| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 85.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1391 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "suddenly" | | 2 | "very" | | 3 | "slightly" |
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| 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) | |
| 67.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "measured" | | 1 | "chaotic" | | 2 | "standard" | | 3 | "pristine" | | 4 | "etched" | | 5 | "intricate" | | 6 | "could feel" | | 7 | "chill" |
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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 | 0 | | narrationSentences | 114 | | matches | (empty) | |
| 92.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "tried to" | | 2 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1391 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 8 | | matches | | 0 | "Looks like a standard turf war, Thorne said, standing and wiping his hands on his trousers." | | 1 | "He was not beaten, Quinn said, her voice a low, steady rasp that cut through the hum of the station." | | 2 | "A Veil Compass, Eva breathed, stepping closer, her green eyes wide behind her lenses." | | 3 | "If it was a mugging, Quinn said, her mind snapping the pieces together with brutal clarity, the killer would have taken …" | | 4 | "They knew exactly what it was, Quinn said." | | 5 | "The killer used the victim as an anchor, Eva said, her voice trembling slightly as she adjusted her glasses." | | 6 | "You will do no such thing, Thorne, Quinn said, her tone leaving no room for argument." | | 7 | "I am going to find out who opened the door, Quinn said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1391 | | uniqueNames | 20 | | maxNameDensity | 1.58 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 2 | | Quinn | 22 | | Metropolitan | 1 | | Police | 1 | | Veil | 4 | | Market | 2 | | Thorne | 11 | | Eva | 6 | | Kowalski | 1 | | Oxford-educated | 1 | | British | 1 | | Museum | 1 | | Met | 1 | | Compass | 2 | | Shade | 2 | | Shade-crafted | 1 | | Bermondsey | 1 | | Morris | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Thorne" | | 5 | "Eva" | | 6 | "Kowalski" | | 7 | "Compass" | | 8 | "Morris" |
| | places | | 0 | "Metropolitan" | | 1 | "British" | | 2 | "Met" |
| | globalScore | 0.709 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1391 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 44.87 | | std | 32.58 | | cv | 0.726 | | sampleLengths | | 0 | 73 | | 1 | 124 | | 2 | 33 | | 3 | 20 | | 4 | 51 | | 5 | 61 | | 6 | 132 | | 7 | 20 | | 8 | 27 | | 9 | 4 | | 10 | 60 | | 11 | 13 | | 12 | 38 | | 13 | 17 | | 14 | 51 | | 15 | 36 | | 16 | 38 | | 17 | 40 | | 18 | 52 | | 19 | 15 | | 20 | 45 | | 21 | 133 | | 22 | 9 | | 23 | 46 | | 24 | 30 | | 25 | 37 | | 26 | 62 | | 27 | 36 | | 28 | 12 | | 29 | 54 | | 30 | 22 |
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| 89.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 114 | | matches | | 0 | "was illuminated" | | 1 | "was torn" | | 2 | "is forced" | | 3 | "was mugged" | | 4 | "was clenched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 236 | | matches | | 0 | "were bleeding" | | 1 | "was tracking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1400 | | adjectiveStacks | 1 | | stackExamples | | 0 | "exact same metallic tang" |
| | adverbCount | 37 | | adverbRatio | 0.02642857142857143 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.013571428571428571 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 12.2 | | std | 6.87 | | cv | 0.563 | | sampleLengths | | 0 | 15 | | 1 | 30 | | 2 | 10 | | 3 | 2 | | 4 | 16 | | 5 | 21 | | 6 | 29 | | 7 | 22 | | 8 | 15 | | 9 | 18 | | 10 | 19 | | 11 | 6 | | 12 | 18 | | 13 | 9 | | 14 | 8 | | 15 | 12 | | 16 | 16 | | 17 | 5 | | 18 | 5 | | 19 | 17 | | 20 | 8 | | 21 | 5 | | 22 | 11 | | 23 | 14 | | 24 | 16 | | 25 | 15 | | 26 | 12 | | 27 | 7 | | 28 | 20 | | 29 | 21 | | 30 | 18 | | 31 | 21 | | 32 | 33 | | 33 | 20 | | 34 | 2 | | 35 | 10 | | 36 | 4 | | 37 | 11 | | 38 | 4 | | 39 | 12 | | 40 | 12 | | 41 | 16 | | 42 | 20 | | 43 | 6 | | 44 | 7 | | 45 | 8 | | 46 | 3 | | 47 | 6 | | 48 | 5 | | 49 | 16 |
