| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1574 | | totalAiIsmAdverbs | 1 | | 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) | |
| 61.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1574 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "intricate" | | 2 | "gloom" | | 3 | "silence" | | 4 | "pulsed" | | 5 | "velvet" | | 6 | "silk" | | 7 | "echoing" | | 8 | "flicked" |
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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 | 137 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 137 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 9 | | totalWords | 1574 | | ratio | 0.006 | | matches | | 0 | "clack" | | 1 | "Protocol says wait for backup." | | 2 | "The pits." | | 3 | "Crunch." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1398 | | uniqueNames | 20 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 18 | | Tomás | 1 | | Herrera | 16 | | Camden | 2 | | London | 1 | | Underground | 1 | | Saint | 1 | | Christopher | 1 | | Rotherhithe | 1 | | Met | 1 | | West | 1 | | End | 1 | | English | 1 | | Spanish | 1 | | Backup | 2 | | Morris | 1 | | Savile | 1 | | Row | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Met" | | 7 | "Backup" | | 8 | "Morris" |
| | places | | 0 | "Detective" | | 1 | "London" | | 2 | "Rotherhithe" | | 3 | "West" | | 4 | "End" |
| | globalScore | 0.856 | | windowScore | 0.667 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like birch bark" | | 1 | "sounded like grinding stones" | | 2 | "felt like hitting an oak tree" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.635 | | wordCount | 1574 | | matches | | 0 | "Not the quiet of an abandoned building, but a heavy, pressurized hush" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 21.56 | | std | 19.23 | | cv | 0.892 | | sampleLengths | | 0 | 15 | | 1 | 38 | | 2 | 2 | | 3 | 23 | | 4 | 79 | | 5 | 89 | | 6 | 28 | | 7 | 20 | | 8 | 4 | | 9 | 5 | | 10 | 10 | | 11 | 39 | | 12 | 6 | | 13 | 10 | | 14 | 4 | | 15 | 20 | | 16 | 48 | | 17 | 4 | | 18 | 6 | | 19 | 5 | | 20 | 53 | | 21 | 6 | | 22 | 11 | | 23 | 4 | | 24 | 33 | | 25 | 51 | | 26 | 19 | | 27 | 12 | | 28 | 11 | | 29 | 1 | | 30 | 5 | | 31 | 48 | | 32 | 60 | | 33 | 19 | | 34 | 47 | | 35 | 16 | | 36 | 43 | | 37 | 25 | | 38 | 5 | | 39 | 24 | | 40 | 16 | | 41 | 45 | | 42 | 11 | | 43 | 15 | | 44 | 46 | | 45 | 52 | | 46 | 15 | | 47 | 1 | | 48 | 17 | | 49 | 65 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 137 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 247 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 161 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1404 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.014245014245014245 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002849002849002849 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 9.78 | | std | 5.85 | | cv | 0.598 | | sampleLengths | | 0 | 15 | | 1 | 8 | | 2 | 30 | | 3 | 2 | | 4 | 3 | | 5 | 20 | | 6 | 13 | | 7 | 17 | | 8 | 15 | | 9 | 10 | | 10 | 9 | | 11 | 15 | | 12 | 8 | | 13 | 17 | | 14 | 15 | | 15 | 4 | | 16 | 6 | | 17 | 15 | | 18 | 3 | | 19 | 21 | | 20 | 19 | | 21 | 9 | | 22 | 4 | | 23 | 16 | | 24 | 4 | | 25 | 5 | | 26 | 10 | | 27 | 6 | | 28 | 3 | | 29 | 15 | | 30 | 5 | | 31 | 10 | | 32 | 6 | | 33 | 10 | | 34 | 4 | | 35 | 20 | | 36 | 5 | | 37 | 9 | | 38 | 12 | | 39 | 6 | | 40 | 16 | | 41 | 4 | | 42 | 6 | | 43 | 5 | | 44 | 2 | | 45 | 8 | | 46 | 7 | | 47 | 26 | | 48 | 7 | | 49 | 3 |
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| 70.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4472049689440994 | | totalSentences | 161 | | uniqueOpeners | 72 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 126 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 126 | | matches | | 0 | "She vaulted the rolling cylinder" | | 1 | "He didn't stop." | | 2 | "They never did when they" | | 3 | "She took the corner wide," | | 4 | "He vaulted a chain-link fence" | | 5 | "She swung a leg over," | | 6 | "She dropped to the other" | | 7 | "She drew her baton, flicking" | | 8 | "His chest heaved." | | 9 | "He was going to need" | | 10 | "His sleeve rode up, exposing" | | 11 | "He wasn't looking at her" | | 12 | "His warm brown eyes darted" | | 13 | "He pulled out a small," | | 14 | "He pressed the bone token" | | 15 | "He descended into the dark." | | 16 | "She stepped back, eyeing the" | | 17 | "She holstered her baton, checked" | | 18 | "She wedged her boot into" | | 19 | "Her ribs scraped against the" |
| | ratio | 0.23 | |
| 63.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 126 | | matches | | 0 | "Herrera's boot clipped the metal" | | 1 | "She vaulted the rolling cylinder" | | 2 | "He didn't stop." | | 3 | "They never did when they" | | 4 | "Quinn pumped her arms, her" | | 5 | "The suspect ducked left, abandoning" | | 6 | "She took the corner wide," | | 7 | "Puddles splashed up her trousers," | | 8 | "He vaulted a chain-link fence" | | 9 | "A closed kebab shop flanked" | | 10 | "Grease and stale mutton hung" | | 11 | "Quinn hit the fence." | | 12 | "The links groaned under her" | | 13 | "She swung a leg over," | | 14 | "The strap held." | | 15 | "She dropped to the other" | | 16 | "Herrera stood twenty yards ahead" | | 17 | "Quinn slowed her pace." | | 18 | "She drew her baton, flicking" | | 19 | "Herrera backed against the iron" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 126 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 5 | | matches | | 0 | "Hundreds of voices, speaking in cadence that didn't sound like English, or Spanish, or anything human." | | 1 | "Candles sat in the grooves of the defunct escalator treads, made of tallow that burned with a smokeless purple flame." | | 2 | "She kept her military bearing in check, slouching her shoulders to mimic the furtive posture of the buyers around her." | | 3 | "Across from him stood a tall man in a butcher's apron stained with something that glowed faint green." | | 4 | "The baton snapped out, striking the creature's knee with a dull thud that felt like hitting an oak tree." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "The butcher chuckled (chuckle)" | | 1 | "Herrera yelled (yell)" |
| | dialogueSentences | 32 | | tagDensity | 0.094 | | leniency | 0.188 | | rawRatio | 0.667 | | effectiveRatio | 0.125 | |