| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 908 | | 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) | |
| 44.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 908 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "silence" | | 1 | "footsteps" | | 2 | "warmth" | | 3 | "glinting" | | 4 | "flicker" | | 5 | "pulse" | | 6 | "gloom" | | 7 | "flickered" | | 8 | "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 | 0 | | narrationSentences | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 24 | | totalWords | 897 | | ratio | 0.027 | | matches | | 0 | "The Raven’s Nest: Open All Night" | | 1 | "Queen of the Jump" | | 2 | "This is it. This is where the truth lives." | | 3 | "knew" | | 4 | "D-locked: Unseen Witnesses Reported" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 847 | | uniqueNames | 19 | | maxNameDensity | 1.18 | | worstName | "Tommy" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tommy" | | discoveredNames | | Detective | 1 | | Harlow | 7 | | Quinn | 1 | | Met | 1 | | London | 1 | | Saint | 2 | | Christopher | 2 | | Raven | 2 | | Nest | 2 | | Open | 1 | | Tommy | 10 | | Soho | 1 | | Sig | 1 | | Sauer | 1 | | Veil | 1 | | Market | 3 | | Morris | 1 | | Unseen | 1 | | Witnesses | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Tommy" | | 7 | "Market" | | 8 | "Morris" |
| | places | | | globalScore | 0.91 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.23 | | wordCount | 897 | | matches | | 0 | "Not the reaction of a man without secrets, but of a man who recognized one being held too close to flame" | | 1 | "Not the scarred medic, but a memory" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 21.36 | | std | 26.22 | | cv | 1.227 | | sampleLengths | | 0 | 99 | | 1 | 57 | | 2 | 120 | | 3 | 20 | | 4 | 72 | | 5 | 57 | | 6 | 4 | | 7 | 5 | | 8 | 56 | | 9 | 6 | | 10 | 6 | | 11 | 14 | | 12 | 41 | | 13 | 35 | | 14 | 37 | | 15 | 13 | | 16 | 8 | | 17 | 5 | | 18 | 17 | | 19 | 2 | | 20 | 4 | | 21 | 9 | | 22 | 24 | | 23 | 24 | | 24 | 3 | | 25 | 5 | | 26 | 11 | | 27 | 18 | | 28 | 6 | | 29 | 5 | | 30 | 3 | | 31 | 3 | | 32 | 26 | | 33 | 13 | | 34 | 14 | | 35 | 9 | | 36 | 4 | | 37 | 3 | | 38 | 3 | | 39 | 18 | | 40 | 12 | | 41 | 6 |
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| 94.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 95 | | matches | | 0 | "being followed" | | 1 | "being held" | | 2 | "were curved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 161 | | matches | | 0 | "was still moving" | | 1 | "was waiting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 1 | | flaggedSentences | 12 | | totalSentences | 101 | | ratio | 0.119 | | matches | | 0 | "She didn’t slow; didn’t breathe heavy." | | 1 | "Her watch glinted cold on her left wrist—5:47 a.m." | | 2 | "He didn’t run—he flowed, jacket collars lifted against the downpour, Saint Christopher medallion chinking softly against the scuffed leather of his shoulder bag." | | 3 | "The chase had begun at The Raven’s Nest when Harlow had walked in under the flickering green neon—*The Raven’s Nest: Open All Night*—and found only Tommy behind the bar, wiping down glassware with a rag that hadn’t seen soap in weeks." | | 4 | "A blurry image of a man in a tailored coat—signet ring glinting—standing in a back room she’d never found." | | 5 | "Harlow went higher, clearing it with the kind of precision that had earned her commendations in uniform and a nickname—*Queen of the Jump*—from older officers who resented her being fast–tracked." | | 6 | "Through the rusted grate, she glimpsed a flicker of light—greenish, like stained sea glass—moving downward into darkness." | | 7 | "The air below was thick, warm, layered with smoke and something metallic—old blood?" | | 8 | "That word—judgment—curdled in her chest." | | 9 | "The walls pulsed with bioluminescent veins—fungus?" | | 10 | "But—*" | | 11 | "She saw Tommy, yes, clearly, but also another Tommy—pale, gaunt, eyes hollow, chain around his ankle." