| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "those Sunday mornings hit unexpectedly [unexpectedly]" |
| | dialogueSentences | 35 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.067 | | effectiveRatio | 0.057 | |
| 86.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 737 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 59.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 737 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "comfortable" | | 1 | "silence" | | 2 | "familiar" | | 3 | "flickered" |
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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 | 36 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 36 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 739 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 464 | | uniqueNames | 9 | | maxNameDensity | 1.51 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Wednesday | 1 | | Silas | 1 | | Evan | 5 | | Aurora | 7 | | Sunday | 1 | | Cardiff | 1 | | London | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Evan" | | 4 | "Aurora" | | 5 | "Cardiff" |
| | places | | | globalScore | 0.746 | | windowScore | 1 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 29 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like someone else's life" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 739 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 91.62% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 26.39 | | std | 12.42 | | cv | 0.471 | | sampleLengths | | 0 | 63 | | 1 | 34 | | 2 | 1 | | 3 | 35 | | 4 | 20 | | 5 | 20 | | 6 | 36 | | 7 | 16 | | 8 | 32 | | 9 | 16 | | 10 | 14 | | 11 | 28 | | 12 | 14 | | 13 | 37 | | 14 | 12 | | 15 | 29 | | 16 | 37 | | 17 | 35 | | 18 | 24 | | 19 | 30 | | 20 | 10 | | 21 | 21 | | 22 | 25 | | 23 | 29 | | 24 | 45 | | 25 | 29 | | 26 | 12 | | 27 | 35 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 36 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 72 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 56 | | ratio | 0.054 | | matches | | 0 | "The Raven's Nest held its usual Wednesday night crowd - a scattered handful of regulars nursing their drinks in comfortable silence." | | 1 | "\"They weren't awful.\" The memory of those Sunday mornings hit unexpectedly hard - sharing pastries, planning futures that now felt like someone else's life." | | 2 | "They talked about safe things then - mutual acquaintances, office politics, the changing face of Cardiff's city center." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 462 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.0367965367965368 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.010822510822510822 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 13.2 | | std | 6.54 | | cv | 0.496 | | sampleLengths | | 0 | 21 | | 1 | 24 | | 2 | 18 | | 3 | 19 | | 4 | 15 | | 5 | 1 | | 6 | 20 | | 7 | 15 | | 8 | 10 | | 9 | 10 | | 10 | 15 | | 11 | 5 | | 12 | 19 | | 13 | 6 | | 14 | 11 | | 15 | 14 | | 16 | 2 | | 17 | 16 | | 18 | 16 | | 19 | 5 | | 20 | 11 | | 21 | 14 | | 22 | 6 | | 23 | 22 | | 24 | 6 | | 25 | 8 | | 26 | 11 | | 27 | 26 | | 28 | 9 | | 29 | 3 | | 30 | 16 | | 31 | 13 | | 32 | 18 | | 33 | 19 | | 34 | 16 | | 35 | 19 | | 36 | 24 | | 37 | 17 | | 38 | 13 | | 39 | 3 | | 40 | 7 | | 41 | 10 | | 42 | 8 | | 43 | 3 | | 44 | 14 | | 45 | 11 | | 46 | 7 | | 47 | 22 | | 48 | 18 | | 49 | 27 |
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| 74.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5 | | totalSentences | 56 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 36 | | matches | | 0 | "He turned at her voice," | | 1 | "His briefcase hit the floor" | | 2 | "She squared her shoulders, grateful" | | 3 | "He loosened his tie" | | 4 | "He took a sip, eyes" | | 5 | "Her laugh came out sharp" | | 6 | "He stared into his scotch" | | 7 | "She grabbed a fresh cloth," | | 8 | "His voice had lost its" | | 9 | "He reached for his wallet" | | 10 | "They talked about safe things" | | 11 | "She gathered their empty glasses" |
| | ratio | 0.333 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 36 | | matches | | 0 | "The Raven's Nest held its" | | 1 | "Aurora wiped down the bar" | | 2 | "The familiar motions of cleaning" | | 3 | "The door creaked open, letting" | | 4 | "Aurora's hand froze mid-wipe, the" | | 5 | "He turned at her voice," | | 6 | "The cocky grin she'd once" | | 7 | "His briefcase hit the floor" | | 8 | "She squared her shoulders, grateful" | | 9 | "Evan settled onto a barstool," | | 10 | "He loosened his tie" | | 11 | "Aurora poured his drink with" | | 12 | "He took a sip, eyes" | | 13 | "The word tasted bitter" | | 14 | "Aurora busied herself arranging clean" | | 15 | "Her laugh came out sharp" | | 16 | "Evan's fingers tightened around his" | | 17 | "The old anger bubbled up," | | 18 | "He stared into his scotch" | | 19 | "She grabbed a fresh cloth," |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "Through the window, the green neon sign cast shifting shadows across Evan's face, making him look older, more vulnerable." | | 1 | "But underneath ran currents of all they weren't saying: the apologies that came too late, the dreams they'd outgrown, the people they'd become in each other's a…" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "He took, eyes moving over the vintage maps on the walls" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |