| 9.52% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 4 | | adverbTags | | 0 | "Eva said quietly [quietly]" | | 1 | "Eva corrected harshly [harshly]" | | 2 | "Harlow lied smoothly [smoothly]" | | 3 | "Eva said softly [softly]" |
| | dialogueSentences | 42 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.19 | | effectiveRatio | 0.19 | |
| 78.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1366 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "quickly" | | 1 | "loosely" | | 2 | "carefully" | | 3 | "slightly" | | 4 | "softly" | | 5 | "slowly" |
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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) | |
| 41.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1366 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "flickered" | | 1 | "rhythmic" | | 2 | "standard" | | 3 | "scanning" | | 4 | "could feel" | | 5 | "chill" | | 6 | "weight" | | 7 | "familiar" | | 8 | "magnetic" | | 9 | "ensuring" | | 10 | "processed" | | 11 | "tangible" | | 12 | "echo" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
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| | highlights | | 0 | "the weight of the truth" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 84 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1365 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 885 | | uniqueNames | 12 | | maxNameDensity | 2.15 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 19 | | Quinn | 1 | | Kowalski | 1 | | Morris | 1 | | Eva | 15 | | Veil | 2 | | Compass | 1 | | Market | 1 | | Camden | 1 | | Milky | 1 | | Way | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Compass" | | 6 | "Market" |
| | places | | | globalScore | 0.427 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.733 | | wordCount | 1365 | | matches | | 0 | "not her iron, but the weight of the truth" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 40.15 | | std | 25.88 | | cv | 0.645 | | sampleLengths | | 0 | 96 | | 1 | 67 | | 2 | 59 | | 3 | 57 | | 4 | 28 | | 5 | 56 | | 6 | 81 | | 7 | 7 | | 8 | 19 | | 9 | 62 | | 10 | 15 | | 11 | 32 | | 12 | 27 | | 13 | 73 | | 14 | 28 | | 15 | 4 | | 16 | 58 | | 17 | 34 | | 18 | 41 | | 19 | 79 | | 20 | 4 | | 21 | 35 | | 22 | 93 | | 23 | 17 | | 24 | 35 | | 25 | 15 | | 26 | 27 | | 27 | 5 | | 28 | 66 | | 29 | 45 | | 30 | 20 | | 31 | 40 | | 32 | 8 | | 33 | 32 |
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| 96.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 84 | | matches | | 0 | "was carved" | | 1 | "was found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 105 | | ratio | 0.01 | | matches | | 0 | "The surge of data filed in her brain—the numbers on the watch, the geometry of the grit beneath her soles, the smell of ozone replacing the smell of copper." |
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| 99.79% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 889 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.031496062992125984 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.020247469066366704 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 13 | | std | 8.22 | | cv | 0.633 | | sampleLengths | | 0 | 19 | | 1 | 19 | | 2 | 15 | | 3 | 20 | | 4 | 23 | | 5 | 14 | | 6 | 5 | | 7 | 18 | | 8 | 5 | | 9 | 8 | | 10 | 9 | | 11 | 8 | | 12 | 14 | | 13 | 17 | | 14 | 17 | | 15 | 11 | | 16 | 25 | | 17 | 22 | | 18 | 10 | | 19 | 16 | | 20 | 6 | | 21 | 6 | | 22 | 13 | | 23 | 14 | | 24 | 29 | | 25 | 5 | | 26 | 20 | | 27 | 5 | | 28 | 5 | | 29 | 29 | | 30 | 7 | | 31 | 3 | | 32 | 1 | | 33 | 1 | | 34 | 5 | | 35 | 5 | | 36 | 2 | | 37 | 6 | | 38 | 13 | | 39 | 6 | | 40 | 18 | | 41 | 13 | | 42 | 10 | | 43 | 5 | | 44 | 10 | | 45 | 15 | | 46 | 6 | | 47 | 7 | | 48 | 8 | | 49 | 11 |
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| 56.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.37142857142857144 | | totalSentences | 105 | | uniqueOpeners | 39 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | 0 | "Just a puddle of crimson" |
| | ratio | 0.013 | |
| 62.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 76 | | matches | | 0 | "She adjusted the collar of" | | 1 | "It was a standard protocol" | | 2 | "She glanced at the victim." | | 3 | "She held a worn leather" | | 4 | "She tucked a strand of" | | 5 | "Her movement was predatory but" | | 6 | "She reached into her satchel" | | 7 | "She could feel the cold" | | 8 | "Her partner, DS Morris, had" | | 9 | "She took out her most" | | 10 | "It was marked with protective" | | 11 | "She removed it carefully, the" | | 12 | "It was a Veil Compass." | | 13 | "She held it out over" | | 14 | "It twitched, jerking violently toward" | | 15 | "She read the report in" | | 16 | "Her bearing, usually military precision," | | 17 | "She looked at the satchel," | | 18 | "She had seen the geometry" | | 19 | "She had seen the shadow" |
| | ratio | 0.395 | |
| 25.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 76 | | matches | | 0 | "The fluorescent bulb above the" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "She adjusted the collar of" | | 3 | "The leather band of her" | | 4 | "A body lay near the" | | 5 | "Harlow didn’t approach too quickly." | | 6 | "It was a standard protocol" | | 7 | "She glanced at the victim." | | 8 | "Eva Kowalski stood a few" | | 9 | "The young woman’s curly red" | | 10 | "She held a worn leather" | | 11 | "Eva looked uncomfortable, though every" | | 12 | "Eva said quietly, her voice" | | 13 | "She tucked a strand of" | | 14 | "Harlow said, her eyes scanning" | | 15 | "Her movement was predatory but" | | 16 | "Eva countered, stepping closer though" | | 17 | "She reached into her satchel" | | 18 | "Harlow leaned over the body." | | 19 | "She could feel the cold" |
| | ratio | 0.868 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 69.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 4 | | matches | | 0 | "The leather band of her wristwatch clicked silently on her left wrist, a rhythmic ticking that matched the slow depression of her head." | | 1 | "She could feel the cold seeping through her coat, a lethargic chill that had nothing to do with the temperature." | | 2 | "She had seen the shadow cast by the pillar at an angle that defected three-dimensional physics." | | 3 | "Eva hesitated, then shoved the satchel into her hand, ducking under the tape with the grace of a student hiding a reading log." |
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| 29.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 4 | | matches | | 0 | "Harlow said, her eyes scanning the body again" | | 1 | "Eva asked, her green eyes narrowing behind the wire rims" | | 2 | "Harlow snapped, though her voice lacked the fire" | | 3 | "Harlow said, her voice steady" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 8 | | fancyTags | | 0 | "Harlow murmured (murmur)" | | 1 | "Eva corrected harshly (correct)" | | 2 | "Harlow lied smoothly (lie)" | | 3 | "Harlow snapped (snap)" | | 4 | "Eva pleaded (plead)" | | 5 | "She pressed (press)" | | 6 | "Harlow agreed (agree)" | | 7 | "Eva whispered (whisper)" |
| | dialogueSentences | 42 | | tagDensity | 0.452 | | leniency | 0.905 | | rawRatio | 0.421 | | effectiveRatio | 0.381 | |