| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 79 | | tagDensity | 0.304 | | leniency | 0.608 | | rawRatio | 0.042 | | effectiveRatio | 0.025 | |
| 96.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1527 | | totalAiIsmAdverbs | 1 | | 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) | |
| 96.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1527 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1527 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 32.68% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 895 | | uniqueNames | 12 | | maxNameDensity | 2.35 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Carys" | | discoveredNames | | Rory | 21 | | Commercial | 1 | | Street | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Hughes | 1 | | Silas | 4 | | Carys | 16 | | Malbec | 1 | | Rhodri | 1 | | Rain | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Hughes" | | 3 | "Silas" | | 4 | "Carys" | | 5 | "Rhodri" | | 6 | "Rain" |
| | places | | 0 | "Commercial" | | 1 | "Street" | | 2 | "Soho" |
| | globalScore | 0.327 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 1527 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 139 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 19.58 | | std | 17.65 | | cv | 0.902 | | sampleLengths | | 0 | 49 | | 1 | 32 | | 2 | 31 | | 3 | 2 | | 4 | 1 | | 5 | 74 | | 6 | 40 | | 7 | 19 | | 8 | 42 | | 9 | 21 | | 10 | 18 | | 11 | 8 | | 12 | 7 | | 13 | 1 | | 14 | 2 | | 15 | 23 | | 16 | 3 | | 17 | 45 | | 18 | 13 | | 19 | 30 | | 20 | 8 | | 21 | 22 | | 22 | 11 | | 23 | 10 | | 24 | 3 | | 25 | 17 | | 26 | 33 | | 27 | 4 | | 28 | 31 | | 29 | 4 | | 30 | 3 | | 31 | 54 | | 32 | 2 | | 33 | 2 | | 34 | 3 | | 35 | 22 | | 36 | 1 | | 37 | 5 | | 38 | 18 | | 39 | 8 | | 40 | 5 | | 41 | 19 | | 42 | 23 | | 43 | 27 | | 44 | 3 | | 45 | 61 | | 46 | 13 | | 47 | 18 | | 48 | 6 | | 49 | 44 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 151 | | matches | | 0 | "was polishing" | | 1 | "were checking" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 139 | | ratio | 0.007 | | matches | | 0 | "The table behind them erupted over something; Rhodri's voice rolled off the ceiling." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 898 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.025612472160356347 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0011135857461024498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 10.99 | | std | 9.81 | | cv | 0.893 | | sampleLengths | | 0 | 20 | | 1 | 29 | | 2 | 9 | | 3 | 9 | | 4 | 14 | | 5 | 5 | | 6 | 26 | | 7 | 2 | | 8 | 1 | | 9 | 10 | | 10 | 22 | | 11 | 8 | | 12 | 6 | | 13 | 15 | | 14 | 13 | | 15 | 30 | | 16 | 2 | | 17 | 2 | | 18 | 6 | | 19 | 19 | | 20 | 2 | | 21 | 25 | | 22 | 4 | | 23 | 11 | | 24 | 13 | | 25 | 6 | | 26 | 2 | | 27 | 9 | | 28 | 9 | | 29 | 8 | | 30 | 3 | | 31 | 4 | | 32 | 1 | | 33 | 2 | | 34 | 20 | | 35 | 3 | | 36 | 3 | | 37 | 14 | | 38 | 12 | | 39 | 19 | | 40 | 4 | | 41 | 9 | | 42 | 26 | | 43 | 4 | | 44 | 8 | | 45 | 22 | | 46 | 8 | | 47 | 3 | | 48 | 10 | | 49 | 3 |
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| 63.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4172661870503597 | | totalSentences | 139 | | uniqueOpeners | 58 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 72 | | matches | | 0 | "Then she looked again, because" | | 1 | "Somewhere behind her eyes, two" | | 2 | "Then she laughed, one beat" | | 3 | "Then, slower, as if it" |
| | ratio | 0.056 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 72 | | matches | | 0 | "She shouldered through the door" | | 1 | "He set the tumbler down," | | 2 | "His mouth twitched under the" | | 3 | "He slid the glass across" | | 4 | "It stank of sesame oil" | | 5 | "She wrapped both hands around" | | 6 | "Her hand tightened on the" | | 7 | "She looked up." | | 8 | "She touched the stone on" | | 9 | "It came out harder than" | | 10 | "She looked down at the" | | 11 | "It had nothing in it." | | 12 | "Her voice thinned" | | 13 | "Her eyes came up, wet" | | 14 | "She pulled a napkin towards" | | 15 | "She slid it across the" | | 16 | "He set down a fresh" | | 17 | "He considered the table, the" |
| | ratio | 0.25 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 72 | | matches | | 0 | "The rain had followed Rory" | | 1 | "She shouldered through the door" | | 2 | "Silas glanced up from the" | | 3 | "Lamplight caught the silver signet" | | 4 | "He set the tumbler down," | | 5 | "Rory dropped onto a stool." | | 6 | "His mouth twitched under the" | | 7 | "He slid the glass across" | | 8 | "It stank of sesame oil" | | 9 | "Everything she owned stank of" | | 10 | "She wrapped both hands around" | | 11 | "The door opened and the" | | 12 | "Rory looked back at her" | | 13 | "Carys Hughes had acquired a" | | 14 | "None of it mattered." | | 15 | "Rory would have known her" | | 16 | "The impulse rose from somewhere" | | 17 | "Her hand tightened on the" | | 18 | "The two men took a" | | 19 | "Carys came to the bar," |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "Carys Hughes had acquired a good coat, a sharper haircut, a stone on her left hand that caught the lamplight the way Silas's ring did." | | 1 | "He considered the table, the bottle going round, Carys's laugh arriving a beat late and real." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | 0 | "Carys set, a card player folding" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 79 | | tagDensity | 0.063 | | leniency | 0.127 | | rawRatio | 0 | | effectiveRatio | 0 | |