| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn mused more [more]" |
| | dialogueSentences | 23 | | tagDensity | 0.565 | | leniency | 1 | | rawRatio | 0.077 | | effectiveRatio | 0.077 | |
| 96.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | 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) | |
| 40.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "echoes" | | 1 | "dancing" | | 2 | "perfect" | | 3 | "tracing" | | 4 | "glint" | | 5 | "etched" | | 6 | "intricate" | | 7 | "quivered" | | 8 | "aftermath" |
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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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1262 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 958 | | uniqueNames | 11 | | maxNameDensity | 1.77 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Quinn | 17 | | London | 1 | | Veil | 1 | | Market | 3 | | Eva | 12 | | Kowalski | 1 | | Morris | 2 | | PhD | 1 | | Detective | 1 | | Harlow | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Morris" | | 5 | "PhD" | | 6 | "Harlow" |
| | places | | | globalScore | 0.613 | | windowScore | 0.5 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "quite place" | | 1 | "as if expecting the Market to return any second" |
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| 41.52% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.585 | | wordCount | 1262 | | matches | | 0 | "not to the man, but to a void beside him" | | 1 | "not just the man, but any botched ritual" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 100 | | matches | | 0 | "needing that understanding" | | 1 | "crafted that …" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 38.24 | | std | 22.77 | | cv | 0.595 | | sampleLengths | | 0 | 72 | | 1 | 89 | | 2 | 41 | | 3 | 63 | | 4 | 40 | | 5 | 7 | | 6 | 27 | | 7 | 46 | | 8 | 39 | | 9 | 49 | | 10 | 18 | | 11 | 37 | | 12 | 40 | | 13 | 37 | | 14 | 16 | | 15 | 8 | | 16 | 37 | | 17 | 41 | | 18 | 9 | | 19 | 57 | | 20 | 34 | | 21 | 46 | | 22 | 12 | | 23 | 36 | | 24 | 46 | | 25 | 38 | | 26 | 11 | | 27 | 14 | | 28 | 8 | | 29 | 93 | | 30 | 80 | | 31 | 53 | | 32 | 18 |
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| 89.67% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 90 | | matches | | 0 | "was unbuttoned" | | 1 | "was curled" | | 2 | "was splayed" | | 3 | "was smudged" | | 4 | "being exploded" |
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| 26.84% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 154 | | matches | | 0 | "wasn’t pointing" | | 1 | "was pointing" | | 2 | "was listening" | | 3 | "was holding" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 100 | | ratio | 0.01 | | matches | | 0 | "“The burn pattern is too clean. It’s not an explosion; it’s an application. Someone used a hell of a lot of power, but they directed it *at* him. They burned him out from the outside in and crafted this perfect circle around him to make it look self-inflicted.” She held up the compass." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 955 | | adjectiveStacks | 1 | | stackExamples | | 0 | "perfect, self-contained accident." |
| | adverbCount | 27 | | adverbRatio | 0.028272251308900525 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0062827225130890054 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 12.62 | | std | 10.88 | | cv | 0.862 | | sampleLengths | | 0 | 24 | | 1 | 30 | | 2 | 18 | | 3 | 11 | | 4 | 28 | | 5 | 11 | | 6 | 22 | | 7 | 13 | | 8 | 4 | | 9 | 10 | | 10 | 18 | | 11 | 10 | | 12 | 3 | | 13 | 12 | | 14 | 11 | | 15 | 17 | | 16 | 14 | | 17 | 9 | | 18 | 20 | | 19 | 3 | | 20 | 17 | | 21 | 5 | | 22 | 2 | | 23 | 6 | | 24 | 21 | | 25 | 14 | | 26 | 7 | | 27 | 18 | | 28 | 3 | | 29 | 4 | | 30 | 39 | | 31 | 17 | | 32 | 5 | | 33 | 24 | | 34 | 3 | | 35 | 18 | | 36 | 18 | | 37 | 19 | | 38 | 10 | | 39 | 5 | | 40 | 2 | | 41 | 10 | | 42 | 8 | | 43 | 5 | | 44 | 18 | | 45 | 2 | | 46 | 13 | | 47 | 4 | | 48 | 10 | | 49 | 6 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.33 | | totalSentences | 100 | | uniqueOpeners | 33 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Away from the epicenter." | | 1 | "Just scorched floor." |
| | ratio | 0.023 | |
| 91.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 87 | | matches | | 0 | "His wallet was still in" | | 1 | "Her round glasses reflected the" | | 2 | "She kept her gaze on" | | 3 | "She’d pulled Eva into consulting" | | 4 | "She examined the floor around" | | 5 | "It looked textbook." | | 6 | "She focused on the details," | | 7 | "His left hand was curled" | | 8 | "Her gaze drifted from the" | | 9 | "She stood and walked toward" | | 10 | "She crouched again, fishing a" | | 11 | "She picked it up." | | 12 | "It was a compass." | | 13 | "It was too deliberate, too" | | 14 | "She held it flat in" | | 15 | "It quivered, spinning in a" | | 16 | "It wasn’t pointing at the" | | 17 | "It was pointing at a" | | 18 | "She walked slowly back to" | | 19 | "She stopped where it indicated." |
| | ratio | 0.322 | |
| 34.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 87 | | matches | | 0 | "The air in the abandoned" | | 1 | "The Veil Market had moved" | | 2 | "Quinn stepped under the tape," | | 3 | "The single overhead work lamp" | | 4 | "A man, late thirties, dressed" | | 5 | "His wallet was still in" | | 6 | "This wasn’t a robbery." | | 7 | "The voice came from the" | | 8 | "Eva Kowalski stood there, her" | | 9 | "Her round glasses reflected the" | | 10 | "Quinn said, her voice flat" | | 11 | "She kept her gaze on" | | 12 | "She’d pulled Eva into consulting" | | 13 | "The girl from the museum’s" | | 14 | "Eva said, tucking a curl" | | 15 | "A nervous habit." | | 16 | "Quinn finally looked at her." | | 17 | "Eva gestured vaguely at the" | | 18 | "Quinn knelt, ignoring the protest" | | 19 | "She examined the floor around" |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 73.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 4 | | matches | | 0 | "The single overhead work lamp the techs had set up cast a harsh, unforgiving light, creating long, dancing shadows that made the cavernous space feel even more …" | | 1 | "His left hand was curled into a loose fist, but his right was splayed open, palm up, as if he’d been reaching for something." | | 2 | "Small, the casing etched with intricate, swirling designs that looked less like navigation aids and more like protective sigils." | | 3 | "Quinn closed her fingers around the cold brass casing, the etched sigils pressing into her palm. She looked from the manufactured scene of the crime back into t…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "Eva said, her voice gaining a note of academic curiosity" | | 2 | "Eva insisted, her nervous tic returning" | | 3 | "Quinn said, her voice low and firm" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0.167 | | effectiveRatio | 0.087 | |