| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.292 | | leniency | 0.583 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1042 | | 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) | |
| 32.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1042 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "gloom" | | 1 | "flicker" | | 2 | "unreadable" | | 3 | "processed" | | 4 | "glinting" | | 5 | "etched" | | 6 | "furrowing" | | 7 | "intricate" | | 8 | "pulse" | | 9 | "database" | | 10 | "pulsed" | | 11 | "searing" | | 12 | "echoing" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | 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 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 9 | | totalWords | 1036 | | ratio | 0.009 | | matches | | 0 | "click" | | 1 | "Camden Town—Mind the Gap" | | 2 | "pushed" | | 3 | "click" | | 4 | "fuck" | | 5 | "thud" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.63% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 820 | | uniqueNames | 13 | | maxNameDensity | 1.71 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Camden | 2 | | Harlow | 1 | | Quinn | 14 | | Town | 1 | | Mind | 1 | | Priya | 10 | | Kapoor | 1 | | Italian | 1 | | Morris | 1 | | Met | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Priya" | | 3 | "Kapoor" | | 4 | "Morris" |
| | places | | | globalScore | 0.646 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1036 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 97 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 38.37 | | std | 22.64 | | cv | 0.59 | | sampleLengths | | 0 | 100 | | 1 | 36 | | 2 | 22 | | 3 | 50 | | 4 | 69 | | 5 | 26 | | 6 | 54 | | 7 | 58 | | 8 | 50 | | 9 | 48 | | 10 | 30 | | 11 | 53 | | 12 | 14 | | 13 | 47 | | 14 | 18 | | 15 | 62 | | 16 | 19 | | 17 | 6 | | 18 | 63 | | 19 | 61 | | 20 | 47 | | 21 | 10 | | 22 | 26 | | 23 | 7 | | 24 | 10 | | 25 | 29 | | 26 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 81 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 132 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 97 | | ratio | 0.082 | | matches | | 0 | "The beam of her torch cut through the gloom, illuminating the peeling paint of a sign that read *Camden Town—Mind the Gap* in faded letters." | | 1 | "The man’s coat was expensive—wool, not the kind you found in high-street shops." | | 2 | "It didn’t point at all—it spun lazily, as if drunk." | | 3 | "A tattoo peeked out—an intricate design of a serpent coiled around a dagger, the ink so fresh it still glistened." | | 4 | "Just dust and the faint, acrid tang of something that didn’t belong—like ozone after a storm." | | 5 | "Not a structural fissure—this was deliberate." | | 6 | "A man in a moth-eaten coat displayed rows of teeth—human, animal, something in between." | | 7 | "The abandoned Tube station was just that again—abandoned." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 830 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02891566265060241 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007228915662650603 | |
| 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 | 10.68 | | std | 6.77 | | cv | 0.634 | | sampleLengths | | 0 | 28 | | 1 | 22 | | 2 | 25 | | 3 | 13 | | 4 | 12 | | 5 | 8 | | 6 | 13 | | 7 | 7 | | 8 | 8 | | 9 | 19 | | 10 | 3 | | 11 | 22 | | 12 | 28 | | 13 | 15 | | 14 | 13 | | 15 | 10 | | 16 | 7 | | 17 | 10 | | 18 | 10 | | 19 | 4 | | 20 | 13 | | 21 | 13 | | 22 | 11 | | 23 | 12 | | 24 | 11 | | 25 | 5 | | 26 | 10 | | 27 | 5 | | 28 | 6 | | 29 | 18 | | 30 | 20 | | 31 | 14 | | 32 | 6 | | 33 | 5 | | 34 | 10 | | 35 | 19 | | 36 | 10 | | 37 | 9 | | 38 | 5 | | 39 | 8 | | 40 | 16 | | 41 | 6 | | 42 | 4 | | 43 | 5 | | 44 | 25 | | 45 | 6 | | 46 | 27 | | 47 | 7 | | 48 | 3 | | 49 | 1 |
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| 54.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36082474226804123 | | totalSentences | 97 | | uniqueOpeners | 35 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 75 | | matches | | 0 | "Just dust and the faint," |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 75 | | matches | | 0 | "It was between what the" | | 1 | "She held a clipboard, her" | | 2 | "His shoes were Italian leather," | | 3 | "She reached for it, her" | | 4 | "It didn’t point at all—it" | | 5 | "She nudged the man’s sleeve" | | 6 | "She’d seen that symbol before." | | 7 | "She stood abruptly, her torch" | | 8 | "She turned the compass over" | | 9 | "They were protective." | | 10 | "She followed its direction, her" | | 11 | "She pressed her palm against" | | 12 | "She knew this place." | | 13 | "She hissed, dropping it." | | 14 | "They turned and ran, the" |
| | ratio | 0.2 | |
| 53.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 75 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped off" | | 2 | "The beam of her torch" | | 3 | "The gap, in this case," | | 4 | "It was between what the" | | 5 | "DS Priya Kapoor emerged, her" | | 6 | "She held a clipboard, her" | | 7 | "Quinn adjusted the worn leather" | | 8 | "Priya gestured toward the far" | | 9 | "Quinn crouched beside the body," | | 10 | "The man’s coat was expensive—wool," | | 11 | "His shoes were Italian leather," | | 12 | "A man who worked with" | | 13 | "Priya said, flipping a page" | | 14 | "Quinn’s torch beam caught something" | | 15 | "She reached for it, her" | | 16 | "A small brass compass, its" | | 17 | "The needle didn’t point north." | | 18 | "It didn’t point at all—it" | | 19 | "Priya leaned in, her brow" |
| | ratio | 0.813 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 55.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 5 | | matches | | 0 | "The abandoned Tube station beneath Camden smelled of damp concrete and rust, the kind of scent that clung to the back of your throat like a bad memory." | | 1 | "The beam of her torch cut through the gloom, illuminating the peeling paint of a sign that read *Camden Town—Mind the Gap* in faded letters." | | 2 | "Just dust and the faint, acrid tang of something that didn’t belong—like ozone after a storm." | | 3 | "The air smelled of wet earth and something older, something that made the hairs on the back of Quinn’s neck stand on end." | | 4 | "Had heard whispers of it in the dark corners of the Met, in the hushed conversations of officers who’d seen things they couldn’t explain." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "She turned, the verdigris patina flaking under her fingers" | | 1 | "Priya said, her voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 24 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.25 | | effectiveRatio | 0.083 | |