| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 98.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2999 | | 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) | |
| 86.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2999 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "aligned" | | 1 | "weight" | | 2 | "stomach" | | 3 | "silence" | | 4 | "grave" | | 5 | "warmth" |
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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 | 130 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 130 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 305 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 141 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2999 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 33 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 103 | | wordCount | 1308 | | uniqueNames | 13 | | maxNameDensity | 2.98 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Silas | 15 | | Blackwood | 1 | | Price | 1 | | Camden | 1 | | Market | 1 | | Eva | 39 | | Rory | 36 | | Prague | 2 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Carter" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Price" | | 5 | "Market" | | 6 | "Eva" | | 7 | "Rory" | | 8 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Prague" |
| | globalScore | 0.009 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | 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 | 2999 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 305 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 248 | | mean | 12.09 | | std | 14.23 | | cv | 1.177 | | sampleLengths | | 0 | 50 | | 1 | 45 | | 2 | 7 | | 3 | 11 | | 4 | 14 | | 5 | 14 | | 6 | 8 | | 7 | 5 | | 8 | 62 | | 9 | 10 | | 10 | 8 | | 11 | 79 | | 12 | 28 | | 13 | 1 | | 14 | 7 | | 15 | 1 | | 16 | 3 | | 17 | 3 | | 18 | 39 | | 19 | 22 | | 20 | 2 | | 21 | 6 | | 22 | 2 | | 23 | 1 | | 24 | 21 | | 25 | 35 | | 26 | 9 | | 27 | 6 | | 28 | 5 | | 29 | 2 | | 30 | 4 | | 31 | 8 | | 32 | 6 | | 33 | 3 | | 34 | 14 | | 35 | 7 | | 36 | 4 | | 37 | 2 | | 38 | 1 | | 39 | 13 | | 40 | 14 | | 41 | 8 | | 42 | 11 | | 43 | 5 | | 44 | 18 | | 45 | 16 | | 46 | 16 | | 47 | 25 | | 48 | 11 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 208 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 305 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1311 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.013729977116704805 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 305 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 305 | | mean | 9.83 | | std | 11.06 | | cv | 1.125 | | sampleLengths | | 0 | 18 | | 1 | 13 | | 2 | 19 | | 3 | 7 | | 4 | 15 | | 5 | 7 | | 6 | 16 | | 7 | 7 | | 8 | 11 | | 9 | 14 | | 10 | 14 | | 11 | 8 | | 12 | 5 | | 13 | 17 | | 14 | 4 | | 15 | 13 | | 16 | 28 | | 17 | 10 | | 18 | 8 | | 19 | 4 | | 20 | 30 | | 21 | 10 | | 22 | 2 | | 23 | 2 | | 24 | 9 | | 25 | 22 | | 26 | 10 | | 27 | 8 | | 28 | 10 | | 29 | 1 | | 30 | 7 | | 31 | 1 | | 32 | 3 | | 33 | 3 | | 34 | 29 | | 35 | 10 | | 36 | 9 | | 37 | 13 | | 38 | 2 | | 39 | 6 | | 40 | 2 | | 41 | 1 | | 42 | 21 | | 43 | 8 | | 44 | 15 | | 45 | 12 | | 46 | 9 | | 47 | 6 | | 48 | 5 | | 49 | 2 |
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| 43.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.25901639344262295 | | totalSentences | 305 | | uniqueOpeners | 79 | |
| 54.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 122 | | matches | | 0 | "Then Eva pressed her lips" | | 1 | "Then grief moved across her" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 122 | | matches | | 0 | "His silver signet ring struck" | | 1 | "She hung her jacket on" | | 2 | "She set her glass down" | | 3 | "She aligned the coaster with" | | 4 | "He took in Eva's suit," | | 5 | "She put her hands around" | | 6 | "Her hand shook on the" | | 7 | "She hid it under the" | | 8 | "Her gold band caught the" | | 9 | "She reached for her coat" | | 10 | "Her coat slid from her" | | 11 | "She took three nuts and" | | 12 | "She shook the ice and" | | 13 | "He dried a glass that" | | 14 | "She coughed and reached for" | | 15 | "He left the bottle on" | | 16 | "She twisted the gold band." | | 17 | "Her eyes shone in the" | | 18 | "Her thumb pressed the crescent" | | 19 | "Her expression shifted into something" |
| | ratio | 0.172 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 115 | | totalSentences | 122 | | matches | | 0 | "The green neon sign above" | | 1 | "Rory Carter pushed inside with" | | 2 | "Maps covered the walls between" | | 3 | "Bottles stood in rows behind" | | 4 | "His silver signet ring struck" | | 5 | "Silas lifted his chin toward" | | 6 | "Rory shook rain from her" | | 7 | "She hung her jacket on" | | 8 | "A man in a wet" | | 9 | "A woman sat alone at" | | 10 | "The woman turned at the" | | 11 | "Rory stopped with one hand" | | 12 | "The hair came first." | | 13 | "Eva Price had worn hers" | | 14 | "This woman wore a blunt" | | 15 | "A charcoal suit kept its" | | 16 | "A thin gold band sat" | | 17 | "She set her glass down" | | 18 | "She aligned the coaster with" | | 19 | "Rory's mouth opened before thought" |
| | ratio | 0.943 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 2 | | matches | | 0 | "It reached her eyes and changed her face back toward the girl who had painted song lyrics on the inside of her school locker." | | 1 | "Rory felt the laugh hit a place under her ribs that had kept its shape for Eva through two years of messages and silence." |
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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 | |