| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1567 | | 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) | |
| 17.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1567 | | totalAiIsms | 26 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "glinting" | | 1 | "resolve" | | 2 | "jaw clenched" | | 3 | "mosaic" | | 4 | "determined" | | 5 | "echoed" | | 6 | "pulse" | | 7 | "traced" | | 8 | "echoes" | | 9 | "shattered" | | 10 | "scanned" | | 11 | "potential" | | 12 | "gleaming" | | 13 | "stomach" | | 14 | "hulking" | | 15 | "racing" | | 16 | "flicker" | | 17 | "velvet" | | 18 | "charged" | | 19 | "raced" | | 20 | "echoing" | | 21 | "silence" | | 22 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 140 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 140 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 140 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1554 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1544 | | uniqueNames | 10 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Tube | 1 | | Veil | 4 | | Market | 4 | | Eastern | 1 | | Europe | 1 | | Morris | 1 | | Quinn | 13 |
| | persons | | 0 | "Raven" | | 1 | "Market" | | 2 | "Morris" | | 3 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Veil" | | 2 | "Eastern" | | 3 | "Europe" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 117 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a knucklebone" |
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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 | 1554 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 140 | | matches | | |
| 13.88% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 70.64 | | std | 14.03 | | cv | 0.199 | | sampleLengths | | 0 | 83 | | 1 | 75 | | 2 | 71 | | 3 | 57 | | 4 | 77 | | 5 | 83 | | 6 | 53 | | 7 | 56 | | 8 | 103 | | 9 | 72 | | 10 | 69 | | 11 | 97 | | 12 | 64 | | 13 | 60 | | 14 | 47 | | 15 | 68 | | 16 | 81 | | 17 | 58 | | 18 | 66 | | 19 | 59 | | 20 | 66 | | 21 | 89 |
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| 97.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 140 | | matches | | 0 | "were mottled" | | 1 | "was covered" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 286 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 4 | | flaggedSentences | 16 | | totalSentences | 140 | | ratio | 0.114 | | matches | | 0 | "She’d come in hot off a tip that someone would leave the bar tonight carrying forbidden cargo—more dangerous than the usual backroom deals." | | 1 | "Eighteen years on the force taught her timing—and tonight, time was all she had." | | 2 | "With every stride, her sharp jaw clenched; each heartbeat pounded in her ears." | | 3 | "She caught a glimpse of his face—hollow cheeks, eyes darting—before thunder swallowed it." | | 4 | "Her flashlight lay strapped to her belt; she fumbled for it." | | 5 | "Water dripped from exposed beams; the walls were mottled with mold." | | 6 | "A woman in a battered trench coat hawked glass bottles filled with shifting liquid—colors flickering like oil on water." | | 7 | "She scanned the clusters of strangers, every one a potential witness—or a threat." | | 8 | "Bone tokens were the admission fee—proof of some dark pact." | | 9 | "He wore patched leathers; one eye was covered by a tuft of thick hair." | | 10 | "Figures drifted in and out of the lantern glow—a pale man with eyes like liquid silver, a girl whose fingertips sprouted black feathers." | | 11 | "And yet if she lost him now—if he disappeared into this illegal market with its rumored supernatural clientele—her case would crumble." | | 12 | "She’d never found what happened to him—something that had never made sense in police files." | | 13 | "Quinn weighed the options: descend into unknown territory with only a service revolver and half-broken flashlight—or retreat, regroup, and risk watching her suspect vanish for good." | | 14 | "She felt the ground’s faint tremble from trains long underground, then recognized the hollow hum as something else—like the heartbeat of the station itself, bleeding magic into the bricks." | | 15 | "With every fiber of her disciplined mind screaming caution, she descended—into the Veil Market’s embrace." