| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1168 | | 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) | |
| 82.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1168 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "clandestine" | | 1 | "pulse" | | 2 | "jaw clenched" | | 3 | "pulsed" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 153 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 153 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 165 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1168 | | ratio | 0.002 | | matches | | 0 | "unexplained circumstances" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1024 | | uniqueNames | 20 | | maxNameDensity | 1.56 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Quinn | 1 | | Herrera | 1 | | Saint | 2 | | Christopher | 2 | | Berwick | 1 | | Street | 1 | | Morris | 3 | | Wardour | 1 | | Tomás | 12 | | Tube | 3 | | Camden | 3 | | Veil | 1 | | Market | 1 | | Rain | 5 | | Harlow | 16 | | Water | 3 | | Protocol | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Tomás" | | 8 | "Camden" | | 9 | "Rain" | | 10 | "Harlow" | | 11 | "Water" | | 12 | "Protocol" |
| | places | | 0 | "Soho" | | 1 | "Berwick" | | 2 | "Street" |
| | globalScore | 0.719 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.856 | | wordCount | 1168 | | matches | | 0 | "not from fire but from pressure, like the air before a storm" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 165 | | matches | | 0 | "knew that door" | | 1 | "carried that name" | | 2 | "seen that scar" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 22.46 | | std | 19.06 | | cv | 0.849 | | sampleLengths | | 0 | 10 | | 1 | 60 | | 2 | 3 | | 3 | 47 | | 4 | 4 | | 5 | 46 | | 6 | 3 | | 7 | 5 | | 8 | 4 | | 9 | 43 | | 10 | 4 | | 11 | 55 | | 12 | 68 | | 13 | 13 | | 14 | 14 | | 15 | 5 | | 16 | 4 | | 17 | 8 | | 18 | 40 | | 19 | 43 | | 20 | 38 | | 21 | 4 | | 22 | 58 | | 23 | 39 | | 24 | 27 | | 25 | 6 | | 26 | 42 | | 27 | 18 | | 28 | 13 | | 29 | 52 | | 30 | 42 | | 31 | 25 | | 32 | 5 | | 33 | 11 | | 34 | 10 | | 35 | 27 | | 36 | 6 | | 37 | 22 | | 38 | 4 | | 39 | 2 | | 40 | 54 | | 41 | 12 | | 42 | 16 | | 43 | 23 | | 44 | 19 | | 45 | 18 | | 46 | 14 | | 47 | 50 | | 48 | 16 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 192 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 165 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1030 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.010679611650485437 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000970873786407767 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 165 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 165 | | mean | 7.08 | | std | 4.5 | | cv | 0.635 | | sampleLengths | | 0 | 10 | | 1 | 10 | | 2 | 19 | | 3 | 4 | | 4 | 13 | | 5 | 7 | | 6 | 7 | | 7 | 3 | | 8 | 6 | | 9 | 7 | | 10 | 9 | | 11 | 12 | | 12 | 13 | | 13 | 4 | | 14 | 4 | | 15 | 2 | | 16 | 4 | | 17 | 4 | | 18 | 11 | | 19 | 9 | | 20 | 12 | | 21 | 3 | | 22 | 3 | | 23 | 2 | | 24 | 4 | | 25 | 5 | | 26 | 5 | | 27 | 6 | | 28 | 18 | | 29 | 9 | | 30 | 4 | | 31 | 4 | | 32 | 8 | | 33 | 2 | | 34 | 4 | | 35 | 3 | | 36 | 3 | | 37 | 7 | | 38 | 14 | | 39 | 10 | | 40 | 6 | | 41 | 6 | | 42 | 10 | | 43 | 5 | | 44 | 3 | | 45 | 3 | | 46 | 13 | | 47 | 8 | | 48 | 2 | | 49 | 1 |
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| 45.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3231707317073171 | | totalSentences | 164 | | uniqueOpeners | 53 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 90.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 139 | | matches | | 0 | "She knew that door." | | 1 | "She had watched it for" | | 2 | "He glanced over his shoulder" | | 3 | "Her boots struck puddles." | | 4 | "Her salt-and-pepper crop dripped." | | 5 | "Her sharp jaw set." | | 6 | "Her bearing held military precision" | | 7 | "His shoulders tightened." | | 8 | "Her breath cut the cold." | | 9 | "His trainers slapped water and" | | 10 | "He did not stop." | | 11 | "He vaulted a crate and" | | 12 | "Her knee twinged." | | 13 | "She ignored it." | | 14 | "She kept pace." | | 15 | "She had seen that scar" | | 16 | "He ran like a man" | | 17 | "Her hand brushed air where" | | 18 | "He turned his head." | | 19 | "His voice cracked through rain." |
| | ratio | 0.324 | |
| 10.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 139 | | matches | | 0 | "The Raven's Nest bled green" | | 1 | "Harlow Quinn stood opposite, collar" | | 2 | "She knew that door." | | 3 | "The maps and the black-and-white" | | 4 | "A hidden back room for" | | 5 | "She had watched it for" | | 6 | "The bookshelf moved." | | 7 | "Tomás Herrera slipped through the" | | 8 | "Olive skin shone damp under" | | 9 | "The Saint Christopher medallion at" | | 10 | "He glanced over his shoulder" | | 11 | "Harlow crossed the street." | | 12 | "Her boots struck puddles." | | 13 | "Her salt-and-pepper crop dripped." | | 14 | "Her sharp jaw set." | | 15 | "The worn leather watch on" | | 16 | "Her bearing held military precision" | | 17 | "Tomás saw her." | | 18 | "His shoulders tightened." | | 19 | "Harlow broke into a sprint." |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 2 | | matches | | 0 | "Tomás cut left down Berwick Street, past shuttered stalls and bins that reeked of rot and fried oil." | | 1 | "Off-the-books medical care for people who did not bleed right." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |