| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 72.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1110 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "cautiously" | | 1 | "suddenly" | | 2 | "slowly" |
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| 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) | |
| 32.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1110 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "down her spine" | | count | 1 |
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| | highlights | | 0 | "footsteps" | | 1 | "pounding" | | 2 | "scanned" | | 3 | "echoing" | | 4 | "could feel" | | 5 | "weight" | | 6 | "echoed" | | 7 | "otherworldly" | | 8 | "chill" | | 9 | "down her spine" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 5 | | maxInWindow | 5 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 3 |
| | 1 | | label | "air was thick with" | | count | 2 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "The air was thick with" | | 3 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 69 | | matches | | 0 | "felt a chill" | | 1 | "d in horror" |
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| 80.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 69 | | filterMatches | (empty) | | hedgeMatches | | 0 | "appeared to" | | 1 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 977 | | uniqueNames | 12 | | maxNameDensity | 2.46 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 24 | | Soho | 1 | | Tomás | 2 | | Herrera | 14 | | Veil | 1 | | Market | 1 | | Morris | 1 | | Gritting | 2 | | Tube | 1 | | Suddenly | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" |
| | places | | | globalScore | 0.272 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1111 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 67.06% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 30.03 | | std | 11.55 | | cv | 0.385 | | sampleLengths | | 0 | 35 | | 1 | 10 | | 2 | 38 | | 3 | 38 | | 4 | 34 | | 5 | 46 | | 6 | 48 | | 7 | 52 | | 8 | 31 | | 9 | 18 | | 10 | 13 | | 11 | 15 | | 12 | 23 | | 13 | 30 | | 14 | 26 | | 15 | 33 | | 16 | 36 | | 17 | 32 | | 18 | 33 | | 19 | 43 | | 20 | 42 | | 21 | 32 | | 22 | 29 | | 23 | 19 | | 24 | 25 | | 25 | 57 | | 26 | 40 | | 27 | 14 | | 28 | 24 | | 29 | 23 | | 30 | 23 | | 31 | 13 | | 32 | 12 | | 33 | 26 | | 34 | 35 | | 35 | 41 | | 36 | 22 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 69 | | matches | | 0 | "was lit" | | 1 | "was filled" | | 2 | "was plunged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 179 | | matches | | |
| 1.76% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 81 | | ratio | 0.049 | | matches | | 0 | "Up ahead, a flash of movement caught her eye - her target, a young man named Tomás Herrera, darting down an alleyway." | | 1 | "She knew this area - it was the entrance to the Veil Market, a shadowy underground bazaar that dealt in all manner of occult goods and information." | | 2 | "The market was a maze of stalls and alcoves, each one offering a bewildering array of items - from glowing crystals to ancient-looking tomes." | | 3 | "Suddenly, a flash of movement caught her eye - there, near the back of the market, was her target, slipping through a curtain." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 976 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02459016393442623 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01331967213114754 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 13.72 | | std | 6.33 | | cv | 0.462 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 10 | | 3 | 22 | | 4 | 16 | | 5 | 22 | | 6 | 16 | | 7 | 16 | | 8 | 18 | | 9 | 8 | | 10 | 27 | | 11 | 11 | | 12 | 5 | | 13 | 13 | | 14 | 16 | | 15 | 14 | | 16 | 15 | | 17 | 13 | | 18 | 24 | | 19 | 7 | | 20 | 19 | | 21 | 5 | | 22 | 5 | | 23 | 13 | | 24 | 3 | | 25 | 10 | | 26 | 3 | | 27 | 12 | | 28 | 7 | | 29 | 16 | | 30 | 3 | | 31 | 21 | | 32 | 6 | | 33 | 9 | | 34 | 17 | | 35 | 23 | | 36 | 10 | | 37 | 23 | | 38 | 13 | | 39 | 14 | | 40 | 7 | | 41 | 11 | | 42 | 14 | | 43 | 19 | | 44 | 22 | | 45 | 21 | | 46 | 6 | | 47 | 12 | | 48 | 24 | | 49 | 11 |
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| 69.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.41975308641975306 | | totalSentences | 81 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 69 | | matches | | 0 | "Suddenly, a flash of movement" | | 1 | "Suddenly, the corridor opened up" | | 2 | "Slowly, she began to move" | | 3 | "Suddenly, a movement in the" | | 4 | "Cautiously, she approached, her weapon" |
| | ratio | 0.072 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 69 | | matches | | 0 | "She'd been pursuing the suspect" | | 1 | "she muttered, eyes narrowed in" | | 2 | "She pushed herself harder, her" | | 3 | "She knew this area -" | | 4 | "Her partner, DS Morris, had" | | 5 | "She burst through the curtain" | | 6 | "She could feel the weight" | | 7 | "She had never encountered anything" |
| | ratio | 0.116 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 69 | | matches | | 0 | "The rain lashed against the" | | 1 | "She'd been pursuing the suspect" | | 2 | "she muttered, eyes narrowed in" | | 3 | "The alley was a maze" | | 4 | "Quinn could hear Herrera's footsteps" | | 5 | "She pushed herself harder, her" | | 6 | "Quinn skidded to a halt," | | 7 | "She knew this area -" | | 8 | "Her partner, DS Morris, had" | | 9 | "The market was a dangerous" | | 10 | "The air was thick with" | | 11 | "Quinn moved cautiously, her hand" | | 12 | "The market was a maze" | | 13 | "A gruff voice called out" | | 14 | "Quinn turned to see a" | | 15 | "Quinn held up a hand." | | 16 | "The man sneered." | | 17 | "Quinn's eyes narrowed." | | 18 | "The man's grip tightened on" | | 19 | "Quinn's jaw tightened." |
| | ratio | 0.623 | |
| 72.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 69 | | matches | | 0 | "Just as Herrera reached the" |
| | ratio | 0.014 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn could hear Herrera's footsteps pounding ahead of her, the sound ricocheting off the brick walls." | | 1 | "She knew this area - it was the entrance to the Veil Market, a shadowy underground bazaar that dealt in all manner of occult goods and information." | | 2 | "Gritting her teeth, Quinn gave chase, her boots pounding against the damp concrete floor." | | 3 | "With a flick of his wrist, he sent a wave of energy hurtling towards Quinn, knocking her backwards and sending her weapon clattering to the floor." | | 4 | "Gritting her teeth, she forced herself to her feet, her gaze darting around the room for any sign of a weapon." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "she muttered, eyes narrowed in concentration" | | 1 | "Quinn shouted, but the man didn't even slow down" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Quinn shouted (shout)" |
| | dialogueSentences | 16 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 1 | | effectiveRatio | 0.25 | |