| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 34 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0.071 | | effectiveRatio | 0.059 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1483 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 93.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1483 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 74 | | ratio | 0 | | matches | (empty) | |
| 69.86% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 26 | | markdownWords | 95 | | totalWords | 1460 | | ratio | 0.065 | | matches | | 0 | "“Rory, rush. Big order to Covent Garden. Doritos—" | | 1 | "—and beer. Don’t be late or I’ll feed you to the dragon maps.”" | | 2 | "Not here." | | 3 | "Oh." | | 4 | "Eva Morgan." | | 5 | "Come. I can help. Safe." | | 6 | "fighting" | | 7 | "I bought you a drink" | | 8 | "spymaster" | | 9 | "Evan" | | 10 | "Sorry. I didn’t mean—" | | 11 | "‘He breaks things. And then he says he’s sorry. And I believe him. I always do.’" | | 12 | "You couldn’t have" | | 13 | "wanted" | | 14 | "you" | | 15 | "alive" | | 16 | "live" | | 17 | "‘No. My sister would have.’" | | 18 | "“For my angel,”" | | 19 | "“Lost,”" | | 20 | "“Good. Get another one. Better.”" | | 21 | "“Rory! You said you’d be here in ten minutes! The Doritos—" | | 22 | "!”" | | 23 | "You" | | 24 | "drag" | | 25 | "you" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 903 | | uniqueNames | 13 | | maxNameDensity | 2.66 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Rory | 24 | | Yu-Fei | 4 | | Oh | 2 | | Cardiff | 1 | | University | 1 | | Paris | 1 | | Evan | 3 | | Eva | 21 | | London | 1 | | Thames | 1 |
| | persons | | 0 | "Raven" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Yu-Fei" | | 4 | "University" | | 5 | "Evan" | | 6 | "Eva" |
| | places | | 0 | "Cardiff" | | 1 | "Paris" | | 2 | "London" | | 3 | "Thames" |
| | globalScore | 0.171 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "As if chasing down debtors and delivering dumplings could pay back a man who gave her a roof" |
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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 | 1460 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 125 | | matches | | 0 | "sent that postcard—*Come" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 40.56 | | std | 28.11 | | cv | 0.693 | | sampleLengths | | 0 | 96 | | 1 | 89 | | 2 | 70 | | 3 | 1 | | 4 | 46 | | 5 | 69 | | 6 | 21 | | 7 | 47 | | 8 | 35 | | 9 | 33 | | 10 | 22 | | 11 | 76 | | 12 | 47 | | 13 | 16 | | 14 | 25 | | 15 | 96 | | 16 | 23 | | 17 | 69 | | 18 | 44 | | 19 | 5 | | 20 | 4 | | 21 | 55 | | 22 | 81 | | 23 | 8 | | 24 | 42 | | 25 | 23 | | 26 | 79 | | 27 | 21 | | 28 | 18 | | 29 | 63 | | 30 | 16 | | 31 | 27 | | 32 | 53 | | 33 | 35 | | 34 | 2 | | 35 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 109 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 15 | | semicolonCount | 1 | | flaggedSentences | 13 | | totalSentences | 125 | | ratio | 0.104 | | matches | | 0 | "The scar on her left wrist—crescent-shaped, a ghost from a childhood fall off a tree—glints as she twists the rag." | | 1 | "She checks her watch—20 past nine—and grabs her jacket, slinging it over her arm." | | 2 | "Her face is a puzzle—familiar bones, a mouth that’s set too firm, eyes that narrow like she’s trying to place a voice." | | 3 | "*Not here.* Not after—" | | 4 | "*Eva Morgan.* They’d been inseparable at Cardiff University—shared a bed, a bottle of cheap wine, a dream of moving to Paris together." | | 5 | "Then Eva sent that postcard—*Come." | | 6 | "Eva has always been a fighter—kicked a policeman once for making a comment about Rory’s skirt." | | 7 | "The former spymaster, the man who taught her to read a room, who fixed the boiler when her radiator exploded, who never asked questions—at least, not out loud." | | 8 | "Eva slid the whiskey glass over; Rory wraps her hands around it, warm through the condensation." | | 9 | "I didn’t mean—*" | | 10 | "She used to write poetry—bad, sappy stuff, about stars and sunsets." | | 11 | "Eva taps the scar on her own wrist—thinner, paler than Rory’s." | | 12 | "There, against her skin, is a silver chain—thin, delicate, a tiny locket." