| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.16% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1302 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1302 | | totalAiIsms | 29 | | found | | 0 | | | 1 | | | 2 | | word | "down her spine" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | word | "moth to a flame" | | count | 1 |
| | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "silence" | | 1 | "loomed" | | 2 | "down her spine" | | 3 | "throbbed" | | 4 | "rhythmic" | | 5 | "pulse" | | 6 | "beacon" | | 7 | "intensity" | | 8 | "navigating" | | 9 | "predictable" | | 10 | "echoed" | | 11 | "scanning" | | 12 | "flicker" | | 13 | "calculated" | | 14 | "predator" | | 15 | "vibrated" | | 16 | "moth to a flame" | | 17 | "porcelain" | | 18 | "analyzed" | | 19 | "resonance" | | 20 | "searing" | | 21 | "churning" |
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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 | 83 | | matches | | |
| 56.80% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1299 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 1295 | | uniqueNames | 9 | | maxNameDensity | 0.54 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Fae | 1 | | Grove | 1 | | Heartstone | 3 | | London | 3 | | Golden | 1 | | Empress | 1 | | Rory | 7 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Fae" | | 3 | "Grove" | | 4 | "Heartstone" | | 5 | "London" | | 6 | "Golden" |
| | globalScore | 1 | | windowScore | 1 | |
| 47.26% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like a warning beacon" | | 1 | "shadows that seemed to detach themselves from the trees" | | 2 | "looked like porcelain stretched over a vo" |
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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 | 1299 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 74.42% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 59.05 | | std | 24.24 | | cv | 0.41 | | sampleLengths | | 0 | 84 | | 1 | 109 | | 2 | 20 | | 3 | 87 | | 4 | 56 | | 5 | 64 | | 6 | 47 | | 7 | 45 | | 8 | 18 | | 9 | 80 | | 10 | 29 | | 11 | 80 | | 12 | 51 | | 13 | 68 | | 14 | 10 | | 15 | 73 | | 16 | 70 | | 17 | 63 | | 18 | 47 | | 19 | 57 | | 20 | 57 | | 21 | 84 |
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| 88.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 83 | | matches | | 0 | "was supposed" | | 1 | "been tasked" | | 2 | "being observed" | | 3 | "were attracted" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 205 | | matches | | 0 | "was stuttering" | | 1 | "were mimicking" | | 2 | "were circling" | | 3 | "were not approaching" | | 4 | "was pulsing" | | 5 | "was reacting" | | 6 | "was searing" | | 7 | "were still waiting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 7 | | flaggedSentences | 10 | | totalSentences | 83 | | ratio | 0.12 | | matches | | 0 | "The air in Richmond Park did not merely cool as the sun dipped behind the horizon; it curdled." | | 1 | "One moment she was in a London park; the next, the air turned dense and floral, smelling of crushed violets and something metallic, like copper coins left to rot in stagnant water." | | 2 | "This wasn't the predictable malice of an ex-boyfriend or the cold pressure of a law exam; it was the prehistoric, heavy awareness of being observed by something that viewed her as an intrusion." | | 3 | "That was the mistake the prey always made; it triggered the predator’s instinct to chase." | | 4 | "The sound grew—a low, discordant hum that vibrated in her marrow." | | 5 | "The entities were not approaching; they were circling, cutting off the path back to the park." | | 6 | "A twig snapped—a sharp, violent sound like a breaking bone." | | 7 | "Her training in the Golden Empress, the chaos of the London streets, the long, grueling nights in the library—all of it had forged a resilience that refused to snap under pressure." | | 8 | "The crescent scar on her skin wasn't just a mark of a childhood accident; it was a resonance point." | | 9 | "It didn't just glow; it tore a hole in the localized reality of the grove." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1302 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.02227342549923195 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008448540706605223 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 15.65 | | std | 7.84 | | cv | 0.501 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 18 | | 3 | 30 | | 4 | 15 | | 5 | 24 | | 6 | 24 | | 7 | 25 | | 8 | 21 | | 9 | 20 | | 10 | 8 | | 11 | 4 | | 12 | 32 | | 13 | 5 | | 14 | 24 | | 15 | 14 | | 16 | 16 | | 17 | 11 | | 18 | 29 | | 19 | 12 | | 20 | 19 | | 21 | 33 | | 22 | 32 | | 23 | 2 | | 24 | 13 | | 25 | 8 | | 26 | 19 | | 27 | 18 | | 28 | 18 | | 29 | 17 | | 30 | 18 | | 31 | 29 | | 32 | 16 | | 33 | 4 | | 34 | 13 | | 35 | 12 | | 36 | 8 | | 37 | 5 | | 38 | 3 | | 39 | 15 | | 40 | 25 | | 41 | 7 | | 42 | 17 | | 43 | 4 | | 44 | 23 | | 45 | 11 | | 46 | 10 | | 47 | 3 | | 48 | 16 | | 49 | 16 |
