | | 15 | "He set the cane against" | | 16 | "He wasn't staying." | | 17 | "Rory folded her arms" | | 18 | "He never bothered with those." | | 19 | "She hadn't planned to say" |
| | ratio | 0.72 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 6.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 4 | | matches | | 0 | "Six weeks and a phone that never rang, and now here he was, dripping on Eva's mat with a cut over his eye and that voice doing the thing it did, the thing that …" | | 1 | "Books stacked in towers that leaned on each other for support, a photocopied grimoire spread across the kitchen table with a mug holding it flat, index cards ta…" | | 2 | "She'd planned a lot of versions of this conversation over six weeks, most of them cooler, some of them cruel, and every one of them had involved her being the p…" | | 3 | "Rain smell, wet wool, and underneath it the thing that was just him, warm and faintly sulphurous, like a struck match in a cold room." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 59 | | tagDensity | 0.119 | | leniency | 0.237 | | rawRatio | 0.143 | | effectiveRatio | 0.034 | |