| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1376 | | totalAiIsmAdverbs | 3 | | 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) | |
| 12.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1376 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "pulsed" | | 1 | "warmth" | | 2 | "shattered" | | 3 | "loomed" | | 4 | "velvet" | | 5 | "flicker" | | 6 | "stomach" | | 7 | "whisper" | | 8 | "pulse" | | 9 | "gloom" | | 10 | "etched" | | 11 | "perfect" | | 12 | "footsteps" | | 13 | "silence" | | 14 | "traced" | | 15 | "beacon" | | 16 | "chill" | | 17 | "throbbed" | | 18 | "predator" | | 19 | "racing" |
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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 | 125 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 125 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1367 | | 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 | 27 | | wordCount | 1351 | | uniqueNames | 14 | | maxNameDensity | 0.67 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 9 | | Richmond | 2 | | Park | 1 | | London | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Heartstone | 3 | | Pendant | 1 | | October | 1 | | Evan | 2 | | Cardiff | 1 | | Brixton | 1 |
| | persons | | 0 | "Rory" | | 1 | "Yu-Fei" | | 2 | "Cheung" | | 3 | "Heartstone" | | 4 | "Pendant" | | 5 | "Evan" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Golden" | | 4 | "October" | | 5 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "thickness that seemed to absorb the edges of things" | | 1 | "as though testing the temperature of bathwater" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.732 | | wordCount | 1367 | | matches | | 0 | "no longer mimicking her rhythm but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 56.96 | | std | 37.45 | | cv | 0.658 | | sampleLengths | | 0 | 119 | | 1 | 66 | | 2 | 121 | | 3 | 6 | | 4 | 122 | | 5 | 3 | | 6 | 77 | | 7 | 14 | | 8 | 83 | | 9 | 6 | | 10 | 66 | | 11 | 7 | | 12 | 95 | | 13 | 59 | | 14 | 70 | | 15 | 12 | | 16 | 85 | | 17 | 6 | | 18 | 35 | | 19 | 69 | | 20 | 68 | | 21 | 59 | | 22 | 60 | | 23 | 59 |
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| 94.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 125 | | matches | | 0 | "being told" | | 1 | "been gone" | | 2 | "been herded" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 222 | | matches | | 0 | "was wading" | | 1 | "was running" | | 2 | "was tearing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 2 | | flaggedSentences | 8 | | totalSentences | 125 | | ratio | 0.064 | | matches | | 0 | "The crescent-shaped scar on her left wrist—an old souvenir from a shattered juice glass when she was seven—caught the moonlight as she pushed a low branch aside." | | 1 | "One moment she was wading through blackthorn and dog-roses; the next, the air turned soft and honeyed." | | 2 | "She thought they were oaks at first, but when she looked away, they became stones—ancient, pocked standing stones that marked a boundary older than Richmond itself." | | 3 | "She had come here for a reason—the pendant’s insistent heat, the irrational certainty that had gripped her on the pavement outside Silas’ bar, where her flat sat silent and empty above the pub’s raucous Thursday crowd." | | 4 | "She tried to count the standing stones—seven, she thought, though the gloom kept rearranging their number." | | 5 | "Time moved differently here; she understood that without being told." | | 6 | "The oaks—no, stones—had formed a perfect ring, and she stood at its center." | | 7 | "Its face was her face—same sharp chin, same blue eyes—but the smile was wrong." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 395 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.0379746835443038 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002531645569620253 | |
