| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1395 | | 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) | |
| 42.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1395 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "chaotic" | | 1 | "flickered" | | 2 | "echo" | | 3 | "pulsed" | | 4 | "echoed" | | 5 | "weight" | | 6 | "sentinel" | | 7 | "gloom" | | 8 | "vibrated" | | 9 | "footsteps" | | 10 | "perfect" | | 11 | "predator" | | 12 | "pristine" | | 13 | "racing" | | 14 | "pulse" | | 15 | "loomed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 160 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 160 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 50 | | totalWords | 1399 | | ratio | 0.036 | | matches | | 0 | "Enter Richmond Park through the pedestrian gate. Walk due east until you hit the dead bracken. Step between the wooden monoliths. Leave the food on the flat rock." | | 1 | "Right. Drop the pork buns, take a photo for proof of delivery, and bolt." | | 2 | "Hel portal." | | 3 | "Chirp." | | 4 | "Chirp. Chirp." | | 5 | "Snap." | | 6 | "Patter-patter-patter." | | 7 | "Chirp." |
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| 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 | 43 | | wordCount | 1366 | | uniqueNames | 16 | | maxNameDensity | 0.95 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 3 | | Park | 3 | | Yu-Fei | 2 | | Cheung | 2 | | Golden | 2 | | Empress | 2 | | London | 3 | | Heartstone | 2 | | Pendant | 1 | | Dymas | 1 | | Hel | 2 | | Grove | 2 | | Rory | 13 | | Cardiff | 1 | | Directly | 1 | | One | 3 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Cheung" | | 2 | "Empress" | | 3 | "Heartstone" | | 4 | "Pendant" | | 5 | "Rory" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Golden" | | 3 | "London" | | 4 | "Grove" | | 5 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like two wet stones grinding toget" |
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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 | 1399 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 20.28 | | std | 16.6 | | cv | 0.819 | | sampleLengths | | 0 | 11 | | 1 | 70 | | 2 | 34 | | 3 | 6 | | 4 | 18 | | 5 | 79 | | 6 | 12 | | 7 | 26 | | 8 | 14 | | 9 | 38 | | 10 | 8 | | 11 | 37 | | 12 | 9 | | 13 | 12 | | 14 | 39 | | 15 | 8 | | 16 | 39 | | 17 | 2 | | 18 | 22 | | 19 | 28 | | 20 | 1 | | 21 | 24 | | 22 | 2 | | 23 | 28 | | 24 | 9 | | 25 | 14 | | 26 | 42 | | 27 | 1 | | 28 | 3 | | 29 | 10 | | 30 | 33 | | 31 | 4 | | 32 | 8 | | 33 | 4 | | 34 | 38 | | 35 | 25 | | 36 | 28 | | 37 | 1 | | 38 | 14 | | 39 | 3 | | 40 | 29 | | 41 | 37 | | 42 | 16 | | 43 | 25 | | 44 | 10 | | 45 | 41 | | 46 | 3 | | 47 | 46 | | 48 | 1 | | 49 | 6 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 160 | | matches | | 0 | "been seven" | | 1 | "was gone" | | 2 | "was flattened" | | 3 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 212 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 164 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1381 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 29 | | adverbRatio | 0.020999275887038378 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0065170166545981175 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 8.53 | | std | 6.14 | | cv | 0.72 | | sampleLengths | | 0 | 11 | | 1 | 8 | | 2 | 18 | | 3 | 9 | | 4 | 14 | | 5 | 9 | | 6 | 5 | | 7 | 7 | | 8 | 13 | | 9 | 21 | | 10 | 6 | | 11 | 7 | | 12 | 4 | | 13 | 7 | | 14 | 3 | | 15 | 12 | | 16 | 6 | | 17 | 18 | | 18 | 13 | | 19 | 12 | | 20 | 15 | | 21 | 12 | | 22 | 3 | | 23 | 13 | | 24 | 5 | | 25 | 2 | | 26 | 3 | | 27 | 1 | | 28 | 13 | | 29 | 5 | | 30 | 25 | | 31 | 8 | | 32 | 8 | | 33 | 5 | | 34 | 10 | | 35 | 2 | | 36 | 7 | | 37 | 13 | | 38 | 9 | | 39 | 4 | | 40 | 8 | | 41 | 6 | | 42 | 19 | | 43 | 14 | | 44 | 8 | | 45 | 2 | | 46 | 13 | | 47 | 14 | | 48 | 10 | | 49 | 2 |
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| 64.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4268292682926829 | | totalSentences | 164 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 123 | | matches | | 0 | "Then 23:14 again." | | 1 | "Just a ceiling of black" | | 2 | "Directly to her right." | | 3 | "Just the gnarled oak monolith" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 123 | | matches | | 0 | "She pulled the crumpled takeaway" | | 1 | "It did not fade." | | 2 | "It cut out like a" | | 3 | "Her ears popped." | | 4 | "She swallowed hard, rubbing the" | | 5 | "Her boots sank into lush" | | 6 | "Their sweet, heavy perfume choked" | | 7 | "She pulled her phone from" | | 8 | "She clicked her flashlight on." | | 9 | "Her voice sounded flat." | | 10 | "She marched toward the central" | | 11 | "She hooked a finger into" | | 12 | "It beat against her palm" | | 13 | "Her unknown benefactor’s warning echoed" | | 14 | "It sounded like two wet" | | 15 | "It did not bounce back." | | 16 | "It stayed bent, trembling under" | | 17 | "Her shoulders bumped against the" | | 18 | "She set the paper bag" | | 19 | "She spun, wielding the heavy" |
| | ratio | 0.276 | |
| 86.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 123 | | matches | | 0 | "The ancient oak standing stone" | | 1 | "Rory wiped her slick fingertips" | | 2 | "She pulled the crumpled takeaway" | | 3 | "The delivery notes had been" | | 4 | "Yu-Fei Cheung had taken the" | | 5 | "Rory stepped between the boundary" | | 6 | "The distant hum of London" | | 7 | "It did not fade." | | 8 | "It cut out like a" | | 9 | "Her ears popped." | | 10 | "She swallowed hard, rubbing the" | | 11 | "Her boots sank into lush" | | 12 | "A dozen paces behind her," | | 13 | "Here, inside the ring of" | | 14 | "Bluebells, foxgloves, and blood-red poppies" | | 15 | "Their sweet, heavy perfume choked" | | 16 | "She pulled her phone from" | | 17 | "The screen flickered." | | 18 | "The battery icon dropped from" | | 19 | "The digital clock read 23:14." |
| | ratio | 0.748 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | | ratio | 0.008 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 5 | | matches | | 0 | "The white beam cut through the clearing, illuminating a circle of towering oak monoliths that looked less like carved wood and more like petrified giants." | | 1 | "Every step crushed dozens of blooms, releasing a sickly, fermented floral stench that coated the back of her throat." | | 2 | "She set the paper bag down on the stone without looking, her fingers fumbling with the folded top." | | 3 | "It was her own voice, pitch-perfect, yet hollow, as though spoken through a long lead pipe." | | 4 | "Evan used to play mind games, moving her keys, gaslighting her until she doubted her own eyes." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "Rory said, her voice tight" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.5 | | effectiveRatio | 0.333 | |