| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 963 | | 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) | |
| 42.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 963 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "flickered" | | 2 | "calculating" | | 3 | "echoed" | | 4 | "pulsed" | | 5 | "whisper" | | 6 | "silence" | | 7 | "porcelain" | | 8 | "resonated" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 963 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 893 | | uniqueNames | 23 | | maxNameDensity | 0.78 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Frith | 1 | | Street | 2 | | Herrera | 7 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | Morris | 4 | | Commercial | 1 | | Tube | 1 | | Town | 1 | | Deep | 1 | | Level | 1 | | Shelter | 1 | | Flying | 1 | | Squad | 1 | | Veil | 1 | | Market | 1 | | Blitz | 1 | | Quinn | 7 | | Tomás | 2 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Shelter" | | 7 | "Squad" | | 8 | "Market" | | 9 | "Quinn" | | 10 | "Tomás" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Camden" | | 4 | "Commercial" | | 5 | "Town" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 963 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 27.51 | | std | 25.64 | | cv | 0.932 | | sampleLengths | | 0 | 4 | | 1 | 56 | | 2 | 25 | | 3 | 8 | | 4 | 4 | | 5 | 54 | | 6 | 52 | | 7 | 2 | | 8 | 27 | | 9 | 79 | | 10 | 68 | | 11 | 21 | | 12 | 7 | | 13 | 2 | | 14 | 57 | | 15 | 32 | | 16 | 62 | | 17 | 3 | | 18 | 23 | | 19 | 66 | | 20 | 2 | | 21 | 2 | | 22 | 6 | | 23 | 3 | | 24 | 97 | | 25 | 14 | | 26 | 4 | | 27 | 41 | | 28 | 29 | | 29 | 32 | | 30 | 28 | | 31 | 24 | | 32 | 16 | | 33 | 8 | | 34 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 156 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 903 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.009966777408637873 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 8.6 | | std | 6.36 | | cv | 0.74 | | sampleLengths | | 0 | 3 | | 1 | 1 | | 2 | 5 | | 3 | 14 | | 4 | 18 | | 5 | 19 | | 6 | 12 | | 7 | 2 | | 8 | 7 | | 9 | 4 | | 10 | 8 | | 11 | 4 | | 12 | 2 | | 13 | 14 | | 14 | 13 | | 15 | 20 | | 16 | 5 | | 17 | 6 | | 18 | 14 | | 19 | 19 | | 20 | 12 | | 21 | 1 | | 22 | 2 | | 23 | 3 | | 24 | 15 | | 25 | 6 | | 26 | 3 | | 27 | 4 | | 28 | 14 | | 29 | 16 | | 30 | 4 | | 31 | 2 | | 32 | 39 | | 33 | 11 | | 34 | 13 | | 35 | 11 | | 36 | 19 | | 37 | 3 | | 38 | 11 | | 39 | 2 | | 40 | 6 | | 41 | 13 | | 42 | 7 | | 43 | 2 | | 44 | 5 | | 45 | 8 | | 46 | 1 | | 47 | 5 | | 48 | 21 | | 49 | 9 |
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| 72.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4864864864864865 | | totalSentences | 111 | | uniqueOpeners | 54 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Instead, it teemed with obscene" |
| | ratio | 0.011 | |
| 97.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 88 | | matches | | 0 | "She had watched him for" | | 1 | "Her shout cracked against the" | | 2 | "He did not stop." | | 3 | "Her boots splashed through puddles," | | 4 | "She had spent three nights" | | 5 | "His short dark curls clung" | | 6 | "He ran faster." | | 7 | "His left arm swung stiff," | | 8 | "Her sharp jaw tightened." | | 9 | "Her brown eyes tracked every" | | 10 | "She gained on him, boots" | | 11 | "He moved toward the old" | | 12 | "He reached into his coat." | | 13 | "His fingers closed around something" | | 14 | "He pressed it against a" | | 15 | "His voice dropped to a" | | 16 | "She could radio for backup." | | 17 | "She could call the Flying" | | 18 | "Her left wrist twisted, checking" | | 19 | "She stepped forward." |
| | ratio | 0.307 | |
| 56.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 88 | | matches | | 0 | "Boot hit pavement." | | 1 | "Quinn ran through the rain." | | 2 | "Needles of water stung her" | | 3 | "The worn leather watch on" | | 4 | "She had watched him for" | | 5 | "Tonight he had moved." | | 6 | "Her shout cracked against the" | | 7 | "He did not stop." | | 8 | "Her boots splashed through puddles," | | 9 | "The green sign of The" | | 10 | "She had spent three nights" | | 11 | "Herrera cut left toward the" | | 12 | "Rain sheeted down in solid" | | 13 | "His short dark curls clung" | | 14 | "He ran faster." | | 15 | "His left arm swung stiff," | | 16 | "A knife attack from his" | | 17 | "Her sharp jaw tightened." | | 18 | "Her brown eyes tracked every" | | 19 | "Herrera vaulted a low wall" |
| | ratio | 0.807 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 51.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 5 | | matches | | 0 | "Her boots splashed through puddles, sending up dark spray that glittered under the neon." | | 1 | "She had spent three nights in that dim bar, nursing whisky at the counter, watching the bookshelf that swung inward." | | 2 | "His short dark curls clung to his skull, the Saint Christopher medallion swinging against his chest, catching the streetlight." | | 3 | "The metal door slammed shut behind her with a reverberating clang that echoed through the empty streets." | | 4 | "Vendors with too many joints in their fingers displayed vials of liquid shadow that swirled against their glass." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.5 | | effectiveRatio | 0.182 | |