| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1423 | | 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) | |
| 61.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1423 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "vibrated" | | 1 | "traced" | | 2 | "warmth" | | 3 | "scanned" | | 4 | "gloom" | | 5 | "stark" | | 6 | "echoed" | | 7 | "silence" | | 8 | "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 | 257 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 0 | | narrationSentences | 257 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 257 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 18 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1416 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1416 | | uniqueNames | 15 | | maxNameDensity | 0.99 | | worstName | "Julian" | | maxWindowNameDensity | 2 | | worstWindowName | "Julian" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 3 | | Aurora | 11 | | Soho | 2 | | University | 1 | | Julian | 14 | | Golden | 1 | | Empress | 1 | | Yu-Fei | 1 | | Cardiff | 2 | | Rory | 2 | | Silas | 9 | | You | 12 | | Evan | 3 |
| | persons | | 0 | "Aurora" | | 1 | "University" | | 2 | "Julian" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "You" | | 6 | "Evan" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Soho" | | 3 | "Golden" | | 4 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 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 | 1416 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 257 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 96 | | mean | 14.75 | | std | 15.72 | | cv | 1.066 | | sampleLengths | | 0 | 60 | | 1 | 59 | | 2 | 49 | | 3 | 9 | | 4 | 60 | | 5 | 8 | | 6 | 26 | | 7 | 1 | | 8 | 35 | | 9 | 2 | | 10 | 5 | | 11 | 50 | | 12 | 17 | | 13 | 11 | | 14 | 11 | | 15 | 20 | | 16 | 2 | | 17 | 9 | | 18 | 24 | | 19 | 19 | | 20 | 20 | | 21 | 7 | | 22 | 14 | | 23 | 4 | | 24 | 26 | | 25 | 39 | | 26 | 3 | | 27 | 2 | | 28 | 3 | | 29 | 3 | | 30 | 14 | | 31 | 16 | | 32 | 30 | | 33 | 5 | | 34 | 12 | | 35 | 5 | | 36 | 6 | | 37 | 39 | | 38 | 5 | | 39 | 10 | | 40 | 1 | | 41 | 21 | | 42 | 28 | | 43 | 5 | | 44 | 1 | | 45 | 17 | | 46 | 12 | | 47 | 3 | | 48 | 13 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 257 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 274 | | matches | | 0 | "was sleeping" | | 1 | "was delivering" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 257 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1423 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.016865776528460996 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014054813773717498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 257 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 257 | | mean | 5.51 | | std | 3.03 | | cv | 0.549 | | sampleLengths | | 0 | 18 | | 1 | 15 | | 2 | 15 | | 3 | 12 | | 4 | 9 | | 5 | 10 | | 6 | 11 | | 7 | 15 | | 8 | 14 | | 9 | 5 | | 10 | 8 | | 11 | 14 | | 12 | 12 | | 13 | 10 | | 14 | 2 | | 15 | 7 | | 16 | 3 | | 17 | 13 | | 18 | 4 | | 19 | 5 | | 20 | 4 | | 21 | 9 | | 22 | 9 | | 23 | 2 | | 24 | 11 | | 25 | 2 | | 26 | 6 | | 27 | 3 | | 28 | 6 | | 29 | 7 | | 30 | 10 | | 31 | 1 | | 32 | 4 | | 33 | 11 | | 34 | 5 | | 35 | 4 | | 36 | 11 | | 37 | 2 | | 38 | 5 | | 39 | 3 | | 40 | 6 | | 41 | 2 | | 42 | 5 | | 43 | 8 | | 44 | 7 | | 45 | 11 | | 46 | 8 | | 47 | 11 | | 48 | 6 | | 49 | 7 |
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| 36.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.23346303501945526 | | totalSentences | 257 | | uniqueOpeners | 60 | |
| 45.87% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 218 | | matches | | 0 | "Just for a weekend." | | 1 | "Then fight for yourself." | | 2 | "Then don't think." |
| | ratio | 0.014 | |
| 14.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 112 | | totalSentences | 218 | | matches | | 0 | "She traced the rim of" | | 1 | "She wore a black hoodie" | | 2 | "Her straight black hair fell" | | 3 | "His left leg dragged slightly" | | 4 | "He polished a tumbler with" | | 5 | "He set a bottle of" | | 6 | "He turned away to wipe" | | 7 | "He shook a wet umbrella." | | 8 | "He scanned the room, eyes" | | 9 | "His gaze landed on the" | | 10 | "Her hand froze on the" | | 11 | "His shoes squeaked on the" | | 12 | "He stopped two feet from" | | 13 | "He leaned in, squinting against" | | 14 | "She did not turn." | | 15 | "She looked at the reflection" | | 16 | "His face had filled out." | | 17 | "He wore a suit that" | | 18 | "I thought you were dead." | | 19 | "His hand hovered near her" |
| | ratio | 0.514 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 201 | | totalSentences | 218 | | matches | | 0 | "The green neon sign above" | | 1 | "Maps from forgotten campaigns covered" | | 2 | "Aurora sat at the far" | | 3 | "She traced the rim of" | | 4 | "The crescent scar on her" | | 5 | "She wore a black hoodie" | | 6 | "Her straight black hair fell" | | 7 | "Silas moved behind the bar." | | 8 | "His left leg dragged slightly" | | 9 | "He polished a tumbler with" | | 10 | "The silver signet ring on" | | 11 | "He set a bottle of" | | 12 | "He turned away to wipe" | | 13 | "The door opened." | | 14 | "A gust of wind from" | | 15 | "A man stepped inside." | | 16 | "He shook a wet umbrella." | | 17 | "Water spotted the floorboards." | | 18 | "He scanned the room, eyes" | | 19 | "His gaze landed on the" |
| | ratio | 0.922 | |
| 45.87% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 218 | | matches | | 0 | "Before the fear started." | | 1 | "Because you bring Cardiff with" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "The green neon sign above the door buzzed with a low, insectile hum that vibrated in the teeth." |
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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 | |