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| 25.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.2289156626506024 | | totalSentences | 83 | | uniqueOpeners | 19 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | | ratio | 0.012 | |
| 34.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 82 | | matches | | 0 | "Her boots crunched against the" | | 1 | "She kept her gaze fixed" | | 2 | "They were ancient, scarred by" | | 3 | "It was warm, almost burning" | | 4 | "It was supposed to be" | | 5 | "You are not wanted here," | | 6 | "She stepped into the clearing." | | 7 | "They stood tall and motionless," | | 8 | "She checked her watch, but" | | 9 | "She had been tasked with" | | 10 | "She took a slow, deliberate" | | 11 | "She took a step forward," | | 12 | "She reached into her pocket," | | 13 | "she whispered, her voice sounding" | | 14 | "She blinked, her heart slamming" | | 15 | "It wasn't a shadow." | | 16 | "It held the quality of" | | 17 | "It moved with a rhythmic," | | 18 | "She didn't turn her back." | | 19 | "She didn't run." |
| | ratio | 0.463 | |
| 8.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 82 | | matches | | 0 | "The air in Richmond Park" | | 1 | "Rory pulled her woolen coat" | | 2 | "Her boots crunched against the" | | 3 | "She kept her gaze fixed" | | 4 | "They were ancient, scarred by" | | 5 | "The crescent-shaped scar on her" | | 6 | "It was warm, almost burning" | | 7 | "It was supposed to be" | | 8 | "You are not wanted here," | | 9 | "Rory reached the inner perimeter" | | 10 | "The shift was immediate." | | 11 | "She stepped into the clearing." | | 12 | "The wildflowers here, which should" | | 13 | "They stood tall and motionless," | | 14 | "She checked her watch, but" | | 15 | "She had been tasked with" | | 16 | "She took a slow, deliberate" | | 17 | "Years of navigating the wreckage" | | 18 | "This wasn't the predictable malice" | | 19 | "She took a step forward," |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 13 | | matches | | 0 | "She kept her gaze fixed straight ahead, past the skeletal silhouettes of the ancient trees, toward the cluster of dark, jagged shapes that marked the boundary o…" | | 1 | "The crescent-shaped scar on her left wrist throbbed, a phantom sensation that matched the rhythmic pulse of the Heartstone pendant hidden beneath her scarf." | | 2 | "The wildflowers here, which should have been long dead in the seasonal bite of autumn, bloomed with the vibrant, sick intensity of a bruise." | | 3 | "This wasn't the predictable malice of an ex-boyfriend or the cold pressure of a law exam; it was the prehistoric, heavy awareness of being observed by something…" | | 4 | "She took a step forward, and the sound of her boot hitting the grass echoed back at her a fraction of a second late, as if the space itself were mimicking her." | | 5 | "The pendant against her chest flared, a sudden, blinding spike of heat that forced her to gasp." | | 6 | "The crimson light saturated her vision, casting long, frantic shadows that seemed to detach themselves from the trees." | | 7 | "The flowers began to wilt, their colors leaching out until they were grey, skeletal husks that chattered in the light breeze like teeth." | | 8 | "The Heartstone pendant was pulsing rapidly now, a feverish, agitated light that strobed against her skin." | | 9 | "It had no face, only a smooth expanse of pale, reflective skin that looked like porcelain stretched over a void." | | 10 | "Her training in the Golden Empress, the chaos of the London streets, the long, grueling nights in the library—all of it had forged a resilience that refused to …" | | 11 | "She turned and sprinted, her lungs burning, her boots churning the frozen earth." | | 12 | "The world was quiet, the streetlamps flickering in the distance, the city continuing to breathe as if nothing had happened." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |