| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.356 | | leniency | 0.711 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1321 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 65.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1321 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "perfect" | | 1 | "traced" | | 2 | "silence" | | 3 | "intensity" | | 4 | "glinting" | | 5 | "weight" | | 6 | "unreadable" | | 7 | "familiar" | | 8 | "depths" |
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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 | 54 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 54 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1321 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 823 | | uniqueNames | 11 | | maxNameDensity | 3.16 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 26 | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | Yu-Fei | 1 | | Silas | 8 | | Friday | 1 | | Eva | 15 | | Soho | 1 | | Sunday | 1 | | October | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Yu-Fei" | | 4 | "Silas" | | 5 | "Eva" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 1 | | matches | | 0 | "something like pride in the weariness of his" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1321 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 27.52 | | std | 16.9 | | cv | 0.614 | | sampleLengths | | 0 | 37 | | 1 | 19 | | 2 | 13 | | 3 | 79 | | 4 | 65 | | 5 | 12 | | 6 | 28 | | 7 | 6 | | 8 | 50 | | 9 | 30 | | 10 | 27 | | 11 | 21 | | 12 | 20 | | 13 | 3 | | 14 | 5 | | 15 | 34 | | 16 | 28 | | 17 | 20 | | 18 | 38 | | 19 | 20 | | 20 | 27 | | 21 | 12 | | 22 | 24 | | 23 | 19 | | 24 | 5 | | 25 | 27 | | 26 | 37 | | 27 | 37 | | 28 | 5 | | 29 | 44 | | 30 | 15 | | 31 | 39 | | 32 | 17 | | 33 | 47 | | 34 | 59 | | 35 | 22 | | 36 | 37 | | 37 | 14 | | 38 | 19 | | 39 | 16 | | 40 | 44 | | 41 | 10 | | 42 | 14 | | 43 | 10 | | 44 | 21 | | 45 | 57 | | 46 | 43 | | 47 | 45 |
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| 98.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 54 | | matches | | |
| 97.44% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 130 | | matches | | 0 | "was posing" | | 1 | "was already moving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 826 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.03389830508474576 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0036319612590799033 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 16.11 | | std | 11.35 | | cv | 0.705 | | sampleLengths | | 0 | 9 | | 1 | 28 | | 2 | 15 | | 3 | 4 | | 4 | 11 | | 5 | 2 | | 6 | 27 | | 7 | 25 | | 8 | 27 | | 9 | 21 | | 10 | 17 | | 11 | 21 | | 12 | 2 | | 13 | 4 | | 14 | 8 | | 15 | 4 | | 16 | 25 | | 17 | 3 | | 18 | 6 | | 19 | 13 | | 20 | 21 | | 21 | 16 | | 22 | 11 | | 23 | 19 | | 24 | 6 | | 25 | 21 | | 26 | 15 | | 27 | 6 | | 28 | 20 | | 29 | 3 | | 30 | 5 | | 31 | 11 | | 32 | 18 | | 33 | 2 | | 34 | 3 | | 35 | 5 | | 36 | 23 | | 37 | 20 | | 38 | 23 | | 39 | 15 | | 40 | 9 | | 41 | 11 | | 42 | 27 | | 43 | 10 | | 44 | 2 | | 45 | 24 | | 46 | 11 | | 47 | 8 | | 48 | 5 | | 49 | 27 |
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| 67.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4268292682926829 | | totalSentences | 82 | | uniqueOpeners | 35 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | 0 | "Just finished with surprises." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 50 | | matches | | 0 | "She'd prepared for mockery, for" | | 1 | "She crossed to the small" | | 2 | "He leaned on a cane" | | 3 | "She pushed the door open" | | 4 | "She stepped into the bar's" |
| | ratio | 0.1 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 50 | | matches | | 0 | "Rory's elbow knocked a half-empty" | | 1 | "Brown liquid spread across the" | | 2 | "Eva said, not looking up" | | 3 | "Rory corrected, and immediately hated" | | 4 | "The back room of The" | | 5 | "Silas had pushed them through" | | 6 | "The bookshelves hadn't lined up" | | 7 | "Eva turned in her chair," | | 8 | "The blonde hair was still" | | 9 | "Eva said, flat" | | 10 | "Rory looked down at her" | | 11 | "The accusation hung there, thick" | | 12 | "Rory traced the crescent scar" | | 13 | "The skin was smooth now," | | 14 | "Eva said, quieter now" | | 15 | "Rory hadn't expected the direct" | | 16 | "She'd prepared for mockery, for" | | 17 | "Rory said, and her voice" | | 18 | "Rory leaned back, feeling the" | | 19 | "Eva's posture shifted, imperceptible to" |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 9 | | matches | | 0 | "Brown liquid spread across the scarred wood, threading towards her coat sleeve, but she righted the glass with reflexes that had seen her through darker rooms t…" | | 1 | "The back room of The Raven's Nest smelled of old leather, pipe smoke, and the faint ozone left by someone who'd lied too hard for too long." | | 2 | "Silas had pushed them through the false bookshelf an hour prior with nothing more than a nod and a limp that dragged like old guilt." | | 3 | "But her eyes, those sharp grey eyes that had once dragged Rory from a drowning flat in Cardiff, had gone flat." | | 4 | "She'd prepared for mockery, for the old competitive edge that had always defined their friendship, but not for this precise excavation." | | 5 | "Rory turned back, her blue eyes catching the low light with an intensity that made Eva blink." | | 6 | "Rory was already moving, her body shifting into patterns that had replaced the old ones, the ones that had studied law and cooked Sunday roasts for parents who'…" | | 7 | "She stepped into the bar's main room, where the green neon from outside tinted everything sick and familiar, and she was gone before Eva could call her back, le…" | | 8 | "Rory pulled her collar up and walked north, her mind already cataloguing the cars Silas had mentioned, her heart beating in a rhythm that wasn't panic but prepa…" |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva said, not looking up from her own drink" | | 1 | "Eva said, flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 1 | | fancyTags | | 0 | "Rory corrected (correct)" |
| | dialogueSentences | 45 | | tagDensity | 0.311 | | leniency | 0.622 | | rawRatio | 0.071 | | effectiveRatio | 0.044 | |