| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1303 | | 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) | |
| 53.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1303 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "database" | | 1 | "pumping" | | 2 | "vibrated" | | 3 | "weight" | | 4 | "pulsed" | | 5 | "velvet" | | 6 | "could feel" | | 7 | "standard" | | 8 | "lilt" | | 9 | "echoed" | | 10 | "footsteps" |
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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 | 109 | | matches | (empty) | |
| 90.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1303 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1119 | | uniqueNames | 20 | | maxNameDensity | 0.8 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 9 | | Great | 1 | | Windmill | 1 | | Street | 2 | | Home | 1 | | Office | 1 | | Camden | 1 | | Wednesday | 1 | | November | 1 | | Carlisle | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 2 | | Northern | 1 | | Line | 1 | | London | 1 | | Tomás | 4 | | Herrera | 1 | | Morris | 1 | | Thames | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Tube" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Morris" |
| | places | | 0 | "Windmill" | | 1 | "Street" | | 2 | "Camden" | | 3 | "November" | | 4 | "Carlisle" | | 5 | "London" | | 6 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "knives that seemed to absorb light rather than reflect it" | | 1 | "sounded like a warning" |
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| 46.51% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.535 | | wordCount | 1303 | | matches | | 0 | "not out of deference but out of suspicion" | | 1 | "not amber like the market bulbs but cold, white" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 29.61 | | std | 25.23 | | cv | 0.852 | | sampleLengths | | 0 | 57 | | 1 | 2 | | 2 | 10 | | 3 | 74 | | 4 | 34 | | 5 | 6 | | 6 | 61 | | 7 | 2 | | 8 | 74 | | 9 | 40 | | 10 | 57 | | 11 | 50 | | 12 | 33 | | 13 | 59 | | 14 | 94 | | 15 | 56 | | 16 | 2 | | 17 | 66 | | 18 | 49 | | 19 | 47 | | 20 | 3 | | 21 | 2 | | 22 | 17 | | 23 | 7 | | 24 | 4 | | 25 | 24 | | 26 | 10 | | 27 | 1 | | 28 | 11 | | 29 | 31 | | 30 | 13 | | 31 | 14 | | 32 | 62 | | 33 | 53 | | 34 | 4 | | 35 | 19 | | 36 | 25 | | 37 | 11 | | 38 | 2 | | 39 | 44 | | 40 | 43 | | 41 | 9 | | 42 | 19 | | 43 | 2 |
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| 95.61% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 109 | | matches | | 0 | "been caught" | | 1 | "been oiled" | | 2 | "being pulled" |
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| 51.02% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 179 | | matches | | 0 | "was going" | | 1 | "was joking" | | 2 | "wasn't running" | | 3 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1120 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.017857142857142856 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004464285714285714 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 10.26 | | std | 8.11 | | cv | 0.791 | | sampleLengths | | 0 | 17 | | 1 | 22 | | 2 | 3 | | 3 | 3 | | 4 | 12 | | 5 | 2 | | 6 | 4 | | 7 | 6 | | 8 | 26 | | 9 | 5 | | 10 | 6 | | 11 | 37 | | 12 | 3 | | 13 | 5 | | 14 | 26 | | 15 | 3 | | 16 | 3 | | 17 | 21 | | 18 | 17 | | 19 | 8 | | 20 | 15 | | 21 | 2 | | 22 | 12 | | 23 | 2 | | 24 | 2 | | 25 | 7 | | 26 | 19 | | 27 | 14 | | 28 | 6 | | 29 | 5 | | 30 | 7 | | 31 | 13 | | 32 | 5 | | 33 | 7 | | 34 | 1 | | 35 | 14 | | 36 | 13 | | 37 | 13 | | 38 | 5 | | 39 | 26 | | 40 | 11 | | 41 | 2 | | 42 | 11 | | 43 | 12 | | 44 | 14 | | 45 | 2 | | 46 | 21 | | 47 | 3 | | 48 | 4 | | 49 | 3 |
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| 60.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.41732283464566927 | | totalSentences | 127 | | uniqueOpeners | 53 | |
| 35.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 94 | | matches | | 0 | "Further down, a man in" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 94 | | matches | | 0 | "She pushed harder." | | 1 | "Her lungs burned." | | 2 | "They never did with this" | | 3 | "She'd been tailing the informant" | | 4 | "He'd spotted her." | | 5 | "She'd stepped too close when" | | 6 | "He cut left onto Carlisle" | | 7 | "She clocked the scar running" | | 8 | "He turned again, this time" | | 9 | "He slipped through without hesitation," | | 10 | "Her fingers caught the wet" | | 11 | "She stepped through." | | 12 | "He pushed through the crowd" | | 13 | "She could feel it in" | | 14 | "She caught up to him" | | 15 | "He turned, and for the" | | 16 | "He wasn't running anymore." | | 17 | "He was waiting." | | 18 | "He glanced back the way" | | 19 | "His jaw tightened." |
| | ratio | 0.266 | |
| 45.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 94 | | matches | | 0 | "The soles of Harlow's shoes" | | 1 | "She pushed harder." | | 2 | "Her lungs burned." | | 3 | "The words meant nothing." | | 4 | "They never did with this" | | 5 | "She'd been tailing the informant" | | 6 | "Drugs that made bodies disappear." | | 7 | "Weapons that left no ballistic" | | 8 | "He'd spotted her." | | 9 | "Amateur mistake on her part." | | 10 | "She'd stepped too close when" | | 11 | "He cut left onto Carlisle" | | 12 | "Harlow matched the turn, her" | | 13 | "The rain tasted of petrol" | | 14 | "A delivery cyclist swore at" | | 15 | "The man in the dark" | | 16 | "The Saint Christopher medallion around" | | 17 | "She clocked the scar running" | | 18 | "A knife wound, old and" | | 19 | "Someone who'd been caught off" |
| | ratio | 0.83 | |
| 53.19% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 94 | | matches | | | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "And tonight, the tip that brought her to Camden on a filthy Wednesday in November, watching a wiry man in a dark jacket collect a parcel from a laundrette that …" | | 1 | "He cut left onto Carlisle Street, legs pumping with the desperate rhythm of someone who knew exactly where he was going." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "He said, the accent of someone raised somewhere warmer than London" | | 1 | "He grabbed, his grip firm, and pulled her toward the rightmost tunnel" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.045 | | leniency | 0.091 | | rawRatio | 0 | | effectiveRatio | 0 | |