| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 82.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 840 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "sharply" |
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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) | |
| 16.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 840 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "gleaming" | | 1 | "echoing" | | 2 | "flickered" | | 3 | "unreadable" | | 4 | "pulse" | | 5 | "gloom" | | 6 | "glinting" | | 7 | "racing" | | 8 | "shimmered" | | 9 | "scanning" | | 10 | "rhythmic" | | 11 | "pulsed" |
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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 | 1 | | narrationSentences | 78 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | 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 | 828 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 828 | | uniqueNames | 13 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 13 | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Market" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Morris" |
| | places | | | globalScore | 0.715 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like human teeth" |
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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 | 828 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 78 | | matches | | |
| 93.02% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 36 | | std | 17.12 | | cv | 0.476 | | sampleLengths | | 0 | 55 | | 1 | 60 | | 2 | 45 | | 3 | 56 | | 4 | 5 | | 5 | 43 | | 6 | 24 | | 7 | 37 | | 8 | 40 | | 9 | 34 | | 10 | 25 | | 11 | 75 | | 12 | 53 | | 13 | 46 | | 14 | 15 | | 15 | 33 | | 16 | 37 | | 17 | 3 | | 18 | 37 | | 19 | 25 | | 20 | 38 | | 21 | 20 | | 22 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 78 | | ratio | 0.128 | | matches | | 0 | "The suspect—some wiry kid with a hood pulled low—darted around a corner, vanishing into the labyrinth of Soho’s back alleys." | | 1 | "The neon glow of a bar sign flickered ahead—the Raven’s Nest." | | 2 | "The Raven’s Nest wasn’t just any dive—it was a place where people disappeared, where whispers of the supernatural clung to the air like cigarette smoke." | | 3 | "The kid was long gone, but the path was clear—fresh mud on the steps, the faintest scuff marks on the stone." | | 4 | "Then, ahead, a faint glow—warm, golden, pulsing like a heartbeat." | | 5 | "Lanterns hung from the ceiling, casting long shadows over tables laden with strange artifacts—glowing vials, bundles of dried herbs, stacks of yellowed parchment." | | 6 | "A figure brushed past her—a man with a Saint Christopher medallion glinting at his throat." | | 7 | "Her partner’s face flashed in her memory—his laugh, the way he’d always stolen her coffee." | | 8 | "Then she saw it—a bone token, half-buried in the mud near a stall selling what looked like human teeth." | | 9 | "And if she wanted answers—real answers—she had to follow." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 845 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over graffiti-covered walls," |
| | adverbCount | 24 | | adverbRatio | 0.028402366863905324 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0035502958579881655 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 10.62 | | std | 6.13 | | cv | 0.578 | | sampleLengths | | 0 | 14 | | 1 | 21 | | 2 | 20 | | 3 | 3 | | 4 | 19 | | 5 | 21 | | 6 | 17 | | 7 | 16 | | 8 | 14 | | 9 | 11 | | 10 | 4 | | 11 | 14 | | 12 | 15 | | 13 | 2 | | 14 | 25 | | 15 | 5 | | 16 | 15 | | 17 | 15 | | 18 | 13 | | 19 | 15 | | 20 | 3 | | 21 | 6 | | 22 | 9 | | 23 | 14 | | 24 | 14 | | 25 | 17 | | 26 | 21 | | 27 | 2 | | 28 | 11 | | 29 | 13 | | 30 | 10 | | 31 | 2 | | 32 | 17 | | 33 | 6 | | 34 | 19 | | 35 | 23 | | 36 | 8 | | 37 | 1 | | 38 | 13 | | 39 | 11 | | 40 | 6 | | 41 | 20 | | 42 | 5 | | 43 | 22 | | 44 | 15 | | 45 | 11 | | 46 | 2 | | 47 | 5 | | 48 | 13 | | 49 | 12 |
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| 39.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2564102564102564 | | totalSentences | 78 | | uniqueOpeners | 20 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 72 | | matches | | 0 | "Then, ahead, a faint glow—warm," | | 1 | "Then the blood." | | 2 | "Just a body and a" | | 3 | "Then she saw it—a bone" |
| | ratio | 0.056 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 72 | | matches | | 0 | "Her leather watch strap dug" | | 1 | "She pushed through the door." | | 2 | "She moved quickly, her pulse" | | 3 | "She rounded a corner and" | | 4 | "She’d heard rumors of this" | | 5 | "He carried a medical bag," | | 6 | "He didn’t spare her a" | | 7 | "She could turn back, call" | | 8 | "Her partner’s face flashed in" | | 9 | "She ignored him, her eyes" | | 10 | "She stood, her pulse quickening." |
| | ratio | 0.153 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 72 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The suspect—some wiry kid with" | | 3 | "Quinn didn’t slow." | | 4 | "Her leather watch strap dug" | | 5 | "The kid had moved too" | | 6 | "A dumpster clanged as the" | | 7 | "Quinn vaulted over it, her" | | 8 | "The neon glow of a" | | 9 | "The kid ducked inside." | | 10 | "Quinn skidded to a halt" | | 11 | "The bar’s green neon sign" | | 12 | "The Raven’s Nest wasn’t just" | | 13 | "She pushed through the door." | | 14 | "The interior was dim, the" | | 15 | "The walls were lined with" | | 16 | "Quinn’s gaze locked onto the" | | 17 | "A hidden door." | | 18 | "The kid had gone through" | | 19 | "She moved quickly, her pulse" |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 2 | | matches | | 0 | "Former paramedic, lost his license after treating patients with injuries that defied explanation." | | 1 | "A stall vendor offered her a vial of something that shimmered like liquid starlight." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |