| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1344 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "suddenly" | | 1 | "precisely" | | 2 | "gently" |
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| 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) | |
| 85.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1344 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "warmth" | | 1 | "footsteps" | | 2 | "silk" | | 3 | "velvet" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1344 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1239 | | uniqueNames | 18 | | maxNameDensity | 0.73 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | Quinn | 9 | | Kentish | 1 | | Town | 1 | | Road | 1 | | Hawley | 1 | | Crescent | 1 | | Soho | 1 | | Herrera | 5 | | Camden | 2 | | Morris | 4 | | Mare | 2 | | Street | 2 | | Spanish | 1 | | Slow | 1 | | London | 2 | | Underground | 1 | | Tomás | 2 | | You | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Camden" | | 3 | "Morris" | | 4 | "Spanish" | | 5 | "Tomás" | | 6 | "You" |
| | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "Road" | | 3 | "Hawley" | | 4 | "Crescent" | | 5 | "Soho" | | 6 | "Mare" | | 7 | "Street" | | 8 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1344 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 113 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 28.6 | | std | 26.24 | | cv | 0.918 | | sampleLengths | | 0 | 36 | | 1 | 73 | | 2 | 34 | | 3 | 22 | | 4 | 3 | | 5 | 61 | | 6 | 33 | | 7 | 10 | | 8 | 76 | | 9 | 42 | | 10 | 11 | | 11 | 6 | | 12 | 69 | | 13 | 8 | | 14 | 26 | | 15 | 4 | | 16 | 14 | | 17 | 30 | | 18 | 44 | | 19 | 4 | | 20 | 1 | | 21 | 35 | | 22 | 62 | | 23 | 1 | | 24 | 10 | | 25 | 21 | | 26 | 53 | | 27 | 74 | | 28 | 12 | | 29 | 43 | | 30 | 18 | | 31 | 4 | | 32 | 18 | | 33 | 5 | | 34 | 15 | | 35 | 59 | | 36 | 4 | | 37 | 17 | | 38 | 122 | | 39 | 16 | | 40 | 34 | | 41 | 5 | | 42 | 3 | | 43 | 58 | | 44 | 30 | | 45 | 10 | | 46 | 8 |
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| 88.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 102 | | matches | | 0 | "is cornered" | | 1 | "gets worn" | | 2 | "been pulled" | | 3 | "being paid" | | 4 | "was, laid" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 206 | | matches | | 0 | "was running" | | 1 | "were already moving" | | 2 | "was playing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 113 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1245 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same burnt-hair smell," |
| | adverbCount | 28 | | adverbRatio | 0.02248995983935743 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004016064257028112 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 11.89 | | std | 10.22 | | cv | 0.86 | | sampleLengths | | 0 | 2 | | 1 | 34 | | 2 | 6 | | 3 | 33 | | 4 | 6 | | 5 | 8 | | 6 | 20 | | 7 | 6 | | 8 | 11 | | 9 | 17 | | 10 | 12 | | 11 | 10 | | 12 | 3 | | 13 | 35 | | 14 | 6 | | 15 | 17 | | 16 | 3 | | 17 | 1 | | 18 | 21 | | 19 | 11 | | 20 | 6 | | 21 | 4 | | 22 | 13 | | 23 | 17 | | 24 | 14 | | 25 | 5 | | 26 | 27 | | 27 | 6 | | 28 | 36 | | 29 | 4 | | 30 | 3 | | 31 | 4 | | 32 | 6 | | 33 | 22 | | 34 | 4 | | 35 | 3 | | 36 | 15 | | 37 | 25 | | 38 | 8 | | 39 | 20 | | 40 | 6 | | 41 | 4 | | 42 | 9 | | 43 | 5 | | 44 | 6 | | 45 | 1 | | 46 | 2 | | 47 | 21 | | 48 | 11 | | 49 | 21 |
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| 79.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.5309734513274337 | | totalSentences | 113 | | uniqueOpeners | 60 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 88 | | matches | | 0 | "Of course he did." | | 1 | "Somewhere a security radio muttered." | | 2 | "Then the light changed." | | 3 | "Then the nearest conversations went" |
| | ratio | 0.045 | |
| 97.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 88 | | matches | | 0 | "It stripped the warmth out" | | 1 | "Her radio bumped against her" | | 2 | "She'd stopped calling it in" | | 3 | "He wasn't running like a" | | 4 | "Her voice cracked off the" | | 5 | "He kept running." | | 6 | "She had the name from" | | 7 | "He cut left into the" | | 8 | "She shut that door." | | 9 | "She always did." | | 10 | "It never quite latched." | | 11 | "His chest heaved." | | 12 | "He had a hand pressed" | | 13 | "He laughed, and it turned" | | 14 | "She took a step" | | 15 | "He wiped his mouth with" | | 16 | "He turned and pushed at" | | 17 | "His footsteps rang below her," | | 18 | "You do not enter a" | | 19 | "You seal the entry point." |
| | ratio | 0.307 | |
| 73.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 88 | | matches | | 0 | "That was the gap, and" | | 1 | "Rain came sideways off the" | | 2 | "It stripped the warmth out" | | 3 | "Her radio bumped against her" | | 4 | "She'd stopped calling it in" | | 5 | "Control wanted a location and" | | 6 | "He wasn't running like a" | | 7 | "Dealers ran for open ground," | | 8 | "This one ran like a" | | 9 | "Her voice cracked off the" | | 10 | "He kept running." | | 11 | "The satchel bounced on his" | | 12 | "Something inside it clinked, glass" | | 13 | "A medic's noise." | | 14 | "She had the name from" | | 15 | "Tomás Herrera, twenty-nine, struck off," | | 16 | "He cut left into the" | | 17 | "Shutters down, tarpaulins slapping, the" | | 18 | "Fairy lights swung above the" | | 19 | "Quinn vaulted a chained bicycle," |
| | ratio | 0.773 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | | ratio | 0.011 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "She had the name from a Soho barman who'd suddenly discovered a passion for polishing the same glass over and over." | | 1 | "Shutters down, tarpaulins slapping, the smell of cold fat and yesterday's noodles hanging in the wet air." | | 2 | "A breath came up out of it that had nothing to do with the weather, warm and dry and stale, and under the staleness something else." | | 3 | "At the far end, half hidden behind a curtain of hanging chains, Tomás Herrera saw her and closed his eyes and said something that could only have been a prayer." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 17 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |