| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 918 | | totalAiIsmAdverbs | 2 | | 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) | |
| 40.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 918 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "scanned" | | 2 | "silence" | | 3 | "etched" | | 4 | "beacon" | | 5 | "chill" | | 6 | "anticipation" | | 7 | "constructed" | | 8 | "solitary" | | 9 | "chilling" | | 10 | "intricate" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 3 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 110 | | matches | (empty) | |
| 77.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 922 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 900 | | uniqueNames | 25 | | maxNameDensity | 1.78 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Detective | 3 | | Harlow | 1 | | Quinn | 16 | | Camden | 1 | | Kowalski | 6 | | Victim | 1 | | Tony | 1 | | Dickerson | 1 | | Found | 1 | | Banksy | 1 | | Huh | 1 | | Elisa | 1 | | Silence | 1 | | Evie | 6 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Exclusive | 1 | | Browning | 1 | | Kris | 1 | | London | 1 | | Val | 1 | | Net | 1 | | Shade | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Tony" | | 5 | "Dickerson" | | 6 | "Banksy" | | 7 | "Huh" | | 8 | "Elisa" | | 9 | "Evie" | | 10 | "Morris" | | 11 | "Kris" | | 12 | "Val" | | 13 | "Net" |
| | places | | | globalScore | 0.611 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a waking dream" |
| |
| 91.54% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.085 | | wordCount | 922 | | matches | | 0 | "not the end, but the beginning" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 111 | | matches | | 0 | "Damn that kid," | | 1 | "relived that gift" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 21.95 | | std | 16.44 | | cv | 0.749 | | sampleLengths | | 0 | 65 | | 1 | 44 | | 2 | 32 | | 3 | 18 | | 4 | 14 | | 5 | 32 | | 6 | 60 | | 7 | 20 | | 8 | 6 | | 9 | 23 | | 10 | 49 | | 11 | 35 | | 12 | 1 | | 13 | 23 | | 14 | 27 | | 15 | 13 | | 16 | 54 | | 17 | 31 | | 18 | 11 | | 19 | 6 | | 20 | 24 | | 21 | 13 | | 22 | 17 | | 23 | 37 | | 24 | 19 | | 25 | 22 | | 26 | 6 | | 27 | 5 | | 28 | 33 | | 29 | 7 | | 30 | 7 | | 31 | 3 | | 32 | 4 | | 33 | 49 | | 34 | 4 | | 35 | 8 | | 36 | 16 | | 37 | 33 | | 38 | 25 | | 39 | 7 | | 40 | 8 | | 41 | 11 |
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| 95.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 110 | | matches | | 0 | "been murdered" | | 1 | "been declared" | | 2 | "been ordered" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 165 | | matches | | 0 | "was getting" | | 1 | "wasn't adding" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 111 | | ratio | 0.009 | | matches | | 0 | "It had belonged to Morris, his final gift to Quinn - the one artifact he'd wanted her to send to her." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 128 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.0234375 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 8.3 | | std | 6.45 | | cv | 0.777 | | sampleLengths | | 0 | 24 | | 1 | 11 | | 2 | 8 | | 3 | 22 | | 4 | 14 | | 5 | 1 | | 6 | 9 | | 7 | 15 | | 8 | 5 | | 9 | 5 | | 10 | 8 | | 11 | 7 | | 12 | 6 | | 13 | 6 | | 14 | 9 | | 15 | 9 | | 16 | 14 | | 17 | 11 | | 18 | 6 | | 19 | 9 | | 20 | 2 | | 21 | 4 | | 22 | 9 | | 23 | 6 | | 24 | 10 | | 25 | 35 | | 26 | 26 | | 27 | 7 | | 28 | 5 | | 29 | 11 | | 30 | 12 | | 31 | 15 | | 32 | 3 | | 33 | 8 | | 34 | 11 | | 35 | 2 | | 36 | 7 | | 37 | 6 | | 38 | 4 | | 39 | 16 | | 40 | 1 | | 41 | 5 | | 42 | 6 | | 43 | 12 | | 44 | 2 | | 45 | 25 | | 46 | 3 | | 47 | 3 | | 48 | 7 | | 49 | 14 |
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| 75.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4864864864864865 | | totalSentences | 111 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 96 | | matches | | 0 | "Ever since, It had guided" | | 1 | "Only one way to find" | | 2 | "Then, all of a sudden," |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 96 | | matches | | 0 | "She could already tell this" | | 1 | "She flipped open her notes." | | 2 | "She glanced at the scrawled" | | 3 | "Her partner, her best mate," | | 4 | "We can't ignore the elephant" | | 5 | "His death had been declared" | | 6 | "Her teeth ground together." | | 7 | "She wouldn't sabotage her career" | | 8 | "Her eyes narrowed, jaw set." | | 9 | "You misunderstand, Detective." | | 10 | "We're not alone." | | 11 | "It had belonged to Morris," | | 12 | "She held out a worn" | | 13 | "She slipped on her well-worn" | | 14 | "My father taught me that" | | 15 | "It's what kept me going" | | 16 | "I know, Evie." | | 17 | "I've been there." | | 18 | "Her eyes shone, determination in" | | 19 | "We're no longer puppets." |
| | ratio | 0.208 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 96 | | matches | | 0 | "The clanging of the metal" | | 1 | "The musty air hung heavy," | | 2 | "Eva Kowalski, her partner's cousin," | | 3 | "A flashlight beam danced across" | | 4 | "Quinn's eyes narrowed as she" | | 5 | "She could already tell this" | | 6 | "A chalk outline marked where" | | 7 | "She flipped open her notes." | | 8 | "Wounds consistent with demonic ritual" | | 9 | "Occult symbols scrawled in victim's" | | 10 | "She glanced at the scrawled" | | 11 | "The young uni student handed" | | 12 | "Quinn cursed under her breath," | | 13 | "This was getting way too" | | 14 | "Her partner, her best mate," | | 15 | "Kowalski gave her a quizzical" | | 16 | "Quinn shook her head, returning" | | 17 | "The evidence wasn't adding up." | | 18 | "Rift sprung, hellsprung...there had to" | | 19 | "Silence hung in the air," |
| | ratio | 0.708 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 96 | | matches | | 0 | "If this connects to the" | | 1 | "If anyone, anyone knows what" | | 2 | "If this was powerful enough," |
| | ratio | 0.031 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |