| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured around [around]" |
| | dialogueSentences | 22 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.167 | | effectiveRatio | 0.091 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1658 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 54.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1658 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "cascading" | | 1 | "glint" | | 2 | "flickered" | | 3 | "sanctuary" | | 4 | "pulsed" | | 5 | "footsteps" | | 6 | "echoing" | | 7 | "electric" | | 8 | "could feel" | | 9 | "weight" | | 10 | "etched" | | 11 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 148 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 148 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1651 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1443 | | uniqueNames | 14 | | maxNameDensity | 0.83 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Herrera | 1 | | London | 2 | | Saint | 1 | | Christopher | 1 | | Glock | 3 | | Raven | 1 | | Nest | 2 | | Tube | 2 | | Veil | 2 | | Market | 2 | | Morris | 4 | | Spanish | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Market" | | 8 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 6.32% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 5 | | matches | | 0 | "not quite fire" | | 1 | "looked like it had been stretched too tig" | | 2 | "sounded like grinding gravel" | | 3 | "feathers that seemed to float without wind, masks carved from bone that watched her as she passed" | | 4 | "looked like a man who had been running fo" |
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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 | 1651 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 164 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 28.47 | | std | 25.2 | | cv | 0.885 | | sampleLengths | | 0 | 45 | | 1 | 68 | | 2 | 2 | | 3 | 84 | | 4 | 53 | | 5 | 38 | | 6 | 40 | | 7 | 43 | | 8 | 39 | | 9 | 7 | | 10 | 27 | | 11 | 76 | | 12 | 3 | | 13 | 56 | | 14 | 18 | | 15 | 5 | | 16 | 43 | | 17 | 14 | | 18 | 12 | | 19 | 79 | | 20 | 62 | | 21 | 16 | | 22 | 49 | | 23 | 12 | | 24 | 45 | | 25 | 4 | | 26 | 20 | | 27 | 61 | | 28 | 71 | | 29 | 106 | | 30 | 7 | | 31 | 2 | | 32 | 2 | | 33 | 41 | | 34 | 18 | | 35 | 6 | | 36 | 23 | | 37 | 29 | | 38 | 17 | | 39 | 14 | | 40 | 43 | | 41 | 38 | | 42 | 4 | | 43 | 1 | | 44 | 18 | | 45 | 17 | | 46 | 9 | | 47 | 1 | | 48 | 28 | | 49 | 32 |
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| 98.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 148 | | matches | | 0 | "was gone" | | 1 | "been stretched" | | 2 | "been buried" | | 3 | "been redacted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 257 | | matches | | 0 | "wasn’t running" | | 1 | "was running" | | 2 | "was beginning" |
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| 20.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 164 | | ratio | 0.043 | | matches | | 0 | "That told her something—he wasn’t running for sanctuary." | | 1 | "The smell changed, too—wet concrete gave way to something older." | | 2 | "A wall of sound—haggling voices, the clink of glass, the sizzle of something cooking over a blue flame." | | 3 | "The vendors were a mix of human and something else—a woman with cat-slit pupils, a man whose fingers were too long, a child whose smile showed too many teeth." | | 4 | "The scar on his left forearm caught the light—a white seam of tissue that she knew from her file had come from a knife attack." | | 5 | "He reached into his pocket, and her hand tightened on the Glock, but he only pulled out a small object—a token, carved from bone, identical to the one she’d used to enter." | | 6 | "Because he was right—she didn’t understand the shape of this world." