| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.396 | | leniency | 0.792 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1209 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 54.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1209 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "perfect" | | 1 | "warmth" | | 2 | "pulse" | | 3 | "scanning" | | 4 | "traced" | | 5 | "shattered" | | 6 | "echoing" | | 7 | "whisper" | | 8 | "tension" | | 9 | "quickened" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 144 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 144 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1198 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 937 | | uniqueNames | 25 | | maxNameDensity | 1.07 | | worstName | "Silas" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Silas" | | discoveredNames | | Blackwood | 1 | | Cardiff | 2 | | Maps | 1 | | Taught | 1 | | Slight | 1 | | Mirror | 1 | | Silas | 10 | | Evan | 5 | | Law | 1 | | Uni | 1 | | East | 1 | | End | 1 | | Raven | 1 | | Nest | 1 | | Odd | 1 | | Foam | 2 | | Laila | 1 | | Euston | 1 | | Road | 1 | | Debts | 1 | | Yu-Fei | 1 | | Soho | 2 | | Morse | 1 | | Shoved | 1 | | Rory | 5 |
| | persons | | 0 | "Blackwood" | | 1 | "Silas" | | 2 | "Evan" | | 3 | "Law" | | 4 | "Raven" | | 5 | "Foam" | | 6 | "Laila" | | 7 | "Debts" | | 8 | "Yu-Fei" | | 9 | "Rory" |
| | places | | 0 | "Cardiff" | | 1 | "Uni" | | 2 | "East" | | 3 | "End" | | 4 | "Euston" | | 5 | "Road" | | 6 | "Soho" |
| | globalScore | 0.966 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | 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 | 1198 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 20.31 | | std | 11.3 | | cv | 0.556 | | sampleLengths | | 0 | 48 | | 1 | 12 | | 2 | 12 | | 3 | 38 | | 4 | 41 | | 5 | 15 | | 6 | 16 | | 7 | 15 | | 8 | 25 | | 9 | 43 | | 10 | 64 | | 11 | 15 | | 12 | 36 | | 13 | 11 | | 14 | 19 | | 15 | 15 | | 16 | 19 | | 17 | 22 | | 18 | 13 | | 19 | 22 | | 20 | 20 | | 21 | 25 | | 22 | 25 | | 23 | 31 | | 24 | 26 | | 25 | 15 | | 26 | 24 | | 27 | 33 | | 28 | 22 | | 29 | 14 | | 30 | 31 | | 31 | 16 | | 32 | 37 | | 33 | 16 | | 34 | 10 | | 35 | 16 | | 36 | 19 | | 37 | 27 | | 38 | 18 | | 39 | 19 | | 40 | 34 | | 41 | 8 | | 42 | 18 | | 43 | 13 | | 44 | 9 | | 45 | 8 | | 46 | 16 | | 47 | 10 | | 48 | 5 | | 49 | 20 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 144 | | matches | | |
| 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 | 175 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 612 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.024509803921568627 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 6.85 | | std | 4.56 | | cv | 0.667 | | sampleLengths | | 0 | 25 | | 1 | 23 | | 2 | 8 | | 3 | 2 | | 4 | 2 | | 5 | 12 | | 6 | 3 | | 7 | 9 | | 8 | 12 | | 9 | 2 | | 10 | 12 | | 11 | 3 | | 12 | 15 | | 13 | 13 | | 14 | 5 | | 15 | 4 | | 16 | 1 | | 17 | 5 | | 18 | 10 | | 19 | 5 | | 20 | 5 | | 21 | 6 | | 22 | 8 | | 23 | 7 | | 24 | 12 | | 25 | 9 | | 26 | 4 | | 27 | 23 | | 28 | 20 | | 29 | 8 | | 30 | 10 | | 31 | 18 | | 32 | 10 | | 33 | 12 | | 34 | 2 | | 35 | 2 | | 36 | 2 | | 37 | 10 | | 38 | 5 | | 39 | 9 | | 40 | 18 | | 41 | 4 | | 42 | 5 | | 43 | 6 | | 44 | 5 | | 45 | 10 | | 46 | 9 | | 47 | 8 | | 48 | 7 | | 49 | 2 |
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| 97.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5885714285714285 | | totalSentences | 175 | | uniqueOpeners | 103 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 65.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 119 | | matches | | 0 | "She rubbed her wrist where" | | 1 | "She glanced up." | | 2 | "Her throat tightened." | | 3 | "She'd been sixteen, all elbows" | | 4 | "He'd vanished two weeks later." | | 5 | "He poured without breaking gaze." | | 6 | "His beard twitched, trimmed neat" | | 7 | "She snatched the pint, foam" | | 8 | "He leaned on his good" | | 9 | "She swigged deep, warmth uncoiling" | | 10 | "He trailed, polishing a glass" | | 11 | "She snorted, but her fingers" | | 12 | "He set the glass down" | | 13 | "She snapped, scooping them sharp" | | 14 | "He slid back, wiping hands" | | 15 | "She froze mid-sip." | | 16 | "His hazel eyes narrowed, scanning" | | 17 | "He grunted, poured himself a" | | 18 | "She traced the bar's gouges" | | 19 | "He downed the shot, throat" |
| | ratio | 0.387 | |
| 48.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 119 | | matches | | 0 | "Rory slammed her delivery satchel" | | 1 | "She rubbed her wrist where" | | 2 | "A liver-spotted hand slid a" | | 3 | "The voice gravelled low, like" | | 4 | "She glanced up." | | 5 | "Hazel eyes locked on hers," | | 6 | "That silver signet ring glinted" | | 7 | "Her throat tightened." | | 8 | "She'd been sixteen, all elbows" | | 9 | "He'd vanished two weeks later." | | 10 | "Prague op, whispers said." | | 11 | "The name scraped out" | | 12 | "He poured without breaking gaze." | | 13 | "Foam crested perfect, no spill." | | 14 | "The glass hit the mat" | | 15 | "His beard twitched, trimmed neat" | | 16 | "She snatched the pint, foam" | | 17 | "Copper tang from the pipes" | | 18 | "He leaned on his good" | | 19 | "Maps on the walls rustled" |
| | ratio | 0.824 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "Rory slammed her delivery satchel onto the bar top, the zipper's rasp cutting through the low murmur of pints clinking and darts thudding into cork." | | 1 | "Childhood summers, he'd shown up at her da's office with files that smelled of wet ink and secrets." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 3 | | matches | | 0 | "He leaned, voice dropping under the jazz croon from the corner speakers" | | 1 | "She snorted, but her fingers gripped tighter" | | 2 | "The man sipped, eyes sliding to her wrist scar" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "She snapped (snap)" | | 1 | "He refilled (refil)" |
| | dialogueSentences | 53 | | tagDensity | 0.075 | | leniency | 0.151 | | rawRatio | 0.5 | | effectiveRatio | 0.075 | |