| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "His lips twitched just [just]" | | 1 | "His voice dropped just [just]" |
| | dialogueSentences | 51 | | tagDensity | 0.275 | | leniency | 0.549 | | rawRatio | 0.143 | | effectiveRatio | 0.078 | |
| 89.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 993 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "slightly" | | 1 | "barely above a whisper" |
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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) | |
| 54.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 993 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulse" | | 2 | "flicked" | | 3 | "weight" | | 4 | "whisper" | | 5 | "silence" | | 6 | "could feel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 21 | | totalWords | 983 | | ratio | 0.021 | | matches | | 0 | "The Raven’s Nest" | | 1 | "clang" | | 2 | "different" | | 3 | "Aurora" | | 4 | "living" | | 5 | "surviving" | | 6 | "thunk" | | 7 | "click" | | 8 | "You left your bag at the restaurant. It’s in the office." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.32% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 717 | | uniqueNames | 9 | | maxNameDensity | 1.67 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | London | 1 | | Rory | 12 | | Silas | 5 | | Evan | 1 |
| | persons | | 0 | "Carter" | | 1 | "Blackwood" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Evan" |
| | places | | | globalScore | 0.663 | | windowScore | 0.833 | |
| 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 | 983 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 109 | | matches | | 0 | "buried that version" | | 1 | "knew that number" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 18.55 | | std | 18.54 | | cv | 1 | | sampleLengths | | 0 | 1 | | 1 | 84 | | 2 | 4 | | 3 | 86 | | 4 | 11 | | 5 | 4 | | 6 | 41 | | 7 | 13 | | 8 | 16 | | 9 | 9 | | 10 | 21 | | 11 | 19 | | 12 | 31 | | 13 | 5 | | 14 | 15 | | 15 | 8 | | 16 | 18 | | 17 | 35 | | 18 | 13 | | 19 | 7 | | 20 | 21 | | 21 | 5 | | 22 | 25 | | 23 | 14 | | 24 | 14 | | 25 | 9 | | 26 | 14 | | 27 | 12 | | 28 | 12 | | 29 | 66 | | 30 | 12 | | 31 | 12 | | 32 | 53 | | 33 | 9 | | 34 | 6 | | 35 | 5 | | 36 | 17 | | 37 | 7 | | 38 | 8 | | 39 | 12 | | 40 | 2 | | 41 | 10 | | 42 | 45 | | 43 | 15 | | 44 | 13 | | 45 | 40 | | 46 | 26 | | 47 | 13 | | 48 | 11 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 126 | | matches | | |
| 64.22% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 109 | | ratio | 0.028 | | matches | | 0 | "The door to *The Raven’s Nest* swung open with a *clang* of brass hinges, and the sudden gust of cold air carried with it the scent of damp wool and something sharper—gun oil, maybe, or the faint metallic tang of old blood." | | 1 | "His auburn hair was longer now, streaked with silver, and the way he moved—just a little slower, just a little more deliberate—made her chest tighten." | | 2 | "That got a real smile out of him—sharp, knowing, the kind that promised secrets." |
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| 95.23% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 726 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.045454545454545456 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009641873278236915 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 9.01 | | std | 7.05 | | cv | 0.783 | | sampleLengths | | 0 | 42 | | 1 | 21 | | 2 | 21 | | 3 | 4 | | 4 | 26 | | 5 | 25 | | 6 | 4 | | 7 | 3 | | 8 | 21 | | 9 | 7 | | 10 | 11 | | 11 | 3 | | 12 | 1 | | 13 | 4 | | 14 | 15 | | 15 | 15 | | 16 | 7 | | 17 | 3 | | 18 | 8 | | 19 | 2 | | 20 | 9 | | 21 | 7 | | 22 | 3 | | 23 | 6 | | 24 | 14 | | 25 | 7 | | 26 | 11 | | 27 | 8 | | 28 | 16 | | 29 | 15 | | 30 | 5 | | 31 | 6 | | 32 | 9 | | 33 | 8 | | 34 | 6 | | 35 | 12 | | 36 | 26 | | 37 | 5 | | 38 | 4 | | 39 | 11 | | 40 | 2 | | 41 | 7 | | 42 | 14 | | 43 | 7 | | 44 | 2 | | 45 | 3 | | 46 | 17 | | 47 | 8 | | 48 | 9 | | 49 | 5 |
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| 51.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3394495412844037 | | totalSentences | 109 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 68 | | matches | | 0 | "Then she saw him." | | 1 | "Instead, she changed the subject." | | 2 | "Then her phone buzzed in" |
| | ratio | 0.044 | |
| 49.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 68 | | matches | | 0 | "His auburn hair was longer" | | 1 | "He wasn’t just older." | | 2 | "He was *different*." | | 3 | "he said, voice rough as" | | 4 | "His head snapped up." | | 5 | "His lips twitched, just once," | | 6 | "She’d buried that version of" | | 7 | "He gestured to the stool" | | 8 | "She didn’t move." | | 9 | "He swirled the amber liquid" | | 10 | "His gaze flicked to her," | | 11 | "He held up the glass" | | 12 | "His voice dropped, just slightly" | | 13 | "He took a slow sip," | | 14 | "He leaned forward, elbows on" | | 15 | "His voice was gentle, almost" | | 16 | "She’d spent years telling herself" | | 17 | "She’d run because she’d been" | | 18 | "she admitted, her voice barely" | | 19 | "She wanted to ask him" |
| | ratio | 0.426 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 68 | | matches | | 0 | "The door to *The Raven’s" | | 1 | "Rory Carter froze mid-step, her" | | 2 | "The bar’s neon sign flickered" | | 3 | "Silas Blackwood stood at the" | | 4 | "His auburn hair was longer" | | 5 | "He wasn’t just older." | | 6 | "He was *different*." | | 7 | "The man who’d once been" | | 8 | "A ghost of the man" | | 9 | "he said, voice rough as" | | 10 | "Rory’s pulse spiked." | | 11 | "His head snapped up." | | 12 | "The hazel in his eyes" | | 13 | "His lips twitched, just once," | | 14 | "The name stung." | | 15 | "She’d buried that version of" | | 16 | "He gestured to the stool" | | 17 | "She didn’t move." | | 18 | "That got a real smile" | | 19 | "Rory exhaled through her nose," |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "Rory exhaled through her nose, a sound that was half-laugh, half-frustration." | | 1 | "Rory finally sat, sliding onto the stool with the deliberate slowness of a woman who’d spent too many nights wondering if she’d made the right choices." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice rough as gravel, without turning around" | | 1 | "she admitted, her voice barely above a whisper" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she admitted (admit)" |
| | dialogueSentences | 51 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0.667 | | effectiveRatio | 0.078 | |