| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1929 | | totalAiIsmAdverbs | 3 | | 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) | |
| 40.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1929 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "familiar" | | 1 | "flicker" | | 2 | "silence" | | 3 | "weight" | | 4 | "fractured" | | 5 | "traced" | | 6 | "glinting" | | 7 | "flickered" | | 8 | "unspoken" | | 9 | "unravel" | | 10 | "pulse" | | 11 | "footsteps" | | 12 | "tension" | | 13 | "flicked" | | 14 | "calculated" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 231 | | matches | (empty) | |
| 87.20% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 1 | | narrationSentences | 231 | | filterMatches | | 0 | "look" | | 1 | "think" | | 2 | "hear" | | 3 | "see" | | 4 | "see look" | | 5 | "wonder" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 231 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1929 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1929 | | uniqueNames | 18 | | maxNameDensity | 1.04 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Cardiff | 3 | | Rory | 20 | | Evan | 3 | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Eva | 16 | | Blackstone | 1 | | Europe | 1 | | London | 2 | | Thames | 1 | | Silas | 9 | | Carter | 1 | | European | 1 | | Seven | 3 | | You | 7 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Evan" | | 3 | "Eva" | | 4 | "Silas" | | 5 | "Carter" | | 6 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "Soho" | | 2 | "Europe" | | 3 | "London" | | 4 | "Thames" |
| | globalScore | 0.982 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 152 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like every evening she had spent u" | | 1 | "felt like another country now" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1929 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 231 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 42.87 | | std | 23.83 | | cv | 0.556 | | sampleLengths | | 0 | 79 | | 1 | 79 | | 2 | 1 | | 3 | 46 | | 4 | 73 | | 5 | 48 | | 6 | 1 | | 7 | 11 | | 8 | 57 | | 9 | 14 | | 10 | 24 | | 11 | 25 | | 12 | 62 | | 13 | 52 | | 14 | 10 | | 15 | 46 | | 16 | 21 | | 17 | 45 | | 18 | 67 | | 19 | 54 | | 20 | 82 | | 21 | 25 | | 22 | 68 | | 23 | 18 | | 24 | 11 | | 25 | 43 | | 26 | 51 | | 27 | 37 | | 28 | 19 | | 29 | 73 | | 30 | 91 | | 31 | 59 | | 32 | 15 | | 33 | 41 | | 34 | 66 | | 35 | 57 | | 36 | 18 | | 37 | 52 | | 38 | 74 | | 39 | 45 | | 40 | 15 | | 41 | 20 | | 42 | 38 | | 43 | 38 | | 44 | 58 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 231 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 321 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 231 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1929 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 58 | | adverbRatio | 0.03006739243131156 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00414722654224987 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 231 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 231 | | mean | 8.35 | | std | 5.21 | | cv | 0.624 | | sampleLengths | | 0 | 9 | | 1 | 12 | | 2 | 16 | | 3 | 8 | | 4 | 17 | | 5 | 17 | | 6 | 9 | | 7 | 9 | | 8 | 7 | | 9 | 7 | | 10 | 10 | | 11 | 21 | | 12 | 11 | | 13 | 5 | | 14 | 1 | | 15 | 4 | | 16 | 5 | | 17 | 4 | | 18 | 6 | | 19 | 27 | | 20 | 4 | | 21 | 5 | | 22 | 7 | | 23 | 2 | | 24 | 5 | | 25 | 21 | | 26 | 1 | | 27 | 11 | | 28 | 17 | | 29 | 4 | | 30 | 7 | | 31 | 13 | | 32 | 5 | | 33 | 19 | | 34 | 1 | | 35 | 11 | | 36 | 4 | | 37 | 9 | | 38 | 8 | | 39 | 1 | | 40 | 1 | | 41 | 18 | | 42 | 6 | | 43 | 4 | | 44 | 6 | | 45 | 14 | | 46 | 7 | | 47 | 9 | | 48 | 8 | | 49 | 2 |
| |
| 61.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3939393939393939 | | totalSentences | 231 | | uniqueOpeners | 91 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 205 | | matches | | 0 | "Then silence from him." | | 1 | "Then the firm swallowed me." | | 2 | "Instead I spend my days" | | 3 | "Only deeper now." | | 4 | "Instead she met Eva’s gaze" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 205 | | matches | | 0 | "She claimed the stool at" | | 1 | "Her fingers found their familiar" | | 2 | "His left leg dragged a" | | 3 | "She wrapped her hand around" | | 4 | "Her eyes widened then settled" | | 5 | "Her coat whispered against the" | | 6 | "His ring tapped the counter" | | 7 | "He knew when ghosts walked" | | 8 | "You look like you stepped" | | 9 | "His movements stayed economical." | | 10 | "He did not watch them" | | 11 | "He simply traded governments for" | | 12 | "I sent that one letter" | | 13 | "I told myself you were" | | 14 | "I pedal packages for the" | | 15 | "We swore we would tear" | | 16 | "You with your out of" | | 17 | "Me with every statute I" | | 18 | "His beard shifted as he" | | 19 | "He limped back to his" |
| | ratio | 0.249 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 164 | | totalSentences | 205 | | matches | | 0 | "Rory shoved through the door" | | 1 | "Rain clung to her jacket" | | 2 | "The green neon sign buzzed" | | 3 | "The air hung thick with" | | 4 | "She claimed the stool at" | | 5 | "Her fingers found their familiar" | | 6 | "Silas looked up from behind" | | 7 | "Hazel eyes narrowed against the" | | 8 | "The silver signet ring on" | | 9 | "His left leg dragged a" | | 10 | "The limp never left him." | | 11 | "Rory dipped her chin." | | 12 | "The glass slid her way." | | 13 | "Foam licked the rim." | | 14 | "She wrapped her hand around" | | 15 | "The small crescent scar on" | | 16 | "The door chimed again." | | 17 | "Heels clicked across the boards." | | 18 | "Rory glanced over and the" | | 19 | "The tilt of the chin." |
| | ratio | 0.8 | |
| 24.39% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 205 | | matches | | 0 | "Now I move through days" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 83 | | technicalSentenceCount | 3 | | matches | | 0 | "Hair that once exploded in wild curls now lay disciplined in a straight bob that gleamed like wet ink." | | 1 | "Instead I spend my days writing contracts that protect the very people we once hated." | | 2 | "The girl who quoted Blackstone like scripture now negotiated corporate escapes." |
| |
| 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 | |