| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1190 | | totalAiIsmAdverbs | 2 | | 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) | |
| 36.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1190 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "fractured" | | 1 | "measured" | | 2 | "tracing" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "flickered" | | 6 | "pulse" | | 7 | "quickened" | | 8 | "gleaming" | | 9 | "scanned" | | 10 | "velvet" | | 11 | "flicked" | | 12 | "standard" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1190 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 1 | | matches | | 0 | "You are wearing a badge, he said, his accent thick with Seville." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1190 | | uniqueNames | 14 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Soho | 1 | | Quinn | 10 | | Metropolitan | 2 | | Police | 2 | | Morris | 1 | | London | 2 | | Transport | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Detective | 2 | | Veil | 1 | | Market | 1 | | You | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "You" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Seville" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like something older, something th" |
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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 | 1190 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 11 | | mean | 108.18 | | std | 68.58 | | cv | 0.634 | | sampleLengths | | 0 | 271 | | 1 | 146 | | 2 | 162 | | 3 | 126 | | 4 | 9 | | 5 | 30 | | 6 | 75 | | 7 | 65 | | 8 | 115 | | 9 | 70 | | 10 | 121 |
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| 98.58% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 105 | | matches | | 0 | "was plastered" | | 1 | "was scratched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 204 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1197 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short, curly dark hair" |
| | adverbCount | 26 | | adverbRatio | 0.02172096908939014 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.00835421888053467 | |
| 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 | 11.33 | | std | 7.65 | | cv | 0.675 | | sampleLengths | | 0 | 25 | | 1 | 7 | | 2 | 23 | | 3 | 18 | | 4 | 18 | | 5 | 29 | | 6 | 24 | | 7 | 22 | | 8 | 12 | | 9 | 16 | | 10 | 13 | | 11 | 27 | | 12 | 5 | | 13 | 32 | | 14 | 4 | | 15 | 5 | | 16 | 8 | | 17 | 11 | | 18 | 7 | | 19 | 3 | | 20 | 27 | | 21 | 16 | | 22 | 1 | | 23 | 2 | | 24 | 7 | | 25 | 9 | | 26 | 14 | | 27 | 11 | | 28 | 4 | | 29 | 2 | | 30 | 10 | | 31 | 5 | | 32 | 31 | | 33 | 9 | | 34 | 13 | | 35 | 16 | | 36 | 17 | | 37 | 6 | | 38 | 7 | | 39 | 8 | | 40 | 17 | | 41 | 6 | | 42 | 13 | | 43 | 13 | | 44 | 6 | | 45 | 17 | | 46 | 9 | | 47 | 28 | | 48 | 23 | | 49 | 7 |
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| 51.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.4 | | totalSentences | 105 | | uniqueOpeners | 42 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 96 | | matches | | 0 | "Instead, he raised a hand," | | 1 | "Somewhere in the distance, a" |
| | ratio | 0.021 | |
| 61.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 96 | | matches | | 0 | "Her boots struck the wet" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "He had the telltale limp" | | 3 | "It had kept time since" | | 4 | "She did not look down." | | 5 | "She kept her eyes locked" | | 6 | "She followed, counting them as" | | 7 | "She did not draw." | | 8 | "She pushed the door open." | | 9 | "It was a black market," | | 10 | "She adjusted her collar, kept" | | 11 | "He wore a faded jacket" | | 12 | "His short, curly dark hair" | | 13 | "He did not reach for" | | 14 | "His warm brown eyes scanned" | | 15 | "You are wearing a badge," | | 16 | "I am following a suspect." | | 17 | "You are following a thread," | | 18 | "He nodded toward the far" | | 19 | "He went past the token" |
| | ratio | 0.396 | |
| 27.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 96 | | matches | | 0 | "Rain fell in relentless sheets," | | 1 | "Harlow Quinn did not slow" | | 2 | "Water soaked through the wool" | | 3 | "Her boots struck the wet" | | 4 | "Her closely cropped salt-and-pepper hair" | | 5 | "The suspect moved fast, a" | | 6 | "He had the telltale limp" | | 7 | "Quinn’s left hand rested near" | | 8 | "The face was scratched, the" | | 9 | "It had kept time since" | | 10 | "She did not look down." | | 11 | "She kept her eyes locked" | | 12 | "Doubt was a liability." | | 13 | "Hesitation was how partners died." | | 14 | "Quinn stepped into the alley" | | 15 | "The suspect’s footsteps echoed down" | | 16 | "She followed, counting them as" | | 17 | "The street sounds of London" | | 18 | "The air grew heavier, thick" | | 19 | "The faint, coppery scent of" |
| | ratio | 0.865 | |
| 52.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 96 | | matches | | | ratio | 0.01 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 8 | | matches | | 0 | "Rain fell in relentless sheets, turning the Soho pavement into a slick black mirror that fractured the neon bleed of closed pubs and flickering streetlamps." | | 1 | "The suspect moved fast, a wiry frame in a dark coat that slipped between parked cars and spilled out of alley mouths." | | 2 | "It had kept time since Morris took his last breath three years ago, since the unexplained circumstances had closed out a file that refused to stay buried." | | 3 | "The street sounds of London faded, replaced by the distant drip of water and the low, resonant hum of ventilation fans that had not spun in decades." | | 4 | "The space beyond was an abandoned tube platform, its vaulted ceiling arched with crumbling brick, its tracks overgrown with pale, bioluminescent fungi that cast…" | | 5 | "He wore a faded jacket that had once belonged to the NHS, the fabric frayed at the cuffs, and a silver Saint Christopher medallion rested against his collarbone…" | | 6 | "His short, curly dark hair was damp, his left forearm marked by a thick, white scar that spoke of a blade moving fast." | | 7 | "The lanterns above flickered, casting long shadows that stretched like grasping fingers." |
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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 | |