| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 37 | | tagDensity | 0.297 | | leniency | 0.595 | | rawRatio | 0.091 | | effectiveRatio | 0.054 | |
| 81.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1061 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "really" | | 1 | "very" | | 2 | "slowly" |
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| 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) | |
| 71.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1061 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "familiar" | | 1 | "footsteps" | | 2 | "tracing" | | 3 | "flicker" | | 4 | "calculating" | | 5 | "stark" |
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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 | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 55 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 80 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1059 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 654 | | uniqueNames | 8 | | maxNameDensity | 0.76 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 5 | | Raven | 1 | | Nest | 1 | | Bute | 1 | | Park | 1 | | Cardiff | 1 | | Evan | 2 | | Latin | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 1059 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 80 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 30.26 | | std | 21.28 | | cv | 0.703 | | sampleLengths | | 0 | 71 | | 1 | 10 | | 2 | 28 | | 3 | 27 | | 4 | 62 | | 5 | 4 | | 6 | 24 | | 7 | 55 | | 8 | 65 | | 9 | 1 | | 10 | 17 | | 11 | 31 | | 12 | 24 | | 13 | 21 | | 14 | 33 | | 15 | 12 | | 16 | 31 | | 17 | 21 | | 18 | 50 | | 19 | 31 | | 20 | 9 | | 21 | 6 | | 22 | 39 | | 23 | 20 | | 24 | 40 | | 25 | 2 | | 26 | 44 | | 27 | 56 | | 28 | 2 | | 29 | 32 | | 30 | 11 | | 31 | 36 | | 32 | 49 | | 33 | 8 | | 34 | 87 |
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| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 55 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 102 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 80 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 655 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.025954198473282442 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00916030534351145 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 80 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 80 | | mean | 13.24 | | std | 7.82 | | cv | 0.591 | | sampleLengths | | 0 | 17 | | 1 | 5 | | 2 | 20 | | 3 | 21 | | 4 | 8 | | 5 | 10 | | 6 | 8 | | 7 | 20 | | 8 | 12 | | 9 | 15 | | 10 | 3 | | 11 | 23 | | 12 | 15 | | 13 | 21 | | 14 | 4 | | 15 | 15 | | 16 | 9 | | 17 | 10 | | 18 | 1 | | 19 | 9 | | 20 | 18 | | 21 | 17 | | 22 | 12 | | 23 | 16 | | 24 | 17 | | 25 | 6 | | 26 | 14 | | 27 | 1 | | 28 | 6 | | 29 | 11 | | 30 | 19 | | 31 | 12 | | 32 | 17 | | 33 | 7 | | 34 | 3 | | 35 | 18 | | 36 | 10 | | 37 | 23 | | 38 | 12 | | 39 | 14 | | 40 | 17 | | 41 | 13 | | 42 | 8 | | 43 | 12 | | 44 | 38 | | 45 | 31 | | 46 | 3 | | 47 | 6 | | 48 | 6 | | 49 | 15 |
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| 66.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.45 | | totalSentences | 80 | | uniqueOpeners | 36 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Then it was gone, replaced" |
| | ratio | 0.019 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 54 | | matches | | 0 | "She slid a crate of" | | 1 | "His silver signet ring caught" | | 2 | "He didn't look up from" | | 3 | "She straightened, wiping her hands" | | 4 | "He set the glass down" | | 5 | "He nursed a single malt," | | 6 | "She walked towards the table," | | 7 | "It was older, harder, the" | | 8 | "He offered a thin, humourless" | | 9 | "Her hand instinctively went to" | | 10 | "He gestured to the empty" | | 11 | "She remained standing." | | 12 | "He took a slow sip" | | 13 | "He leaned forward, the light" | | 14 | "He placed his glass on" | | 15 | "He slid a folded piece" | | 16 | "It was thick, expensive stock." | | 17 | "He leaned back, a flicker" | | 18 | "Her father, a man of" | | 19 | "It was absurd." |
| | ratio | 0.5 | |
| 6.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 54 | | matches | | 0 | "The bell above the door" | | 1 | "Tonight, it signalled the beginning." | | 2 | "She slid a crate of" | | 3 | "Silas was polishing a tumbler," | | 4 | "His silver signet ring caught" | | 5 | "He didn't look up from" | | 6 | "She straightened, wiping her hands" | | 7 | "He set the glass down" | | 8 | "Rory glanced over." | | 9 | "The man was a study" | | 10 | "He nursed a single malt," | | 11 | "Something about the set of" | | 12 | "Silas finally met her gaze," | | 13 | "The name hit her like" | | 14 | "The girl who wore cardigans" | | 15 | "The girl who had a" | | 16 | "The girl who had left" | | 17 | "She walked towards the table," | | 18 | "It was older, harder, the" | | 19 | "A bright, piercing blue that" |
| | ratio | 0.907 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 3 | | matches | | 0 | "Silas was polishing a tumbler, his movements economical and precise, a man who had performed this same action a thousand times." | | 1 | "The girl who had a crescent-shaped scar on her wrist from falling off a swing in Bute Park." | | 2 | "Her father, a man of impeccable integrity, a man who quoted Latin at the dinner table and believed in the sanctity of the law above all else." |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "He leaned, the light catching the tired lines around his eyes" | | 1 | "He paused, his blue eyes locking with hers" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |