| 40.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 4 | | adverbTags | | 0 | "he asked again [again]" | | 1 | "he said abruptly [abruptly]" | | 2 | "she said quietly [quietly]" | | 3 | "she said firmly [firmly]" |
| | dialogueSentences | 50 | | tagDensity | 0.36 | | leniency | 0.72 | | rawRatio | 0.222 | | effectiveRatio | 0.16 | |
| 74.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1568 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "completely" | | 1 | "slowly" | | 2 | "suddenly" | | 3 | "really" |
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| 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) | |
| 74.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1568 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "traced" | | 2 | "implication" | | 3 | "unspoken" | | 4 | "weight" | | 5 | "measured" | | 6 | "firmly" |
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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 | 100 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 100 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1566 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1090 | | uniqueNames | 11 | | maxNameDensity | 0.55 | | worstName | "Marcus" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Marcus" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 2 | | Tuesday | 1 | | Silas | 5 | | Webb | 1 | | Evan | 1 | | Singapore | 1 | | Marcus | 6 | | Rory | 3 | | Hendrick | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Webb" | | 5 | "Evan" | | 6 | "Marcus" | | 7 | "Rory" | | 8 | "Hendrick" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 4 | | matches | | 0 | "So had he, apparently, though in his case" | | 1 | "sounded like someone else's" | | 2 | "quite name" | | 3 | "felt like from both sides" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.639 | | wordCount | 1566 | | matches | | 0 | "Not at her directly, but past her, the way you might notice a change in air pressure" |
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| 40.40% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 5 | | totalSentences | 132 | | matches | | 0 | "discovered that they" | | 1 | "pretending that work" | | 2 | "solo, that the" | | 3 | "learned that apologies" | | 4 | "understood that time" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 22.7 | | std | 19.28 | | cv | 0.849 | | sampleLengths | | 0 | 36 | | 1 | 56 | | 2 | 61 | | 3 | 4 | | 4 | 63 | | 5 | 4 | | 6 | 46 | | 7 | 4 | | 8 | 9 | | 9 | 40 | | 10 | 5 | | 11 | 10 | | 12 | 7 | | 13 | 19 | | 14 | 39 | | 15 | 55 | | 16 | 37 | | 17 | 10 | | 18 | 1 | | 19 | 27 | | 20 | 57 | | 21 | 3 | | 22 | 15 | | 23 | 51 | | 24 | 2 | | 25 | 28 | | 26 | 25 | | 27 | 4 | | 28 | 33 | | 29 | 48 | | 30 | 5 | | 31 | 38 | | 32 | 21 | | 33 | 24 | | 34 | 32 | | 35 | 2 | | 36 | 42 | | 37 | 56 | | 38 | 10 | | 39 | 18 | | 40 | 23 | | 41 | 10 | | 42 | 3 | | 43 | 72 | | 44 | 7 | | 45 | 8 | | 46 | 10 | | 47 | 47 | | 48 | 7 | | 49 | 36 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 100 | | matches | | 0 | "been replaced" | | 1 | "being obeyed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 207 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 77.93% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1090 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 69 | | adverbRatio | 0.06330275229357799 | | lyAdverbCount | 24 | | lyAdverbRatio | 0.022018348623853212 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 11.86 | | std | 10.79 | | cv | 0.91 | | sampleLengths | | 0 | 9 | | 1 | 9 | | 2 | 18 | | 3 | 16 | | 4 | 11 | | 5 | 29 | | 6 | 9 | | 7 | 4 | | 8 | 10 | | 9 | 20 | | 10 | 18 | | 11 | 4 | | 12 | 25 | | 13 | 4 | | 14 | 13 | | 15 | 15 | | 16 | 1 | | 17 | 5 | | 18 | 4 | | 19 | 25 | | 20 | 7 | | 21 | 9 | | 22 | 5 | | 23 | 4 | | 24 | 9 | | 25 | 9 | | 26 | 13 | | 27 | 18 | | 28 | 5 | | 29 | 10 | | 30 | 7 | | 31 | 12 | | 32 | 7 | | 33 | 5 | | 34 | 17 | | 35 | 17 | | 36 | 10 | | 37 | 15 | | 38 | 6 | | 39 | 24 | | 40 | 37 | | 41 | 10 | | 42 | 1 | | 43 | 3 | | 44 | 24 | | 45 | 52 | | 46 | 5 | | 47 | 3 | | 48 | 15 | | 49 | 3 |
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| 54.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.36363636363636365 | | totalSentences | 132 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 88 | | matches | | 0 | "Then she saw him." | | 1 | "Then his eyes swung back" | | 2 | "Then he'd moved to Singapore" | | 3 | "Too distant despite his proximity." |
| | ratio | 0.045 | |
| 24.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 88 | | matches | | 0 | "She pushed through the heavy" | | 1 | "She recognised most faces." | | 2 | "He sat at a table" | | 3 | "His hair had darkened to" | | 4 | "His suit was definitely expensive." | | 5 | "His hazel eyes tracked her" | | 6 | "They moved from her face" | | 7 | "She thrust the containers at" | | 8 | "He took them without complaint," | | 9 | "He disappeared into the kitchen" | | 10 | "They'd met in primary school" | | 11 | "He'd held her hair back" | | 12 | "She watched recognition bloom across" | | 13 | "She didn't move." | | 14 | "He stood instead, nearly knocking" | | 15 | "He stopped, laughed, the sound" | | 16 | "She was different." | | 17 | "It wasn't a question, the" | | 18 | "She pulled out the chair" | | 19 | "he asked again, buying time," |
| | ratio | 0.489 | |
| 33.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 88 | | matches | | 0 | "The Raven's Nest smelled exactly" | | 1 | "Mahogany and whisky and something" | | 2 | "She pushed through the heavy" | | 3 | "Silas had called the restaurant" | | 4 | "The bar was half full" | | 5 | "She recognised most faces." | | 6 | "The accountant in the corner" | | 7 | "The two art students who" | | 8 | "The businessman with the wedding" | | 9 | "He sat at a table" | | 10 | "The easy slouch she'd known" | | 11 | "His hair had darkened to" | | 12 | "His suit was definitely expensive." | | 13 | "Rory's feet stopped moving." | | 14 | "Silas materialised from behind the" | | 15 | "His hazel eyes tracked her" | | 16 | "They moved from her face" | | 17 | "Something flickered there, something knowing." | | 18 | "She thrust the containers at" | | 19 | "He took them without complaint," |
| | ratio | 0.852 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | 0 | "As though the Aurora he'd" |
| | ratio | 0.011 | |
| 55.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 5 | | matches | | 0 | "Then he'd moved to Singapore for a position at some investment firm, and they'd become the kind of friends that said they'd keep in touch and slowly discovered …" | | 1 | "That much was evident in the way he was looking at her, as though she'd rearranged herself while he wasn't watching." | | 2 | "As though the Aurora he'd left behind would have stayed the same, preserved in amber, waiting for him to decide when he was ready to come home." | | 3 | "She'd learned that apologies were often just requests for forgiveness that relieved the apologiser without costing them anything real." | | 4 | "This time when he reached for it, she stood, leaving the vodka untouched, and walked toward the bar where Silas waited with the particular patience of someone w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.22 | | leniency | 0.44 | | rawRatio | 0 | | effectiveRatio | 0 | |