| 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 | 1346 | | 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) | |
| 70.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1346 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "aligned" | | 1 | "footsteps" | | 2 | "scanned" | | 3 | "traced" | | 4 | "silence" | | 5 | "eyebrow" | | 6 | "echoed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 225 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 225 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 225 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1346 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1346 | | uniqueNames | 14 | | maxNameDensity | 1.93 | | worstName | "You" | | maxWindowNameDensity | 4.5 | | worstWindowName | "You" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Rory | 15 | | Cardiff | 2 | | Scotch | 2 | | Yu-Fei | 1 | | Evan | 1 | | London | 2 | | Eva | 12 | | Paddington | 1 | | Brecon | 2 | | Hay-on-Wye | 1 | | You | 26 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Evan" | | 4 | "Eva" | | 5 | "You" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" | | 3 | "Paddington" | | 4 | "Brecon" | | 5 | "Hay-on-Wye" |
| | globalScore | 0.534 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 105 | | 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 | 1346 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 225 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 24.93 | | std | 20.78 | | cv | 0.834 | | sampleLengths | | 0 | 98 | | 1 | 30 | | 2 | 69 | | 3 | 5 | | 4 | 5 | | 5 | 3 | | 6 | 28 | | 7 | 3 | | 8 | 10 | | 9 | 14 | | 10 | 31 | | 11 | 13 | | 12 | 32 | | 13 | 3 | | 14 | 39 | | 15 | 22 | | 16 | 24 | | 17 | 30 | | 18 | 4 | | 19 | 35 | | 20 | 32 | | 21 | 24 | | 22 | 7 | | 23 | 30 | | 24 | 5 | | 25 | 5 | | 26 | 23 | | 27 | 37 | | 28 | 11 | | 29 | 33 | | 30 | 4 | | 31 | 56 | | 32 | 9 | | 33 | 43 | | 34 | 22 | | 35 | 17 | | 36 | 21 | | 37 | 30 | | 38 | 24 | | 39 | 62 | | 40 | 2 | | 41 | 66 | | 42 | 4 | | 43 | 7 | | 44 | 40 | | 45 | 5 | | 46 | 60 | | 47 | 6 | | 48 | 55 | | 49 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 225 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 267 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 225 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.028824833702882482 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003695491500369549 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 225 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 225 | | mean | 5.98 | | std | 4.26 | | cv | 0.712 | | sampleLengths | | 0 | 20 | | 1 | 11 | | 2 | 15 | | 3 | 15 | | 4 | 15 | | 5 | 22 | | 6 | 6 | | 7 | 13 | | 8 | 4 | | 9 | 1 | | 10 | 1 | | 11 | 5 | | 12 | 3 | | 13 | 14 | | 14 | 21 | | 15 | 6 | | 16 | 7 | | 17 | 14 | | 18 | 4 | | 19 | 5 | | 20 | 5 | | 21 | 3 | | 22 | 5 | | 23 | 15 | | 24 | 6 | | 25 | 1 | | 26 | 1 | | 27 | 3 | | 28 | 5 | | 29 | 5 | | 30 | 3 | | 31 | 7 | | 32 | 1 | | 33 | 1 | | 34 | 2 | | 35 | 11 | | 36 | 8 | | 37 | 6 | | 38 | 6 | | 39 | 5 | | 40 | 2 | | 41 | 6 | | 42 | 8 | | 43 | 8 | | 44 | 4 | | 45 | 7 | | 46 | 2 | | 47 | 2 | | 48 | 1 | | 49 | 3 |
| |
| 40.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 30 | | diversityRatio | 0.32 | | totalSentences | 225 | | uniqueOpeners | 72 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 191 | | matches | (empty) | | ratio | 0 | |
| 31.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 90 | | totalSentences | 191 | | matches | | 0 | "She stacked three clean tumblers," | | 1 | "Her hair, once a wild" | | 2 | "She scanned the room, eyes" | | 3 | "They landed on Rory." | | 4 | "You kept the Cardiff accent." | | 5 | "You lost yours." | | 6 | "She slid onto the third" | | 7 | "Her fingers tapped the bar" | | 8 | "You stopped answering your phone." | | 9 | "I buried it." | | 10 | "She pushed it across the" | | 11 | "You used to drink cider." | | 12 | "I used to do a" | | 13 | "Her knuckles stood white." | | 14 | "I’m still here." | | 15 | "She took a slow drink." | | 16 | "You’re wiping tables in a" | | 17 | "You were supposed to be" | | 18 | "She dried her hands on" | | 19 | "I deliver noodles for Yu-Fei" |
| | ratio | 0.471 | |
| 41.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 160 | | totalSentences | 191 | | matches | | 0 | "The green neon bled through" | | 1 | "Rory wiped down the mahogany" | | 2 | "The place smelled of lemon" | | 3 | "She stacked three clean tumblers," | | 4 | "The bell above the door" | | 5 | "A draft swept in, carrying" | | 6 | "Footsteps crossed the threshold." | | 7 | "Rory looked up." | | 8 | "The woman stood just inside" | | 9 | "Her hair, once a wild" | | 10 | "A silver watch caught the" | | 11 | "She scanned the room, eyes" | | 12 | "They landed on Rory." | | 13 | "The breath left Rory’s lungs." | | 14 | "You kept the Cardiff accent." | | 15 | "You lost yours." | | 16 | "The woman unbuttoned her coat." | | 17 | "She slid onto the third" | | 18 | "Her fingers tapped the bar" | | 19 | "You stopped answering your phone." |
| | ratio | 0.838 | |
| 52.36% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 191 | | matches | | 0 | "Now you hand out noodles" | | 1 | "If any of it was" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |