| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2174 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 54.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2174 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "traced" | | 1 | "weight" | | 2 | "familiar" | | 3 | "scanned" | | 4 | "tension" | | 5 | "pulsed" | | 6 | "rhythmic" | | 7 | "aligned" | | 8 | "flickered" | | 9 | "silence" | | 10 | "electric" | | 11 | "vibrated" | | 12 | "trembled" | | 13 | "etched" |
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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 | 284 | | matches | (empty) | |
| 82.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 12 | | hedgeCount | 0 | | narrationSentences | 284 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "think" | | 3 | "see" | | 4 | "hear" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 284 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 112 | | wordCount | 2174 | | uniqueNames | 22 | | maxNameDensity | 1.52 | | worstName | "You" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Cardiff | 4 | | Rory | 17 | | London | 3 | | Silas | 8 | | Eva | 19 | | Manchester | 2 | | Thames | 2 | | July | 1 | | Soho | 1 | | August | 1 | | Bristol | 1 | | Dagenham | 2 | | Central | 1 | | Oyster | 2 | | Brixton | 2 | | Boots | 2 | | You | 33 | | Three | 3 | | Drink | 3 | | Tell | 3 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "Boots" | | 5 | "You" | | 6 | "Drink" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Manchester" | | 3 | "Thames" | | 4 | "July" | | 5 | "Soho" | | 6 | "August" | | 7 | "Bristol" | | 8 | "Dagenham" | | 9 | "Brixton" | | 10 | "Tell" |
| | globalScore | 0.741 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 166 | | 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 | 2174 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 284 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 34.51 | | std | 19.76 | | cv | 0.573 | | sampleLengths | | 0 | 129 | | 1 | 25 | | 2 | 26 | | 3 | 95 | | 4 | 20 | | 5 | 5 | | 6 | 11 | | 7 | 4 | | 8 | 44 | | 9 | 20 | | 10 | 28 | | 11 | 19 | | 12 | 15 | | 13 | 27 | | 14 | 25 | | 15 | 19 | | 16 | 27 | | 17 | 27 | | 18 | 46 | | 19 | 12 | | 20 | 28 | | 21 | 31 | | 22 | 15 | | 23 | 40 | | 24 | 34 | | 25 | 18 | | 26 | 51 | | 27 | 21 | | 28 | 33 | | 29 | 26 | | 30 | 35 | | 31 | 26 | | 32 | 54 | | 33 | 39 | | 34 | 47 | | 35 | 47 | | 36 | 47 | | 37 | 36 | | 38 | 41 | | 39 | 18 | | 40 | 53 | | 41 | 15 | | 42 | 46 | | 43 | 41 | | 44 | 45 | | 45 | 45 | | 46 | 48 | | 47 | 54 | | 48 | 30 | | 49 | 29 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 284 | | matches | | 0 | "get found" | | 1 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 443 | | matches | | 0 | "was suffocating" | | 1 | "wasn’t chasing" | | 2 | "was breaking" | | 3 | "were falling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 284 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2176 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 60 | | adverbRatio | 0.027573529411764705 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0027573529411764708 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 284 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 284 | | mean | 7.65 | | std | 4.64 | | cv | 0.606 | | sampleLengths | | 0 | 14 | | 1 | 10 | | 2 | 12 | | 3 | 14 | | 4 | 18 | | 5 | 17 | | 6 | 11 | | 7 | 9 | | 8 | 13 | | 9 | 11 | | 10 | 3 | | 11 | 6 | | 12 | 4 | | 13 | 12 | | 14 | 5 | | 15 | 5 | | 16 | 12 | | 17 | 4 | | 18 | 25 | | 19 | 19 | | 20 | 7 | | 21 | 7 | | 22 | 14 | | 23 | 9 | | 24 | 14 | | 25 | 3 | | 26 | 3 | | 27 | 6 | | 28 | 8 | | 29 | 5 | | 30 | 5 | | 31 | 6 | | 32 | 4 | | 33 | 5 | | 34 | 6 | | 35 | 12 | | 36 | 13 | | 37 | 8 | | 38 | 6 | | 39 | 5 | | 40 | 9 | | 41 | 10 | | 42 | 10 | | 43 | 8 | | 44 | 5 | | 45 | 3 | | 46 | 6 | | 47 | 5 | | 48 | 9 | | 49 | 6 |
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| 30.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 55 | | diversityRatio | 0.22183098591549297 | | totalSentences | 284 | | uniqueOpeners | 63 | |
| 36.90% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 271 | | matches | | 0 | "Then you vanished." | | 1 | "Then we walk to Brixton." | | 2 | "Then stop digging graves for" |
| | ratio | 0.011 | |
| 19.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 136 | | totalSentences | 271 | | matches | | 0 | "He polished a tumbler with" | | 1 | "Her hair, once a wild" | | 2 | "She scanned the room, shoulders" | | 3 | "Her boots carried dried mud" | | 4 | "Her hands looked rough, nails" | | 5 | "Their eyes locked." | | 6 | "Her mouth opened, then snapped" | | 7 | "She stepped forward, boots heavy" | | 8 | "You look like a ghost." | | 9 | "You cut your hair." | | 10 | "She sat, kept her coat" | | 11 | "He nodded, poured a measure" | | 12 | "She swallowed, winced, and set" | | 13 | "You stopped answering your phone." | | 14 | "I thought you moved to" | | 15 | "I kept moving." | | 16 | "You know how it goes." | | 17 | "I know how it goes" | | 18 | "I do it for a" | | 19 | "She turned the whisky glass" |
| | ratio | 0.502 | |
| 30.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 233 | | totalSentences | 271 | | matches | | 0 | "The green neon tube above" | | 1 | "Rain drummed against the frosted" | | 2 | "Rory traced the crescent mark" | | 3 | "The wood of the bar" | | 4 | "Silas moved behind the counter," | | 5 | "He polished a tumbler with" | | 6 | "The silver band on his" | | 7 | "The air smelled of stale" | | 8 | "The hinge groaned." | | 9 | "A draught cut through the" | | 10 | "Boots scuffed the threshold." | | 11 | "Rory kept her chin down," | | 12 | "A familiar scent hit first." | | 13 | "The kind they used to" | | 14 | "Rory lifted her gaze." | | 15 | "The woman shaking out a" | | 16 | "Her hair, once a wild" | | 17 | "Dark hollows carved themselves beneath" | | 18 | "A silver chain bit into" | | 19 | "She scanned the room, shoulders" |
| | ratio | 0.86 | |
| 73.80% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 271 | | matches | | 0 | "Now you sit in a" | | 1 | "Even when it meant cutting" | | 2 | "To the storage unit in" | | 3 | "Even when I couldn’t keep" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 78 | | technicalSentenceCount | 3 | | matches | | 0 | "The woman shaking out a soaked trench coat bore little resemblance to the girl who had dragged Rory onto the London train three years prior." | | 1 | "You burned your life for a ghost that wasn’t chasing you." | | 2 | "I see the girl who used to hide law textbooks under her bed so her father wouldn’t lecture her at dinner." |
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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 | |