| 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 | 10.94 | | std | 8.92 | | cv | 0.816 | | sampleLengths | | 0 | 40 | | 1 | 37 | | 2 | 7 | | 3 | 35 | | 4 | 8 | | 5 | 15 | | 6 | 9 | | 7 | 27 | | 8 | 7 | | 9 | 17 | | 10 | 11 | | 11 | 26 | | 12 | 14 | | 13 | 3 | | 14 | 13 | | 15 | 2 | | 16 | 19 | | 17 | 16 | | 18 | 4 | | 19 | 2 | | 20 | 8 | | 21 | 25 | | 22 | 4 | | 23 | 5 | | 24 | 9 | | 25 | 36 | | 26 | 4 | | 27 | 12 | | 28 | 4 | | 29 | 15 | | 30 | 3 | | 31 | 3 | | 32 | 2 | | 33 | 13 | | 34 | 37 | | 35 | 22 | | 36 | 1 | | 37 | 13 | | 38 | 5 | | 39 | 14 | | 40 | 11 | | 41 | 16 | | 42 | 20 | | 43 | 17 | | 44 | 6 | | 45 | 12 | | 46 | 2 | | 47 | 3 | | 48 | 28 | | 49 | 16 |
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| 41.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.328 | | totalSentences | 125 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 113 | | matches | | 0 | "Only foxgloves nodding in a" | | 1 | "Soft, bare feet pressing down" | | 2 | "Then, opposite, a flicker of" | | 3 | "Then, slowly, the figure beneath" |
| | ratio | 0.035 | |
| 74.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 113 | | matches | | 0 | "It was still damp from" | | 1 | "She touched it once, then" | | 2 | "She thought they were oaks" | | 3 | "Their perfume cloyed, overripe, like" | | 4 | "Her bright blue eyes, usually" | | 5 | "She checked her phone." | | 6 | "It had been 11:03 for" | | 7 | "She thumbed the screen." | | 8 | "She had come here for" | | 9 | "She had wanted answers." | | 10 | "She turned her head slowly," | | 11 | "She tried to count the" | | 12 | "She heard breathing that wasn’t" | | 13 | "It matched her inhale exactly," | | 14 | "She walked faster, telling herself" | | 15 | "Her shadow, cast by no" | | 16 | "It moved when she didn’t." | | 17 | "Her shadow took another step." | | 18 | "She jerked sideways, heart hammering" | | 19 | "She might walk out to" |
| | ratio | 0.363 | |
| 75.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 113 | | matches | | 0 | "The brambles scratched at Rory’s" | | 1 | "It was still damp from" | | 2 | "The Heartstone Pendant had made" | | 3 | "A faint inner glow leaked" | | 4 | "She touched it once, then" | | 5 | "The crescent-shaped scar on her" | | 6 | "She thought they were oaks" | | 7 | "Wildflowers carpeted the earth beneath" | | 8 | "Bluebells, campion, foxgloves." | | 9 | "Their perfume cloyed, overripe, like" | | 10 | "Her bright blue eyes, usually" | | 11 | "The darkness here had texture," | | 12 | "She checked her phone." | | 13 | "It had been 11:03 for" | | 14 | "She thumbed the screen." | | 15 | "The digits refused to flicker." | | 16 | "A cold thread of unease" | | 17 | "She had come here for" | | 18 | "She had wanted answers." | | 19 | "A twig cracked." |
| | ratio | 0.77 | |
| 88.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 113 | | matches | | 0 | "Now the silver chain was" | | 1 | "Now, standing in this pocket" |
| | ratio | 0.018 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "It was still damp from the drizzle that had chased her across half of London after her last delivery for Yu-Fei Cheung, the insulated bag forgotten beside her b…" | | 1 | "For an hour, the thumbnail-sized crimson gemstone had pulsed against her sternum with a warmth that felt less like jewelry and more like a second heart trying t…" | | 2 | "The darkness here had texture, a velvet thickness that seemed to absorb the edges of things." | | 3 | "When she stared directly at them, they were oaks, their bark etched with whorls that resembled faces if glimpsed indirectly." | | 4 | "But its head hung at an angle that made her own neck ache in sympathetic protest, and its hands were too long, fingers trailing in the foxgloves as though testi…" | | 5 | "Behind her, the breathing resumed, no longer mimicking her rhythm but steady, hungry, close enough that she felt the chill of it on the back of her neck, smelli…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | 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 | |