| 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 | 1379 | | 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) | |
| 74.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1379 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "weight" | | 1 | "scanned" | | 2 | "echoed" | | 3 | "etched" | | 4 | "flickered" | | 5 | "electric" | | 6 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 192 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 192 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 204 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1377 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1276 | | uniqueNames | 21 | | maxNameDensity | 1.33 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Old | 1 | | Compton | 1 | | Street | 2 | | Raven | 2 | | Nest | 3 | | Dean | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Underground | 2 | | Leicester | 1 | | Square | 1 | | Herrera | 1 | | Tomás | 8 | | Tube | 2 | | Inspector | 1 | | Morris | 3 | | Camden | 1 | | Veil | 1 | | Market | 3 | | Quinn | 17 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Square" | | 3 | "Herrera" | | 4 | "Tomás" | | 5 | "Tube" | | 6 | "Inspector" | | 7 | "Morris" | | 8 | "Market" | | 9 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Dean" | | 5 | "Shaftesbury" | | 6 | "Avenue" | | 7 | "Underground" | | 8 | "Leicester" |
| | globalScore | 0.834 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1377 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 204 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 20.25 | | std | 14 | | cv | 0.691 | | sampleLengths | | 0 | 5 | | 1 | 41 | | 2 | 66 | | 3 | 28 | | 4 | 5 | | 5 | 14 | | 6 | 2 | | 7 | 2 | | 8 | 27 | | 9 | 26 | | 10 | 15 | | 11 | 4 | | 12 | 14 | | 13 | 21 | | 14 | 19 | | 15 | 40 | | 16 | 11 | | 17 | 13 | | 18 | 4 | | 19 | 23 | | 20 | 30 | | 21 | 18 | | 22 | 13 | | 23 | 25 | | 24 | 35 | | 25 | 27 | | 26 | 21 | | 27 | 7 | | 28 | 26 | | 29 | 25 | | 30 | 44 | | 31 | 10 | | 32 | 13 | | 33 | 5 | | 34 | 9 | | 35 | 7 | | 36 | 15 | | 37 | 23 | | 38 | 53 | | 39 | 21 | | 40 | 3 | | 41 | 24 | | 42 | 31 | | 43 | 17 | | 44 | 38 | | 45 | 9 | | 46 | 12 | | 47 | 23 | | 48 | 5 | | 49 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 192 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 244 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 204 | | ratio | 0.015 | | matches | | 0 | "Except—" | | 1 | "Light flickered down there; not electric." | | 2 | "People—some people—haggled." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 718 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.019498607242339833 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002785515320334262 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 204 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 204 | | mean | 6.75 | | std | 4.5 | | cv | 0.666 | | sampleLengths | | 0 | 5 | | 1 | 8 | | 2 | 4 | | 3 | 17 | | 4 | 12 | | 5 | 8 | | 6 | 7 | | 7 | 15 | | 8 | 15 | | 9 | 13 | | 10 | 8 | | 11 | 9 | | 12 | 2 | | 13 | 5 | | 14 | 12 | | 15 | 5 | | 16 | 7 | | 17 | 4 | | 18 | 3 | | 19 | 2 | | 20 | 2 | | 21 | 4 | | 22 | 3 | | 23 | 7 | | 24 | 13 | | 25 | 11 | | 26 | 7 | | 27 | 3 | | 28 | 5 | | 29 | 3 | | 30 | 3 | | 31 | 9 | | 32 | 4 | | 33 | 10 | | 34 | 2 | | 35 | 2 | | 36 | 3 | | 37 | 11 | | 38 | 2 | | 39 | 5 | | 40 | 2 | | 41 | 7 | | 42 | 6 | | 43 | 4 | | 44 | 5 | | 45 | 4 | | 46 | 17 | | 47 | 2 | | 48 | 5 | | 49 | 7 |
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| 61.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4117647058823529 | | totalSentences | 204 | | uniqueOpeners | 84 | |
| 41.41% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 161 | | matches | | 0 | "Too warm a coat for" | | 1 | "Then she saw movement down" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 161 | | matches | | 0 | "It turned Old Compton Street" | | 1 | "Her coat collar stuck to" | | 2 | "She stood with her weight" | | 3 | "She closed ten feet before" | | 4 | "His face jumped." | | 5 | "Her boots slapped water." | | 6 | "She cut around its bumper," | | 7 | "He knocked a stack of" | | 8 | "Her knees drove." | | 9 | "Her breath sawed in and" | | 10 | "Her voice cut through the" | | 11 | "He didn't stop." | | 12 | "He veered left into a" | | 13 | "Her shoulder brushed brick and" | | 14 | "He vaulted a chained bike." | | 15 | "She hissed and pushed up." | | 16 | "She fell into her companion." | | 17 | "He cut toward the Underground" | | 18 | "Her calf clipped the barrier" | | 19 | "He was not on the" |
| | ratio | 0.286 | |
| 40.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 161 | | matches | | 0 | "It turned Old Compton Street" | | 1 | "Neon bled across it." | | 2 | "The green sign of The" | | 3 | "Quinn caught it in her" | | 4 | "Her coat collar stuck to" | | 5 | "Water ran off her cropped" | | 6 | "She stood with her weight" | | 7 | "The worn leather strap of" | | 8 | "The figure from the bar" | | 9 | "Hands jammed in a windbreaker." | | 10 | "Quinn pushed off the wall." | | 11 | "She closed ten feet before" | | 12 | "Brown eyes met brown." | | 13 | "His face jumped." | | 14 | "Her boots slapped water." | | 15 | "The street tilted." | | 16 | "A taxi blasted its horn" | | 17 | "She cut around its bumper," | | 18 | "The suspect hit the mouth" | | 19 | "He knocked a stack of" |
| | ratio | 0.839 | |
| 93.17% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 161 | | matches | | 0 | "If she turned now, Tomás" | | 1 | "If she went down, she" | | 2 | "To steady her." |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 2 | | matches | | 0 | "Rough-hewn, wet, leading down into a vaulted space that hummed." | | 1 | "The murmur of a crowd, barter, argument, laughter that did not sound human." |
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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 | |