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1567 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.02169751116783663 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006381620931716656 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 140 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 140 | | mean | 11.1 | | std | 5.75 | | cv | 0.518 | | sampleLengths | | 0 | 16 | | 1 | 22 | | 2 | 23 | | 3 | 15 | | 4 | 7 | | 5 | 12 | | 6 | 8 | | 7 | 11 | | 8 | 17 | | 9 | 14 | | 10 | 13 | | 11 | 6 | | 12 | 15 | | 13 | 15 | | 14 | 6 | | 15 | 17 | | 16 | 5 | | 17 | 7 | | 18 | 7 | | 19 | 13 | | 20 | 12 | | 21 | 12 | | 22 | 13 | | 23 | 5 | | 24 | 12 | | 25 | 11 | | 26 | 10 | | 27 | 13 | | 28 | 9 | | 29 | 17 | | 30 | 6 | | 31 | 11 | | 32 | 20 | | 33 | 11 | | 34 | 8 | | 35 | 6 | | 36 | 14 | | 37 | 7 | | 38 | 9 | | 39 | 15 | | 40 | 8 | | 41 | 2 | | 42 | 7 | | 43 | 12 | | 44 | 12 | | 45 | 9 | | 46 | 5 | | 47 | 16 | | 48 | 2 | | 49 | 12 |
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| 48.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.32857142857142857 | | totalSentences | 140 | | uniqueOpeners | 46 | |
| 24.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 137 | | matches | | 0 | "Just distant echoes of drip" |
| | ratio | 0.007 | |
| 68.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 137 | | matches | | 0 | "She’d come in hot off" | | 1 | "She’d recognized the figure slipping" | | 2 | "Her coat, heavy wool from" | | 3 | "She pressed her hand against" | | 4 | "She burst into a narrow" | | 5 | "Her breath curled in the" | | 6 | "She spotted the man’s retreat" | | 7 | "He was faster, lithe, ghostlike." | | 8 | "She caught a glimpse of" | | 9 | "He sprinted toward a manhole" | | 10 | "She dropped her shoulder into" | | 11 | "She tested the edge with" | | 12 | "She slid down, each rung" | | 13 | "Her flashlight lay strapped to" | | 14 | "She pushed her sleeves above" | | 15 | "She traced it absently as" | | 16 | "She whispered his name: Her" | | 17 | "She squared her shoulders and" | | 18 | "Her department had files on" | | 19 | "She advanced through arched doorways," |
| | ratio | 0.38 | |
| 76.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 137 | | matches | | 0 | "Quinn burst through the neon-lit" | | 1 | "The green glow from the" | | 2 | "She’d come in hot off" | | 3 | "She’d recognized the figure slipping" | | 4 | "Her coat, heavy wool from" | | 5 | "Water weighed her shoulders, seeped" | | 6 | "She pressed her hand against" | | 7 | "The suspect vanished around a" | | 8 | "Quinn flung herself after him," | | 9 | "She burst into a narrow" | | 10 | "Her breath curled in the" | | 11 | "She spotted the man’s retreat" | | 12 | "He was faster, lithe, ghostlike." | | 13 | "Lightning cracked overhead, momentarily illuminating" | | 14 | "She caught a glimpse of" | | 15 | "He sprinted toward a manhole" | | 16 | "Quinn skidded to a halt" | | 17 | "Every instinct screamed that she’d" | | 18 | "She dropped her shoulder into" | | 19 | "The hollow clang echoed into" |
| | ratio | 0.766 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 137 | | matches | | 0 | "Now she chased him into" | | 1 | "Now, each clue pointed toward" | | 2 | "If she descended, there would" |
| | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 2 | | matches | | 0 | "She’d heard whispers of the Veil Market, an underground bazaar that shifted stations every full moon and dealt in cursed charms and banned alchemical substances…" | | 1 | "Each flicker of movement teased her senses, tugging at that part of her that had never fully believed reality ended at the city’s surface." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "She whispered (whisper)" | | 1 | "A flickering lightbulb revealed (reveal)" |
| | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |