| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said softly [softly]" |
| | dialogueSentences | 58 | | tagDensity | 0.31 | | leniency | 0.621 | | rawRatio | 0.056 | | effectiveRatio | 0.034 | |
| 89.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1385 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "tightly" | | 1 | "softly" | | 2 | "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) | |
| 49.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1385 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "echoing" | | 2 | "chaotic" | | 3 | "silk" | | 4 | "stark" | | 5 | "scanned" | | 6 | "chill" | | 7 | "etched" | | 8 | "intricate" | | 9 | "vibrated" | | 10 | "warmth" | | 11 | "wavering" | | 12 | "silence" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1374 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 24.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 873 | | uniqueNames | 6 | | maxNameDensity | 2.52 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Harlow | 22 | | Quinn | 1 | | Hayes | 10 | | Kowalski | 1 | | Eva | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Hayes" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.24 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to shift in the light" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.728 | | wordCount | 1374 | | matches | | 0 | "not from the outside, but from the inside out" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 20.82 | | std | 16.18 | | cv | 0.777 | | sampleLengths | | 0 | 76 | | 1 | 29 | | 2 | 25 | | 3 | 3 | | 4 | 33 | | 5 | 1 | | 6 | 12 | | 7 | 7 | | 8 | 30 | | 9 | 52 | | 10 | 1 | | 11 | 38 | | 12 | 39 | | 13 | 6 | | 14 | 8 | | 15 | 3 | | 16 | 78 | | 17 | 1 | | 18 | 5 | | 19 | 39 | | 20 | 2 | | 21 | 10 | | 22 | 19 | | 23 | 5 | | 24 | 32 | | 25 | 14 | | 26 | 9 | | 27 | 19 | | 28 | 23 | | 29 | 16 | | 30 | 15 | | 31 | 3 | | 32 | 14 | | 33 | 15 | | 34 | 27 | | 35 | 38 | | 36 | 49 | | 37 | 17 | | 38 | 37 | | 39 | 19 | | 40 | 15 | | 41 | 29 | | 42 | 14 | | 43 | 5 | | 44 | 5 | | 45 | 22 | | 46 | 17 | | 47 | 16 | | 48 | 35 | | 49 | 26 |
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| 83.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 98 | | matches | | 0 | "was torn" | | 1 | "was jagged" | | 2 | "been ripped" | | 3 | "was shredded" | | 4 | "were curled" | | 5 | "was unblemished" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 153 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 878 | | adjectiveStacks | 1 | | stackExamples | | 0 | "harsh, clinical white, pushing" |
| | adverbCount | 22 | | adverbRatio | 0.025056947608200455 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009111617312072893 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 9.96 | | std | 6.17 | | cv | 0.62 | | sampleLengths | | 0 | 21 | | 1 | 14 | | 2 | 16 | | 3 | 25 | | 4 | 20 | | 5 | 9 | | 6 | 20 | | 7 | 5 | | 8 | 3 | | 9 | 10 | | 10 | 12 | | 11 | 11 | | 12 | 1 | | 13 | 12 | | 14 | 7 | | 15 | 30 | | 16 | 2 | | 17 | 9 | | 18 | 6 | | 19 | 6 | | 20 | 14 | | 21 | 15 | | 22 | 1 | | 23 | 8 | | 24 | 30 | | 25 | 6 | | 26 | 5 | | 27 | 22 | | 28 | 6 | | 29 | 6 | | 30 | 8 | | 31 | 3 | | 32 | 13 | | 33 | 6 | | 34 | 11 | | 35 | 11 | | 36 | 8 | | 37 | 15 | | 38 | 14 | | 39 | 1 | | 40 | 5 | | 41 | 11 | | 42 | 17 | | 43 | 11 | | 44 | 2 | | 45 | 10 | | 46 | 19 | | 47 | 5 | | 48 | 25 | | 49 | 7 |
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| 50.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.34057971014492755 | | totalSentences | 138 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 88 | | matches | | 0 | "She clicked the stopwatch on" | | 1 | "He straightened as Harlow approached." | | 2 | "She didn't touch the body" | | 3 | "She looked at the surrounding" | | 4 | "It wasn't a clean stab." | | 5 | "She lifted the dead man's" | | 6 | "It was stiff, rigor mortis" | | 7 | "She turned her attention to" | | 8 | "She spoke without looking up." | | 9 | "She looked pale, her freckles" | | 10 | "She peered at the tear" | | 11 | "She leaned in closer, hovering" | | 12 | "She scanned the floor again," | | 13 | "She walked toward the pile" | | 14 | "She picked it up." | | 15 | "It was heavy, warm to" | | 16 | "It spun erratically, twitching like" | | 17 | "It pointed directly at the" | | 18 | "She held the compass steady." | | 19 | "She dropped the token into" |
| | ratio | 0.295 | |
| 16.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 88 | | matches | | 0 | "Water dripped from the arched" | | 1 | "The air smelled of ozone," | | 2 | "Detective Harlow Quinn ducked under" | | 3 | "She clicked the stopwatch on" | | 4 | "DC Hayes stood by the" | | 5 | "He straightened as Harlow approached." | | 6 | "Harlow ignored the pleasantry and" | | 7 | "The fabric was torn at" | | 8 | "Hayes flipped a page on" | | 9 | "The knees of her trousers" | | 10 | "She didn't touch the body" | | 11 | "She looked at the surrounding" | | 12 | "The platform was cluttered with" | | 13 | "Dust lay thick on the" | | 14 | "Hayes pointed with his pen" | | 15 | "Harlow leaned closer to the" | | 16 | "It wasn't a clean stab." | | 17 | "The skin was jagged, as" | | 18 | "Harlow reached into her pocket" | | 19 | "She lifted the dead man's" |
| | ratio | 0.886 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "Male, mid-thirties, dressed in a bespoke suit that had seen better days." | | 1 | "Green corrosion bloomed across the casing, but the face was clean, etched with intricate symbols that seemed to shift in the light." | | 2 | "The needle was now spinning counter-clockwise, a slow, lazy rotation that made the hairs on her arms stand up." | | 3 | "The warmth seeped into her skin, pulsing in time with a faint, low-frequency hum coming from the tunnel wall." |
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| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 4 | | matches | | 0 | "Eva tucked, her eyes darting around the platform" | | 1 | "Eva said, her breath hitching" | | 2 | "Harlow said, her voice hard" | | 3 | "Hayes asked, his voice wavering slightly" |
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| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 4 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "Eva corrected (correct)" | | 2 | "Eva warned (warn)" | | 3 | "Hayes suggested (suggest)" |
| | dialogueSentences | 58 | | tagDensity | 0.224 | | leniency | 0.448 | | rawRatio | 0.308 | | effectiveRatio | 0.138 | |