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| 55.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.37719298245614036 | | totalSentences | 114 | | uniqueOpeners | 43 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 111 | | matches | | 0 | "Usually, it was a chaotic" | | 1 | "Probably a buyer who decided" | | 2 | "Maybe they did not know" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 111 | | matches | | 0 | "She checked the worn leather" | | 1 | "They recognized the sharp jaw" | | 2 | "He looked up as Quinn" | | 3 | "Her brown eyes swept the" | | 4 | "His coat was torn, and" | | 5 | "It was too localized, pooling" | | 6 | "She stepped closer, her boots" | | 7 | "She reached up with a" | | 8 | "He was not beaten, Quinn" | | 9 | "It was a pale, iridescent" | | 10 | "It is a displacement burn." | | 11 | "It happens when organic matter" | | 12 | "She knelt beside the body," | | 13 | "They are incredibly rare." | | 14 | "It points toward the nearest" | | 15 | "It pointed straight down, vibrating" | | 16 | "They took his coin, they" | | 17 | "They knew exactly what it" | | 18 | "She looked at the fractal" | | 19 | "They wanted a door." |
| | ratio | 0.288 | |
| 72.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 111 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "She checked the worn leather" | | 3 | "The full moon was at" | | 4 | "The underground market moved locations" | | 5 | "The vendors, a mix of" | | 6 | "They recognized the sharp jaw" | | 7 | "Quinn ducked under the yellow" | | 8 | "The alcove was illuminated by" | | 9 | "DS Thorne crouched beside the" | | 10 | "He looked up as Quinn" | | 11 | "Looks like a standard turf" | | 12 | "Victim is a known fence." | | 13 | "Pockets are turned inside out." | | 14 | "Quinn did not answer immediately." | | 15 | "Her brown eyes swept the" | | 16 | "The victim was a heavy-set" | | 17 | "His coat was torn, and" | | 18 | "It was too localized, pooling" | | 19 | "She stepped closer, her boots" |
| | ratio | 0.775 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 111 | | matches | | 0 | "If it was a mugging," | | 1 | "Now, the shadows were bleeding" | | 2 | "If there is a door," |
| | ratio | 0.027 | |
| 43.96% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 9 | | matches | | 0 | "The underground market moved locations every full moon, a hidden supernatural black market that sold enchanted goods, banned alchemical substances, and informat…" | | 1 | "They recognized the sharp jaw and closely cropped salt-and-pepper hair of a woman who did not tolerate nonsense." | | 2 | "Eva worked in the restricted archives at the British Museum, and she understood the hidden world better than anyone in the Met, but right now, she looked as tho…" | | 3 | "It was a pale, iridescent black, branching out in jagged, fractal patterns that looked more like frostbite than impact trauma." | | 4 | "The casing was heavily oxidized, bearing a thick patina of verdigris, but the face was pristine, etched with intricate protective sigils that caught the light." | | 5 | "It pointed straight down, vibrating with a low, barely audible hum that made Quinn's teeth ache." | | 6 | "If it was a mugging, Quinn said, her mind snapping the pieces together with brutal clarity, the killer would have taken this." | | 7 | "She had spent three years chasing shadows, trying to understand the supernatural forces that had taken him, hitting dead ends at every turn." | | 8 | "She could feel the cold radiating from the stone, a deep, unnatural chill that mirrored the ice in her own veins." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |