| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1436 | | 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) | |
| 86.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1436 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "measured" | | 1 | "warmth" | | 2 | "gloom" | | 3 | "porcelain" |
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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 | 2 | | narrationSentences | 168 | | matches | | 0 | "t with panic" | | 1 | "t with confusion" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 186 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1436 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 35.19% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1350 | | uniqueNames | 12 | | maxNameDensity | 2.3 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 31 | | Shaftesbury | 1 | | Avenue | 1 | | Metropolitan | 1 | | Police | 1 | | London | 3 | | Tube | 1 | | Camden | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Camden" | | 4 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Shaftesbury" | | 2 | "Avenue" | | 3 | "Metropolitan" | | 4 | "London" |
| | globalScore | 0.352 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 1 | | matches | | 0 | "line that seemed to vibrate in her teeth" |
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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 | 1436 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 186 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 109 | | mean | 13.17 | | std | 13.03 | | cv | 0.989 | | sampleLengths | | 0 | 26 | | 1 | 54 | | 2 | 3 | | 3 | 4 | | 4 | 14 | | 5 | 6 | | 6 | 42 | | 7 | 26 | | 8 | 25 | | 9 | 3 | | 10 | 60 | | 11 | 6 | | 12 | 6 | | 13 | 35 | | 14 | 4 | | 15 | 3 | | 16 | 32 | | 17 | 4 | | 18 | 13 | | 19 | 10 | | 20 | 11 | | 21 | 4 | | 22 | 29 | | 23 | 4 | | 24 | 10 | | 25 | 7 | | 26 | 4 | | 27 | 33 | | 28 | 21 | | 29 | 9 | | 30 | 7 | | 31 | 2 | | 32 | 38 | | 33 | 4 | | 34 | 45 | | 35 | 35 | | 36 | 6 | | 37 | 30 | | 38 | 6 | | 39 | 17 | | 40 | 4 | | 41 | 4 | | 42 | 5 | | 43 | 3 | | 44 | 12 | | 45 | 3 | | 46 | 28 | | 47 | 18 | | 48 | 1 | | 49 | 13 |
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| 94.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 168 | | matches | | 0 | "being chased" | | 1 | "been erected" | | 2 | "was silenced" | | 3 | "being told" | | 4 | "was hidden" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 246 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 186 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1354 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.022156573116691284 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0022156573116691287 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 186 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 186 | | mean | 7.72 | | std | 5.35 | | cv | 0.693 | | sampleLengths | | 0 | 26 | | 1 | 31 | | 2 | 13 | | 3 | 10 | | 4 | 3 | | 5 | 4 | | 6 | 14 | | 7 | 6 | | 8 | 5 | | 9 | 6 | | 10 | 11 | | 11 | 13 | | 12 | 4 | | 13 | 3 | | 14 | 24 | | 15 | 2 | | 16 | 15 | | 17 | 10 | | 18 | 3 | | 19 | 15 | | 20 | 21 | | 21 | 15 | | 22 | 6 | | 23 | 3 | | 24 | 6 | | 25 | 6 | | 26 | 6 | | 27 | 18 | | 28 | 11 | | 29 | 4 | | 30 | 3 | | 31 | 17 | | 32 | 4 | | 33 | 2 | | 34 | 9 | | 35 | 4 | | 36 | 11 | | 37 | 2 | | 38 | 9 | | 39 | 1 | | 40 | 6 | | 41 | 5 | | 42 | 4 | | 43 | 16 | | 44 | 5 | | 45 | 8 | | 46 | 4 | | 47 | 10 | | 48 | 7 | | 49 | 4 |
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| 50.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.34594594594594597 | | totalSentences | 185 | | uniqueOpeners | 64 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 150 | | matches | | 0 | "Then a bus roared between" | | 1 | "Somewhere below, the suspect’s shoes" | | 2 | "Just a faded black symbol" | | 3 | "Somewhere in the gloom, something" | | 4 | "Then the transmission had filled" | | 5 | "Then a bell rang." | | 6 | "Then she looked at the" |
| | ratio | 0.047 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 150 | | matches | | 0 | "He wore a charcoal coat" | | 1 | "Her boots struck the flooded" | | 2 | "Her breath came measured despite" | | 3 | "She cleared the bus’s rear" | | 4 | "She crossed the room." | | 5 | "His eyes dropped to the" | | 6 | "His expression did not change." | | 7 | "She drew her pistol." | | 8 | "She descended quickly, one hand" | | 9 | "Her leather watch caught the" | | 10 | "Their tables sagged beneath stoppered" | | 11 | "It searched for costumes, a" | | 12 | "She had seen people manufacture" | | 13 | "She twisted, drove her elbow" | | 14 | "Her suspect was already there," | | 15 | "He reached into his coat." | | 16 | "His hand emerged holding a" | | 17 | "She had his face, his" | | 18 | "She could call for armed" | | 19 | "She could leave, climb the" |
| | ratio | 0.227 | |
| 66.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 118 | | totalSentences | 150 | | matches | | 0 | "The man ahead of her" | | 1 | "He wore a charcoal coat" | | 2 | "The suspect glanced back." | | 3 | "Quinn swore and drove forward." | | 4 | "Her boots struck the flooded" | | 5 | "Her breath came measured despite" | | 6 | "Panic was a luxury." | | 7 | "She cleared the bus’s rear" | | 8 | "The alley stank of wet" | | 9 | "THE RAVEN’S NEST." | | 10 | "The bar’s sign buzzed above" | | 11 | "Quinn had been inside once," | | 12 | "The place had been dim" | | 13 | "The bartender had smiled too" | | 14 | "Everyone had lied." | | 15 | "The suspect shoved through the" | | 16 | "Quinn reached it a heartbeat" | | 17 | "Warmth and whiskey-smoke rolled over" | | 18 | "Music thudded from somewhere behind" | | 19 | "Patrons turned to look at" |
| | ratio | 0.787 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 150 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 3 | | matches | | 0 | "Quinn had been inside once, six months ago, asking questions about a string of disappearances that had never made the papers." | | 1 | "Their tables sagged beneath stoppered bottles, silver knives, bundles of dried herbs, glass jars containing things that moved." | | 2 | "The bricks softened, sagging inward like wet clay, revealing a passage that glowed with amber light." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 34.62% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "she shouted (shout)" | | 2 | "she demanded (demand)" |
| | dialogueSentences | 26 | | tagDensity | 0.269 | | leniency | 0.538 | | rawRatio | 0.429 | | effectiveRatio | 0.231 | |