| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 1 | | adverbTags | | 0 | "The woman's smile showed too [too]" |
| | dialogueSentences | 9 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.333 | | effectiveRatio | 0.222 | |
| 85.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1004 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "suddenly" |
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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) | |
| 85.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1004 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulse" | | 1 | "silence" | | 2 | "absolutely" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1004 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 953 | | uniqueNames | 5 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Mornington | 1 | | Crescent | 1 | | Quinn | 8 | | Tube | 1 | | Morris | 4 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.996 | | wordCount | 1004 | | matches | | 0 | "no longer her thin white beam but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 25.74 | | std | 24.06 | | cv | 0.935 | | sampleLengths | | 0 | 27 | | 1 | 3 | | 2 | 62 | | 3 | 5 | | 4 | 6 | | 5 | 47 | | 6 | 80 | | 7 | 18 | | 8 | 3 | | 9 | 48 | | 10 | 1 | | 11 | 44 | | 12 | 37 | | 13 | 11 | | 14 | 6 | | 15 | 17 | | 16 | 24 | | 17 | 15 | | 18 | 52 | | 19 | 34 | | 20 | 5 | | 21 | 52 | | 22 | 3 | | 23 | 3 | | 24 | 70 | | 25 | 9 | | 26 | 8 | | 27 | 84 | | 28 | 13 | | 29 | 38 | | 30 | 65 | | 31 | 5 | | 32 | 4 | | 33 | 10 | | 34 | 20 | | 35 | 5 | | 36 | 8 | | 37 | 52 | | 38 | 10 |
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| 97.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 94 | | matches | | 0 | "been welded" | | 1 | "was riddled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 100 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 957 | | adjectiveStacks | 1 | | stackExamples | | 0 | "pressed cold against her" |
| | adverbCount | 26 | | adverbRatio | 0.027168234064785787 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00522466039707419 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 10.04 | | std | 9.06 | | cv | 0.902 | | sampleLengths | | 0 | 27 | | 1 | 3 | | 2 | 8 | | 3 | 33 | | 4 | 3 | | 5 | 18 | | 6 | 5 | | 7 | 3 | | 8 | 3 | | 9 | 20 | | 10 | 14 | | 11 | 5 | | 12 | 8 | | 13 | 4 | | 14 | 21 | | 15 | 25 | | 16 | 6 | | 17 | 12 | | 18 | 12 | | 19 | 10 | | 20 | 2 | | 21 | 1 | | 22 | 5 | | 23 | 3 | | 24 | 2 | | 25 | 1 | | 26 | 26 | | 27 | 19 | | 28 | 1 | | 29 | 2 | | 30 | 17 | | 31 | 8 | | 32 | 12 | | 33 | 5 | | 34 | 11 | | 35 | 9 | | 36 | 4 | | 37 | 13 | | 38 | 4 | | 39 | 7 | | 40 | 6 | | 41 | 17 | | 42 | 3 | | 43 | 2 | | 44 | 19 | | 45 | 4 | | 46 | 9 | | 47 | 2 | | 48 | 9 | | 49 | 2 |
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| 73.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.47 | | totalSentences | 100 | | uniqueOpeners | 47 | |
| 41.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 80 | | matches | | | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 80 | | matches | | 0 | "Her boots hit the wet" | | 1 | "She ignored it." | | 2 | "He didn't stop." | | 3 | "They never did." | | 4 | "He cut left down a" | | 5 | "Her lungs burned, and the" | | 6 | "She kept her eyes on" | | 7 | "He swung round a skip" | | 8 | "Her fingers grazed wet fabric." | | 9 | "She slammed her shoulder against" | | 10 | "She pulled her torch and" | | 11 | "She stared at it." | | 12 | "He hadn't come back up." | | 13 | "She went down." | | 14 | "She killed the torch and" | | 15 | "She filed it away in" | | 16 | "She caught him near the" | | 17 | "He'd stopped running." | | 18 | "He thought he was safe." | | 19 | "He was talking to a" |
| | ratio | 0.263 | |
| 78.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 80 | | matches | | 0 | "The man in the grey" | | 1 | "Her boots hit the wet" | | 2 | "Rain sheeted off the awnings" | | 3 | "She ignored it." | | 4 | "He didn't stop." | | 5 | "They never did." | | 6 | "He cut left down a" | | 7 | "A man in a stained" | | 8 | "Camden minded its own business." | | 9 | "That was half the reason" | | 10 | "Quinn pumped her arms." | | 11 | "Her lungs burned, and the" | | 12 | "She kept her eyes on" | | 13 | "That bag had pulled her" | | 14 | "A witness, a name, a" | | 15 | "He swung round a skip" | | 16 | "Her fingers grazed wet fabric." | | 17 | "Sideways, between a bricked-up arch" | | 18 | "She slammed her shoulder against" | | 19 | "The mouth of an old" |
| | ratio | 0.763 | |
| 62.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 80 | | matches | | 0 | "Now, there was the hood." |
| | ratio | 0.013 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 5 | | matches | | 0 | "Rain sheeted off the awnings of shuttered shops, turning the gutters into little black rivers, and somewhere behind her a bus hissed past, throwing up a wave th…" | | 1 | "Died, mid-word, the little green light winking out as if someone had reached into her pocket and pinched it." | | 2 | "Tallow smoke and something sweeter, something that caught at the back of her throat like burnt sugar and copper." | | 3 | "By the final turn it had become voices, hundreds of them, haggling and laughing and whispering, and the light changed, no longer her thin white beam but a warm …" | | 4 | "He was talking to a tall figure in a plague doctor's mask, passing the canvas bag across the counter, and Quinn moved through the crowd low and quiet, the way M…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "the woman agreed (agree)" |
| | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.5 | | effectiveRatio | 0.222 | |