| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 24 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.083 | | effectiveRatio | 0.083 | |
| 96.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1288 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 18.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1288 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "carried the weight" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "weight" | | 1 | "flickered" | | 2 | "warmth" | | 3 | "chill" | | 4 | "echoed" | | 5 | "carried the weight" | | 6 | "glistening" | | 7 | "tracing" | | 8 | "jaw clenched" | | 9 | "sinister" | | 10 | "flicker" | | 11 | "charm" | | 12 | "etched" | | 13 | "scanning" | | 14 | "glinting" | | 15 | "structure" | | 16 | "dancing" | | 17 | "glint" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1272 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 983 | | uniqueNames | 14 | | maxNameDensity | 1.32 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Dean | 1 | | Street | 1 | | Harlow | 13 | | Quinn | 1 | | Tommy | 1 | | Herrera | 1 | | London | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Nest | 1 | | Tomás | 7 | | Grief | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tommy" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "London" | | 3 | "Nest" |
| | globalScore | 0.839 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | 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 | 1272 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 42.4 | | std | 27.33 | | cv | 0.645 | | sampleLengths | | 0 | 110 | | 1 | 42 | | 2 | 92 | | 3 | 83 | | 4 | 36 | | 5 | 5 | | 6 | 61 | | 7 | 13 | | 8 | 24 | | 9 | 18 | | 10 | 75 | | 11 | 37 | | 12 | 23 | | 13 | 23 | | 14 | 24 | | 15 | 63 | | 16 | 31 | | 17 | 36 | | 18 | 36 | | 19 | 76 | | 20 | 18 | | 21 | 73 | | 22 | 17 | | 23 | 61 | | 24 | 79 | | 25 | 40 | | 26 | 21 | | 27 | 9 | | 28 | 22 | | 29 | 24 |
| |
| 95.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 69 | | matches | | 0 | "was supposed" | | 1 | "were plastered" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 165 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 84 | | ratio | 0.131 | | matches | | 0 | "She’d lost track of Tommy Herrera three blocks back, but the paramedic had slowed, and she could smell it on him: the coppery tang of fear mixed with something else—something she couldn’t name." | | 1 | "Maps of the city’s older districts—some annotated in handwriting she didn’t recognize—hung beside black-and-white shots of cobblestone alleys and shuttered storefronts." | | 2 | "The bone token requirement was the latest twist in her investigation—a detail she’d confirmed through a fence who’d since vanished himself." | | 3 | "Harlow had found his fingerprints on three separate incidents—each tied to the disappearance of someone connected to the market." | | 4 | "They’d both known the truth—that Morris’ death hadn’t been an accident, that the case file had been sealed with something more sinister than bureaucratic red tape." | | 5 | "But hearing it from Tomás, seeing the way his shoulders shook with the effort of holding it in—that cracked something in her." | | 6 | "The scar on his forearm—had he gotten that before or after the knife attack that had cost him his license?" | | 7 | "The medallion—was it a relic of his past life, or a charm against the darkness creeping into their lives?" | | 8 | "With a practiced twist, she slid the book aside, revealing a single shelf of bone tokens—human, animal, and something else she couldn’t identify." | | 9 | "Harlow’s heel struck something hard—bone, maybe, or a loose stone—and she cursed under her breath." | | 10 | "Torches flickered along the walls, casting dancing shadows of figures that might have been people—or might have been something else entirely." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 247 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.02834008097165992 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.004048582995951417 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 15.14 | | std | 8.39 | | cv | 0.554 | | sampleLengths | | 0 | 21 | | 1 | 26 | | 2 | 30 | | 3 | 33 | | 4 | 7 | | 5 | 35 | | 6 | 26 | | 7 | 21 | | 8 | 16 | | 9 | 24 | | 10 | 1 | | 11 | 4 | | 12 | 22 | | 13 | 25 | | 14 | 21 | | 15 | 15 | | 16 | 9 | | 17 | 10 | | 18 | 17 | | 19 | 5 | | 20 | 13 | | 21 | 27 | | 22 | 21 | | 23 | 11 | | 24 | 2 | | 25 | 11 | | 26 | 13 | | 27 | 3 | | 28 | 15 | | 29 | 12 | | 30 | 19 | | 31 | 2 | | 32 | 2 | | 33 | 2 | | 34 | 15 | | 35 | 23 | | 36 | 8 | | 37 | 29 | | 38 | 11 | | 39 | 12 | | 40 | 19 | | 41 | 2 | | 42 | 2 | | 43 | 14 | | 44 | 10 | | 45 | 9 | | 46 | 6 | | 47 | 26 | | 48 | 22 | | 49 | 10 |
| |
| 52.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3493975903614458 | | totalSentences | 83 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 60 | | matches | | 0 | "Instead, he’d vanished into the" | | 1 | "Too late for comfort." | | 2 | "All gone without a trace," |
| | ratio | 0.05 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 60 | | matches | | 0 | "She’d lost track of Tommy" | | 1 | "He was supposed to be" | | 2 | "Her left wrist bore the" | | 3 | "She’d been chasing a lead" | | 4 | "She spun, her pistol half-drawn," | | 5 | "His dark curls were plastered" | | 6 | "he said, voice tight" | | 7 | "she said, stepping closer" | | 8 | "They’d both known the truth—that" | | 9 | "she said finally" | | 10 | "She studied him, weighing his" | | 11 | "She pulled one from the" | | 12 | "He looked at the token," | | 13 | "He turned as they entered," |
| | ratio | 0.233 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 60 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn moved like" | | 2 | "The green neon sign of" | | 3 | "She’d lost track of Tommy" | | 4 | "He was supposed to be" | | 5 | "The bar was nearly empty," | | 6 | "Maps of the city’s older" | | 7 | "Harlow pressed her back against" | | 8 | "Her left wrist bore the" | | 9 | "She’d been chasing a lead" | | 10 | "A place where the line" | | 11 | "The bone token requirement was" | | 12 | "A cough echoed from the" | | 13 | "Harlow’s hand dipped to her" | | 14 | "The voice that emerged was" | | 15 | "She spun, her pistol half-drawn," | | 16 | "Tomás stood there, his olive" | | 17 | "His dark curls were plastered" | | 18 | "he said, voice tight" | | 19 | "Harlow said, keeping her weapon" |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 76.41% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 4 | | matches | | 0 | "The bone token requirement was the latest twist in her investigation—a detail she’d confirmed through a fence who’d since vanished himself." | | 1 | "The scar on his forearm—had he gotten that before or after the knife attack that had cost him his license?" | | 2 | "Harlow stepped through first, her hand never straying far from her weapon, her eyes scanning the darkness ahead." | | 3 | "Tomás followed, the medallion around his neck glinting once in the faint lamplight before fading into shadow." |
| |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice tight" | | 1 | "Tomás interrupted, voice raw" |
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| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás interrupted (interrupt)" | | 1 | "Harlow pressed (press)" |
| | dialogueSentences | 24 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.2 | | effectiveRatio | 0.167 | |