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 861 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03716608594657375 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006968641114982578 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 8.88 | | std | 7.44 | | cv | 0.838 | | sampleLengths | | 0 | 30 | | 1 | 6 | | 2 | 19 | | 3 | 9 | | 4 | 12 | | 5 | 6 | | 6 | 17 | | 7 | 7 | | 8 | 23 | | 9 | 5 | | 10 | 4 | | 11 | 5 | | 12 | 13 | | 13 | 41 | | 14 | 28 | | 15 | 8 | | 16 | 19 | | 17 | 3 | | 18 | 21 | | 19 | 20 | | 20 | 22 | | 21 | 17 | | 22 | 10 | | 23 | 14 | | 24 | 4 | | 25 | 5 | | 26 | 13 | | 27 | 30 | | 28 | 14 | | 29 | 4 | | 30 | 3 | | 31 | 2 | | 32 | 19 | | 33 | 5 | | 34 | 5 | | 35 | 17 | | 36 | 2 | | 37 | 8 | | 38 | 6 | | 39 | 6 | | 40 | 8 | | 41 | 6 | | 42 | 5 | | 43 | 2 | | 44 | 20 | | 45 | 14 | | 46 | 12 | | 47 | 4 | | 48 | 19 | | 49 | 13 |
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| 59.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4158415841584158 | | totalSentences | 101 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 81 | | matches | | 0 | "Just like her partner’s voice" | | 1 | "Then the man had slipped" | | 2 | "Then the street ended." | | 3 | "Too close to the case" | | 4 | "Then, without a word, he" |
| | ratio | 0.062 | |
| 71.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 81 | | matches | | 0 | "She didn’t slow; didn’t breathe" | | 1 | "Her watch glinted cold on" | | 2 | "He didn’t run—he flowed, jacket" | | 3 | "He didn’t look behind him." | | 4 | "He didn’t need to." | | 5 | "He knew she was there." | | 6 | "He’d looked up with that" | | 7 | "She’d slid a surveillance photo" | | 8 | "She kept her Sig Sauer" | | 9 | "She landed hard on the" | | 10 | "She didn’t know where this" | | 11 | "She didn’t know what waited" | | 12 | "She checked her watch again." | | 13 | "You didn’t need a bone" | | 14 | "She holstered the revolver." | | 15 | "She took the metal rungs" | | 16 | "She couldn’t tell." | | 17 | "He almost smiled." | | 18 | "She touched the revolver under" | | 19 | "He stepped closer." |
| | ratio | 0.37 | |
| 95.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 81 | | matches | | 0 | "The rain came down like" | | 1 | "She didn’t slow; didn’t breathe" | | 2 | "Her watch glinted cold on" | | 3 | "Herrera moved like a shadow" | | 4 | "He didn’t run—he flowed, jacket" | | 5 | "He didn’t look behind him." | | 6 | "He didn’t need to." | | 7 | "He knew she was there." | | 8 | "The scar on his forearm" | | 9 | "The chase had begun at" | | 10 | "He’d looked up with that" | | 11 | "She’d slid a surveillance photo" | | 12 | "A blurry image of a" | | 13 | "Tommy had paled." | | 14 | "Tommy darted left, through a" | | 15 | "Harlow followed, boots splashing in" | | 16 | "She kept her Sig Sauer" | | 17 | "Firearms were last resort." | | 18 | "Tommy vaulted over a toppled" | | 19 | "Harlow went higher, clearing it" |
| | ratio | 0.728 | |
| 61.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 81 | | matches | | 0 | "Now, they were deep in" |
| | ratio | 0.012 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "The chase had begun at The Raven’s Nest when Harlow had walked in under the flickering green neon—*The Raven’s Nest: Open All Night*—and found only Tommy behind…" | | 1 | "Harlow went higher, clearing it with the kind of precision that had earned her commendations in uniform and a nickname—*Queen of the Jump*—from older officers w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |