| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "his mouth unhurried like [like]" |
| | dialogueSentences | 45 | | tagDensity | 0.244 | | leniency | 0.489 | | rawRatio | 0.091 | | effectiveRatio | 0.044 | |
| 91.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1221 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 91.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1221 | | totalAiIsms | 2 | | 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 | 59 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 59 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 93 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1236 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 701 | | uniqueNames | 8 | | maxNameDensity | 0.86 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Lucien | 1 | | Moreau | 1 | | Eva | 6 | | Ptolemy | 3 | | Brick | 1 | | Lane | 1 | | French | 2 | | Rory | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Ptolemy" | | 4 | "French" | | 5 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1236 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 93 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 22.07 | | std | 21.51 | | cv | 0.975 | | sampleLengths | | 0 | 16 | | 1 | 59 | | 2 | 27 | | 3 | 3 | | 4 | 4 | | 5 | 37 | | 6 | 2 | | 7 | 4 | | 8 | 7 | | 9 | 48 | | 10 | 8 | | 11 | 11 | | 12 | 13 | | 13 | 24 | | 14 | 12 | | 15 | 4 | | 16 | 35 | | 17 | 67 | | 18 | 27 | | 19 | 23 | | 20 | 48 | | 21 | 1 | | 22 | 29 | | 23 | 3 | | 24 | 41 | | 25 | 28 | | 26 | 6 | | 27 | 2 | | 28 | 75 | | 29 | 108 | | 30 | 5 | | 31 | 5 | | 32 | 28 | | 33 | 5 | | 34 | 39 | | 35 | 5 | | 36 | 16 | | 37 | 28 | | 38 | 58 | | 39 | 15 | | 40 | 15 | | 41 | 7 | | 42 | 7 | | 43 | 8 | | 44 | 49 | | 45 | 13 | | 46 | 43 | | 47 | 14 | | 48 | 20 | | 49 | 29 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 93 | | ratio | 0.086 | | matches | | 0 | "He stood on Eva's mat with the stairwell bulb doing its last honest work above him, charcoal suit soaked to slate across the shoulders, platinum hair — always lacquered, always ordered — plastered to his brow like he'd lost an argument with the weather." | | 1 | "The ivory handle of his cane rose — not to block it, just to rest against the frame, polite as a raised hand at a lecture." | | 2 | "It did its work in her wrists first — the old crescent scar pulling tight, though scars don't do that." | | 3 | "Rory pulled a towel off the radiator and threw it at him — one of Eva's, a protection sigil embroidered along the border, allegedly." | | 4 | "\"To you.\" He said it the way he priced things — flat, final." | | 5 | "The margins were full of pencil — French, tight, slanted." | | 6 | "\"You're committed to most things.\" His thumb hovered an inch above the scar, not touching — just an inch of rain-cooled air that she could feel anyway, everywhere." | | 7 | "He turned his face half away — the amber half to the window, the black half in shadow — and the French came out quiet, almost to the glass." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 695 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.027338129496402876 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0028776978417266188 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 13.29 | | std | 12.99 | | cv | 0.977 | | sampleLengths | | 0 | 16 | | 1 | 44 | | 2 | 9 | | 3 | 6 | | 4 | 12 | | 5 | 4 | | 6 | 4 | | 7 | 7 | | 8 | 3 | | 9 | 4 | | 10 | 11 | | 11 | 26 | | 12 | 2 | | 13 | 4 | | 14 | 7 | | 15 | 19 | | 16 | 29 | | 17 | 8 | | 18 | 11 | | 19 | 13 | | 20 | 24 | | 21 | 6 | | 22 | 6 | | 23 | 4 | | 24 | 20 | | 25 | 3 | | 26 | 3 | | 27 | 9 | | 28 | 6 | | 29 | 19 | | 30 | 4 | | 31 | 14 | | 32 | 24 | | 33 | 27 | | 34 | 17 | | 35 | 6 | | 36 | 21 | | 37 | 27 | | 38 | 1 | | 39 | 29 | | 40 | 3 | | 41 | 18 | | 42 | 23 | | 43 | 10 | | 44 | 18 | | 45 | 6 | | 46 | 2 | | 47 | 13 | | 48 | 62 | | 49 | 15 |
| |
| 73.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4838709677419355 | | totalSentences | 93 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 30.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 57 | | matches | | 0 | "He stood on Eva's mat" | | 1 | "She put her palm flat" | | 2 | "Her name in his mouth," | | 3 | "She should have closed the" | | 4 | "Her hand stayed on the" | | 5 | "It did its work in" | | 6 | "She stepped back." | | 7 | "She'd have let a bailiff" | | 8 | "He caught it one-handed and" | | 9 | "She crossed to the kettle" | | 10 | "He draped his coat over" | | 11 | "She set the kettle down" | | 12 | "He said it the way" | | 13 | "She turned, and the mug" | | 14 | "He reached into the coat" | | 15 | "He held it out." | | 16 | "She took it because her" | | 17 | "He'd read it four times." | | 18 | "She hated him for it," | | 19 | "She stepped closer to shut" |
| | ratio | 0.474 | |
| 38.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 57 | | matches | | 0 | "The third deadbolt gave under" | | 1 | "He stood on Eva's mat" | | 2 | "The black one swallowed it" | | 3 | "The cat despised everyone." | | 4 | "The postman got claws." | | 5 | "Eva got teeth, and Eva" | | 6 | "She put her palm flat" | | 7 | "The ivory handle of his" | | 8 | "Rain dripped off his jaw" | | 9 | "Her name in his mouth," | | 10 | "She should have closed the" | | 11 | "Her hand stayed on the" | | 12 | "It did its work in" | | 13 | "She stepped back." | | 14 | "She'd have let a bailiff" | | 15 | "The flat was a paper" | | 16 | "Books stood in load-bearing columns," | | 17 | "Scrolls furred the sofa." | | 18 | "Notes pinned the curtains to" | | 19 | "Rory pulled a towel off" |
| | ratio | 0.842 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Rory muttered (mutter)" |
| | dialogueSentences | 45 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0.333 | | effectiveRatio | 0.044 | |