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 911 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.027442371020856202 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003293084522502744 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 11.68 | | std | 11.6 | | cv | 0.993 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 21 | | 3 | 20 | | 4 | 20 | | 5 | 8 | | 6 | 39 | | 7 | 16 | | 8 | 14 | | 9 | 12 | | 10 | 6 | | 11 | 4 | | 12 | 13 | | 13 | 18 | | 14 | 22 | | 15 | 3 | | 16 | 4 | | 17 | 1 | | 18 | 8 | | 19 | 12 | | 20 | 24 | | 21 | 2 | | 22 | 7 | | 23 | 1 | | 24 | 22 | | 25 | 20 | | 26 | 5 | | 27 | 3 | | 28 | 9 | | 29 | 2 | | 30 | 10 | | 31 | 11 | | 32 | 3 | | 33 | 1 | | 34 | 11 | | 35 | 5 | | 36 | 8 | | 37 | 3 | | 38 | 16 | | 39 | 2 | | 40 | 33 | | 41 | 2 | | 42 | 19 | | 43 | 6 | | 44 | 6 | | 45 | 9 | | 46 | 13 | | 47 | 3 | | 48 | 1 | | 49 | 28 |
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| 46.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.336 | | totalSentences | 125 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 90 | | matches | | 0 | "Then Rory left Evan, the" | | 1 | "Then Eva sent that postcard—*Come." | | 2 | "Somewhere, a clock chimes." | | 3 | "Just a little." |
| | ratio | 0.044 | |
| 95.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 90 | | matches | | 0 | "Her hair, still black but" | | 1 | "She checks her watch—20 past" | | 2 | "She’s taller than Rory remembers," | | 3 | "Her face is a puzzle—familiar" | | 4 | "I can help." | | 5 | "She grabs the rag, dabs" | | 6 | "She nods to the corner" | | 7 | "She steps closer, eyeing the" | | 8 | "He’d found her in London," | | 9 | "She sits, hard, the chair" | | 10 | "I didn’t mean—*" | | 11 | "Her voice cracks" | | 12 | "She takes a drink of" | | 13 | "It burns her chest." | | 14 | "She pauses, stares at the" | | 15 | "She used to write poetry—bad," | | 16 | "She stopped writing." | | 17 | "She’d forgotten she was wearing" | | 18 | "She’d worn it every day" | | 19 | "she’d told Yu-Fei, who’d handed" |
| | ratio | 0.311 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 90 | | matches | | 0 | "The Raven’s Nest hums with" | | 1 | "The green neon sign above" | | 2 | "Rory Carter is behind the" | | 3 | "Her hair, still black but" | | 4 | "The scar on her left" | | 5 | "Yu-Fei, the restaurant owner, called" | | 6 | "The dragon maps are the" | | 7 | "Rory doubts Yu-Fei actually owns" | | 8 | "She checks her watch—20 past" | | 9 | "Leaves the rag on the" | | 10 | "The bell above the door" | | 11 | "Rory pauses, half-glancing up." | | 12 | "A woman stands there, rain" | | 13 | "She’s taller than Rory remembers," | | 14 | "Her face is a puzzle—familiar" | | 15 | "Rory’s throat tightens." | | 16 | "The name comes out as" | | 17 | "The woman steps farther into" | | 18 | "Neon light flickers over her:" | | 19 | "Rory’s hands freeze around her" |
| | ratio | 0.889 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "As if chasing down debtors" |
| | ratio | 0.011 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "A woman stands there, rain beading on her coat, blocking the green light." | | 1 | "Her face is a puzzle—familiar bones, a mouth that’s set too firm, eyes that narrow like she’s trying to place a voice." | | 2 | "The former spymaster, the man who taught her to read a room, who fixed the boiler when her radiator exploded, who never asked questions—at least, not out loud." | | 3 | "As if chasing down debtors and delivering dumplings could pay back a man who gave her a roof." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 34 | | tagDensity | 0.147 | | leniency | 0.294 | | rawRatio | 0.2 | | effectiveRatio | 0.059 | |