| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1240 | | 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) | |
| 39.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1240 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "fleeting" | | 1 | "jaw clenched" | | 2 | "flickered" | | 3 | "clandestine" | | 4 | "calculating" | | 5 | "tapestry" | | 6 | "echoed" | | 7 | "maw" | | 8 | "sanctuary" | | 9 | "depths" | | 10 | "gloom" | | 11 | "chaotic" | | 12 | "vibrated" | | 13 | "velvet" | | 14 | "intricate" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | 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 | 1240 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1240 | | uniqueNames | 21 | | maxNameDensity | 1.13 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Harlow | 14 | | Quinn | 2 | | Metropolitan | 2 | | Police | 2 | | Wardour | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Northern | 1 | | Soho | 1 | | Camden | 3 | | Morris | 2 | | Tube | 2 | | Deep | 1 | | Veil | 3 | | Market | 3 | | Tomas | 1 | | Herrera | 1 | | Seville | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Tomas" | | 6 | "Herrera" |
| | places | | 0 | "London" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Northern" | | 4 | "Soho" | | 5 | "Camden" | | 6 | "Deep" | | 7 | "Veil" | | 8 | "Seville" |
| | globalScore | 0.935 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like blood and burnt sugar" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.806 | | wordCount | 1240 | | matches | | 0 | "not the harsh white of fluorescent tubes, but a sickly, shifting violet" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 56.19% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 77.5 | | std | 26.87 | | cv | 0.347 | | sampleLengths | | 0 | 126 | | 1 | 97 | | 2 | 67 | | 3 | 70 | | 4 | 99 | | 5 | 77 | | 6 | 75 | | 7 | 49 | | 8 | 73 | | 9 | 73 | | 10 | 79 | | 11 | 138 | | 12 | 67 | | 13 | 68 | | 14 | 61 | | 15 | 21 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 96 | | matches | | 0 | "was plastered" | | 1 | "was involved" | | 2 | "been sealed" | | 3 | "were traded" | | 4 | "was hidden" | | 5 | "got torn" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 189 | | matches | | 0 | "was playing" | | 1 | "were moving" | | 2 | "was running" | | 3 | "was bleeding" | | 4 | "was stepping" | | 5 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1250 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.0168 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 12.92 | | std | 6.09 | | cv | 0.472 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 18 | | 3 | 10 | | 4 | 16 | | 5 | 10 | | 6 | 4 | | 7 | 18 | | 8 | 18 | | 9 | 9 | | 10 | 11 | | 11 | 6 | | 12 | 19 | | 13 | 31 | | 14 | 9 | | 15 | 7 | | 16 | 5 | | 17 | 7 | | 18 | 15 | | 19 | 4 | | 20 | 10 | | 21 | 17 | | 22 | 8 | | 23 | 6 | | 24 | 10 | | 25 | 11 | | 26 | 10 | | 27 | 16 | | 28 | 10 | | 29 | 8 | | 30 | 5 | | 31 | 15 | | 32 | 13 | | 33 | 10 | | 34 | 8 | | 35 | 5 | | 36 | 22 | | 37 | 11 | | 38 | 15 | | 39 | 13 | | 40 | 13 | | 41 | 14 | | 42 | 19 | | 43 | 18 | | 44 | 11 | | 45 | 11 | | 46 | 18 | | 47 | 28 | | 48 | 7 | | 49 | 16 |
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| 39.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.28125 | | totalSentences | 96 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 96 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She checked the worn leather" | | 2 | "They blasted past The Raven's" | | 3 | "She knew the clique used" | | 4 | "He was fast, but he" | | 5 | "His feet slipped on a" | | 6 | "She could hear his ragged" | | 7 | "She reached for her radio," | | 8 | "She was entirely on her" | | 9 | "They were moving out of" | | 10 | "Her thighs burned, a deep" | | 11 | "It grounded her in reality." | | 12 | "She had seen the shadows" | | 13 | "She had seen the impossible." | | 14 | "She would not lose another" | | 15 | "They were in a derelict" | | 16 | "It smelled like blood and" | | 17 | "She drew her service weapon," | | 18 | "It was not the harsh" | | 19 | "Her investigations into the clique" |
| | ratio | 0.375 | |
| 32.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 96 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn pumped her" | | 2 | "Harlow wiped a sheet of" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "She checked the worn leather" | | 5 | "The job had taught her" | | 6 | "The suspect hooked a sharp" | | 7 | "Harlow followed, her sharp jaw" | | 8 | "They blasted past The Raven's" | | 9 | "The distinctive green neon sign" | | 10 | "She knew the clique used" | | 11 | "Tonight was about the runner." | | 12 | "He was fast, but he" | | 13 | "His feet slipped on a" | | 14 | "Harlow closed the distance." | | 15 | "She could hear his ragged" | | 16 | "She reached for her radio," | | 17 | "The storm was playing hell" | | 18 | "She was entirely on her" | | 19 | "The runner scrambled upright and" |
| | ratio | 0.854 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 96 | | matches | | 0 | "To get in, one needed" | | 1 | "If she went down there," | | 2 | "If she got torn up" |
| | ratio | 0.031 | |
| 89.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 5 | | matches | | 0 | "The air rising from the tunnel was unnaturally warm, carrying the scent of ozone, damp earth, and something coppery that made the hairs on her arms stand up." | | 1 | "It was an underground black market that moved to a new location every full moon, a sanctuary for the unnatural." | | 2 | "Banned alchemical substances, enchanted goods, and secrets that could rot a soul were traded in its depths." | | 3 | "The guardian's face was hidden in the deep cowl, but Harlow could see hands that were too long, with too many joints." | | 4 | "Her military bearing snapped back into place, her spine straightening, her brown eyes hard and focused." |
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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 | |