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1374 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.026200873362445413 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005094614264919942 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 10.07 | | std | 7.77 | | cv | 0.772 | | sampleLengths | | 0 | 16 | | 1 | 18 | | 2 | 11 | | 3 | 7 | | 4 | 2 | | 5 | 25 | | 6 | 16 | | 7 | 6 | | 8 | 12 | | 9 | 2 | | 10 | 13 | | 11 | 16 | | 12 | 13 | | 13 | 20 | | 14 | 13 | | 15 | 2 | | 16 | 4 | | 17 | 3 | | 18 | 12 | | 19 | 1 | | 20 | 18 | | 21 | 3 | | 22 | 5 | | 23 | 8 | | 24 | 6 | | 25 | 24 | | 26 | 12 | | 27 | 1 | | 28 | 1 | | 29 | 17 | | 30 | 23 | | 31 | 4 | | 32 | 3 | | 33 | 9 | | 34 | 13 | | 35 | 10 | | 36 | 2 | | 37 | 1 | | 38 | 1 | | 39 | 19 | | 40 | 9 | | 41 | 11 | | 42 | 6 | | 43 | 1 | | 44 | 27 | | 45 | 4 | | 46 | 10 | | 47 | 23 | | 48 | 2 | | 49 | 16 |
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| 52.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3719512195121951 | | totalSentences | 164 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 125 | | matches | | 0 | "Just a shell of a" | | 1 | "Maybe she did now." | | 2 | "Then the smells: saffron and" | | 3 | "Then she let him go." |
| | ratio | 0.032 | |
| 40.80% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 125 | | matches | | 0 | "She’d been watching him for" | | 1 | "She’d seen him meet with" | | 2 | "He ducked left into a" | | 3 | "Her shoulder brushed damp stone" | | 4 | "She could see the glint" | | 5 | "Her hand went to the" | | 6 | "She needed him alive." | | 7 | "She needed answers." | | 8 | "He burst out of the" | | 9 | "He didn’t slow." | | 10 | "He was running for something" | | 11 | "She followed him past the" | | 12 | "Her suspect vaulted the gate" | | 13 | "They spiraled deeper than any" | | 14 | "She could still hear his" | | 15 | "She took the stairs two" | | 16 | "She’d heard whispers." | | 17 | "Her suspect was already fifty" | | 18 | "He was tall, gaunt, with" | | 19 | "His eyes were the color" |
| | ratio | 0.448 | |
| 52.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 125 | | matches | | 0 | "Rain slashed across Harlow Quinn’s" | | 1 | "The suspect was twenty yards" | | 2 | "She’d been watching him for" | | 3 | "She’d seen him meet with" | | 4 | "Each time, he walked away" | | 5 | "Each time, she was left" | | 6 | "He ducked left into a" | | 7 | "The alley was a gorge" | | 8 | "Her shoulder brushed damp stone" | | 9 | "She could see the glint" | | 10 | "Her hand went to the" | | 11 | "She needed him alive." | | 12 | "She needed answers." | | 13 | "He burst out of the" | | 14 | "The green neon sign of" | | 15 | "He didn’t slow." | | 16 | "That told her something—he wasn’t" | | 17 | "He was running for something" | | 18 | "She followed him past the" | | 19 | "This time toward a set" |
| | ratio | 0.816 | |
| 40.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 125 | | matches | | 0 | "Because he was right—she didn’t" |
| | ratio | 0.008 | |
| 60.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 7 | | matches | | 0 | "Former paramedic, now a ghost who moved through London’s underbelly with the kind of practiced invisibility that came from knowing exactly which shadows to use." | | 1 | "She could see the glint of his Saint Christopher medallion swinging against his chest, catching the distant wash of streetlight." | | 2 | "He was tall, gaunt, with skin that looked like it had been stretched too tight over his skull." | | 3 | "Wait for warrants and jurisdictional handshakes and all the bureaucratic machinery that had failed her for three years." | | 4 | "She pushed through the crowd, keeping her eyes fixed on the suspect’s retreating back, and tried to orient herself in a space that defied every map she’d ever s…" | | 5 | "Stalls sold jars of iridescent powder, feathers that seemed to float without wind, masks carved from bone that watched her as she passed." | | 6 | "A former paramedic who had thrown away his license to treat patients who couldn’t go to hospitals." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 22 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0.2 | | effectiveRatio | 0.091 | |