| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 996 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 9.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 996 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "flickered" | | 1 | "scanned" | | 2 | "gloom" | | 3 | "familiar" | | 4 | "shimmered" | | 5 | "silence" | | 6 | "weight" | | 7 | "echoing" | | 8 | "electric" | | 9 | "dancing" | | 10 | "cacophony" | | 11 | "velvet" | | 12 | "intricate" | | 13 | "chill" | | 14 | "navigated" | | 15 | "silk" | | 16 | "echoed" |
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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 | 1 | | narrationSentences | 97 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 996 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 952 | | uniqueNames | 9 | | maxNameDensity | 0.95 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | London | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Quinn | 9 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "weight that seemed to press against her eardrums" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 996 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 19.92 | | std | 16.91 | | cv | 0.849 | | sampleLengths | | 0 | 49 | | 1 | 1 | | 2 | 29 | | 3 | 53 | | 4 | 33 | | 5 | 22 | | 6 | 4 | | 7 | 41 | | 8 | 42 | | 9 | 9 | | 10 | 21 | | 11 | 50 | | 12 | 3 | | 13 | 49 | | 14 | 4 | | 15 | 40 | | 16 | 14 | | 17 | 19 | | 18 | 5 | | 19 | 61 | | 20 | 12 | | 21 | 11 | | 22 | 3 | | 23 | 33 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 12 | | 28 | 14 | | 29 | 1 | | 30 | 9 | | 31 | 6 | | 32 | 35 | | 33 | 27 | | 34 | 1 | | 35 | 8 | | 36 | 39 | | 37 | 41 | | 38 | 6 | | 39 | 34 | | 40 | 3 | | 41 | 19 | | 42 | 16 | | 43 | 7 | | 44 | 43 | | 45 | 14 | | 46 | 2 | | 47 | 24 | | 48 | 3 | | 49 | 8 |
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| 98.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 97 | | matches | | 0 | "was hunched" | | 1 | "were tipped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 955 | | adjectiveStacks | 1 | | stackExamples | | 0 | "primal, soul-deep dread." |
| | adverbCount | 22 | | adverbRatio | 0.023036649214659685 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.010471204188481676 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 9.31 | | std | 5.45 | | cv | 0.585 | | sampleLengths | | 0 | 15 | | 1 | 14 | | 2 | 20 | | 3 | 1 | | 4 | 8 | | 5 | 4 | | 6 | 17 | | 7 | 13 | | 8 | 11 | | 9 | 17 | | 10 | 12 | | 11 | 12 | | 12 | 15 | | 13 | 6 | | 14 | 4 | | 15 | 3 | | 16 | 15 | | 17 | 4 | | 18 | 12 | | 19 | 3 | | 20 | 16 | | 21 | 10 | | 22 | 8 | | 23 | 18 | | 24 | 16 | | 25 | 9 | | 26 | 12 | | 27 | 9 | | 28 | 13 | | 29 | 4 | | 30 | 11 | | 31 | 22 | | 32 | 3 | | 33 | 5 | | 34 | 19 | | 35 | 4 | | 36 | 5 | | 37 | 5 | | 38 | 11 | | 39 | 4 | | 40 | 10 | | 41 | 7 | | 42 | 23 | | 43 | 6 | | 44 | 8 | | 45 | 4 | | 46 | 15 | | 47 | 5 | | 48 | 6 | | 49 | 19 |
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| 32.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.2523364485981308 | | totalSentences | 107 | | uniqueOpeners | 27 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Then he vanished into the" |
| | ratio | 0.011 | |
| 51.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 95 | | matches | | 0 | "He moved with a jagged," | | 1 | "He lunged into a narrow" | | 2 | "She vaulted a low brick" | | 3 | "Her left wrist ached, the" | | 4 | "She scanned the gloom, her" | | 5 | "She didn't hesitate." | | 6 | "She squeezed her frame through" | | 7 | "It smelled of burnt sage," | | 8 | "She descended, her hand hovering" | | 9 | "She reached the bottom of" | | 10 | "It wasn't the empty, echoing" | | 11 | "It was a sprawling, subterranean" | | 12 | "Her military precision, the training" | | 13 | "He stood near a stall" | | 14 | "He was hunched over, his" | | 15 | "He produced a small, white," | | 16 | "He whispered something into the" | | 17 | "She kept her head down," | | 18 | "He was large, his chest" | | 19 | "He held a butcher's knife," |
| | ratio | 0.421 | |
| 7.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 95 | | matches | | 0 | "Quinn’s boots hammered the slick" | | 1 | "He moved with a jagged," | | 2 | "The command left her throat," | | 3 | "The man didn't slow." | | 4 | "He lunged into a narrow" | | 5 | "Quinn pushed her pace, her" | | 6 | "She vaulted a low brick" | | 7 | "Her left wrist ached, the" | | 8 | "She scanned the gloom, her" | | 9 | "The alley ended abruptly in" | | 10 | "A heavy iron gate, rusted" | | 11 | "The suspect had disappeared through" | | 12 | "Quinn reached the gate." | | 13 | "She didn't hesitate." | | 14 | "She squeezed her frame through" | | 15 | "The descent began immediately." | | 16 | "A crumbling concrete staircase spiralled" | | 17 | "The air changed." | | 18 | "The familiar, heavy scent of" | | 19 | "It smelled of burnt sage," |
| | ratio | 0.905 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 95 | | matches | | 0 | "To call for backup." | | 1 | "To wait for a team." | | 2 | "To her left, a woman" | | 3 | "To her right, a man" |
| | ratio | 0.042 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "Each step felt precarious, the stone steps slick with an oily residue that shimmered in her flashlight's beam." | | 1 | "The silence here was unnatural, a heavy, suffocating weight that seemed to press against her eardrums." | | 2 | "A low, melodic hum of voices drifted through the air, a cacophony of languages that felt entirely wrong for a Camden basement." | | 3 | "The shoppers moved with a strange, gliding grace, their clothes a patchwork of velvet, leather, and things that looked uncomfortably like scales." |
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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 | |