| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1494 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1494 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 57 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1497 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 721 | | uniqueNames | 10 | | maxNameDensity | 1.8 | | worstName | "Silas" | | maxWindowNameDensity | 3 | | worstWindowName | "Silas" | | discoveredNames | | Wardour | 1 | | Street | 1 | | Rory | 7 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Tuesdays | 1 | | Silas | 13 | | Bohemia | 2 | | Teddy | 8 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Teddy" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Soho" |
| | globalScore | 0.598 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 1497 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 19.44 | | std | 20.13 | | cv | 1.036 | | sampleLengths | | 0 | 52 | | 1 | 10 | | 2 | 11 | | 3 | 13 | | 4 | 58 | | 5 | 9 | | 6 | 63 | | 7 | 14 | | 8 | 8 | | 9 | 18 | | 10 | 1 | | 11 | 2 | | 12 | 23 | | 13 | 6 | | 14 | 41 | | 15 | 33 | | 16 | 53 | | 17 | 4 | | 18 | 3 | | 19 | 22 | | 20 | 26 | | 21 | 4 | | 22 | 4 | | 23 | 9 | | 24 | 3 | | 25 | 7 | | 26 | 14 | | 27 | 8 | | 28 | 4 | | 29 | 38 | | 30 | 6 | | 31 | 5 | | 32 | 3 | | 33 | 12 | | 34 | 28 | | 35 | 10 | | 36 | 9 | | 37 | 3 | | 38 | 15 | | 39 | 14 | | 40 | 90 | | 41 | 5 | | 42 | 55 | | 43 | 13 | | 44 | 39 | | 45 | 1 | | 46 | 2 | | 47 | 9 | | 48 | 41 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 114 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 112 | | ratio | 0.009 | | matches | | 0 | "He read the walls before he read the room — the maps, the black-and-white photographs — the way a man checks a place for what has changed in it since he last stood there." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 722 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.0221606648199446 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 13.37 | | std | 11.07 | | cv | 0.829 | | sampleLengths | | 0 | 31 | | 1 | 21 | | 2 | 10 | | 3 | 11 | | 4 | 13 | | 5 | 27 | | 6 | 16 | | 7 | 15 | | 8 | 9 | | 9 | 29 | | 10 | 34 | | 11 | 9 | | 12 | 5 | | 13 | 5 | | 14 | 3 | | 15 | 6 | | 16 | 12 | | 17 | 1 | | 18 | 2 | | 19 | 12 | | 20 | 11 | | 21 | 6 | | 22 | 18 | | 23 | 23 | | 24 | 23 | | 25 | 10 | | 26 | 20 | | 27 | 9 | | 28 | 5 | | 29 | 19 | | 30 | 4 | | 31 | 3 | | 32 | 7 | | 33 | 15 | | 34 | 26 | | 35 | 4 | | 36 | 4 | | 37 | 9 | | 38 | 3 | | 39 | 7 | | 40 | 14 | | 41 | 8 | | 42 | 4 | | 43 | 15 | | 44 | 23 | | 45 | 6 | | 46 | 5 | | 47 | 3 | | 48 | 12 | | 49 | 21 |
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| 58.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.38392857142857145 | | totalSentences | 112 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 55 | | matches | | 0 | "Then they stood a little" | | 1 | "Somewhere in the cellar a" | | 2 | "Then he pressed the till," |
| | ratio | 0.055 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 55 | | matches | | 0 | "She peeled off her helmet," | | 1 | "He read the walls before" | | 2 | "He didn't look at her." | | 3 | "She had never once heard" | | 4 | "His eyes went to the" | | 5 | "He chose the stools instead." | | 6 | "He looked along the shelves," | | 7 | "He turned his own glass" | | 8 | "He looked at the bottle" | | 9 | "He set the glass down" | | 10 | "He looked at his own" | | 11 | "He squared the glass with" | | 12 | "He looked up" | | 13 | "He slid the note across" | | 14 | "He poured two fingers of" |
| | ratio | 0.273 | |
| 41.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 55 | | matches | | 0 | "The rain came off Wardour" | | 1 | "She peeled off her helmet," | | 2 | "Silas took the sack and" | | 3 | "The radio muttered to one" | | 4 | "The bell over the door" | | 5 | "The man on the mat" | | 6 | "He read the walls before" | | 7 | "Rory reached for a glass" | | 8 | "He didn't look at her." | | 9 | "The cloth stopped on the" | | 10 | "Silas set it down with" | | 11 | "Rory had hung her raincoat" | | 12 | "She had never once heard" | | 13 | "The cab driver read Silas's" | | 14 | "Rory turned the lock and" | | 15 | "The handshake became a grip" | | 16 | "Silas came around the bar" | | 17 | "His eyes went to the" | | 18 | "He chose the stools instead." | | 19 | "Teddy circled the glass with" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Rory turned the lock and went to the quiet end of the bar, where she started restacking glasses that did not need restacking." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.014 | | leniency | 0.029 | | rawRatio | 0 | | effectiveRatio | 0 | |