| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1046 | | 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) | |
| 85.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1046 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "electric" | | 1 | "velvet" | | 2 | "silence" |
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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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1046 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.22% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 986 | | uniqueNames | 11 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Morris" | | discoveredNames | | Bayham | 1 | | Street | 1 | | London | 1 | | Quinn | 11 | | Camden | 1 | | Town | 1 | | Deptford | 1 | | Morris | 6 | | Victorian | 1 | | Underground | 1 | | Heads | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Heads" |
| | places | | 0 | "Bayham" | | 1 | "Street" | | 2 | "London" | | 3 | "Camden" | | 4 | "Town" | | 5 | "Deptford" |
| | globalScore | 0.942 | | windowScore | 1 | |
| 43.62% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like teeth, actual teeth, arranged" | | 1 | "quite faces, some too smooth, some too sharp, all of them watching the woman with the sharp jaw and the wet coat like she'd wandered into someone's front room uninvited" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1046 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 78 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 27.53 | | std | 23.36 | | cv | 0.849 | | sampleLengths | | 0 | 19 | | 1 | 2 | | 2 | 64 | | 3 | 5 | | 4 | 41 | | 5 | 15 | | 6 | 17 | | 7 | 35 | | 8 | 15 | | 9 | 2 | | 10 | 60 | | 11 | 9 | | 12 | 61 | | 13 | 41 | | 14 | 12 | | 15 | 22 | | 16 | 5 | | 17 | 74 | | 18 | 42 | | 19 | 11 | | 20 | 72 | | 21 | 23 | | 22 | 5 | | 23 | 29 | | 24 | 8 | | 25 | 2 | | 26 | 4 | | 27 | 76 | | 28 | 55 | | 29 | 26 | | 30 | 8 | | 31 | 51 | | 32 | 53 | | 33 | 47 | | 34 | 20 | | 35 | 6 | | 36 | 4 | | 37 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 174 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03232323232323232 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005050505050505051 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 13.41 | | std | 11.46 | | cv | 0.854 | | sampleLengths | | 0 | 19 | | 1 | 2 | | 2 | 4 | | 3 | 19 | | 4 | 14 | | 5 | 22 | | 6 | 5 | | 7 | 5 | | 8 | 13 | | 9 | 16 | | 10 | 2 | | 11 | 10 | | 12 | 4 | | 13 | 4 | | 14 | 7 | | 15 | 17 | | 16 | 3 | | 17 | 14 | | 18 | 1 | | 19 | 17 | | 20 | 11 | | 21 | 4 | | 22 | 2 | | 23 | 2 | | 24 | 39 | | 25 | 7 | | 26 | 12 | | 27 | 9 | | 28 | 3 | | 29 | 13 | | 30 | 27 | | 31 | 3 | | 32 | 15 | | 33 | 8 | | 34 | 6 | | 35 | 27 | | 36 | 12 | | 37 | 5 | | 38 | 4 | | 39 | 13 | | 40 | 5 | | 41 | 41 | | 42 | 11 | | 43 | 22 | | 44 | 8 | | 45 | 15 | | 46 | 19 | | 47 | 11 | | 48 | 3 | | 49 | 19 |
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| 70.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.47435897435897434 | | totalSentences | 78 | | uniqueOpeners | 37 | |
| 51.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 65 | | matches | | 0 | "Somewhere behind her, the hooded" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 65 | | matches | | 0 | "He cut left down Bayham" | | 1 | "He vaulted a low wall" | | 2 | "She'd seen one before." | | 3 | "she shouted, not expecting an" | | 4 | "He glanced back." | | 5 | "He knew exactly what she" | | 6 | "he called back, voice cracking" | | 7 | "He put his head down" | | 8 | "He went through the gap" | | 9 | "She'd felt it once before," | | 10 | "She hadn't waited." | | 11 | "She still didn't know what" | | 12 | "She keyed her radio anyway." | | 13 | "She rounded the final bend" | | 14 | "He was getting away." | | 15 | "She pushed past the stall," | | 16 | "She stood at the threshold," | | 17 | "She went in after him." |
| | ratio | 0.277 | |
| 52.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 65 | | matches | | 0 | "Quinn's boots hit the pavement" | | 1 | "The suspect didn't stop." | | 2 | "He cut left down Bayham" | | 3 | "Quinn matched him stride for" | | 4 | "This one might not be." | | 5 | "He vaulted a low wall" | | 6 | "Quinn followed, palms scraping brick," | | 7 | "A token, bone-white, catching what" | | 8 | "She'd seen one before." | | 9 | "she shouted, not expecting an" | | 10 | "He glanced back." | | 11 | "He knew exactly what she" | | 12 | "he called back, voice cracking" | | 13 | "He put his head down" | | 14 | "A chain-link gate hung ajar," | | 15 | "He went through the gap" | | 16 | "She'd felt it once before," | | 17 | "She hadn't waited." | | 18 | "Morris had gone in first" | | 19 | "She still didn't know what" |
| | ratio | 0.815 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 8 | | matches | | 0 | "He cut left down Bayham Street, feet slapping through a puddle that threw silver arcs into the sodium light." | | 1 | "Quinn matched him stride for stride, rain needling her face, her lungs burning cold." | | 2 | "She'd felt it once before, three years ago, standing outside a warehouse in Deptford with Morris breathing beside her, radio crackling, telling her to wait for …" | | 3 | "The official report was a lie written by people who hadn't seen what she'd seen in the last transmission, a sound like nothing human, cut off mid-syllable." | | 4 | "The stairwell beyond swallowed the streetlight after four steps, and she pulled her torch from her coat, thumbed it on, watched the beam catch drifting dust and…" | | 5 | "Stalls lined the abandoned platform, canvas and driftwood and things she couldn't immediately name, stacked with jars, blades, ledgers bound in something that w…" | | 6 | "A woman near the nearest stall, face half-hidden under a hood stitched with small bones, tracked Quinn's approach with eyes that reflected the candlelight wrong…" | | 7 | "Quinn kept her eyes on the fleeing shape ahead, weaving deeper into the crowd, past a stall stacked with cages holding things that weren't birds, past a man hag…" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "he called back, voice cracking" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "he called back (call back)" | | 2 | "someone snapped (snap)" |
| | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.6 | | effectiveRatio | 0.5 | |