| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1244 | | 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) | |
| 51.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1244 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "treacherous" | | 1 | "stark" | | 2 | "calculated" | | 3 | "shattered" | | 4 | "maw" | | 5 | "depths" | | 6 | "chill" | | 7 | "rhythmic" | | 8 | "hulking" | | 9 | "cacophony" | | 10 | "constructed" | | 11 | "crystal" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 124 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 124 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1234 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1234 | | uniqueNames | 20 | | maxNameDensity | 0.89 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 2 | | Harlow | 1 | | Quinn | 10 | | Metropolitan | 1 | | Police | 1 | | Tomás | 1 | | Herrera | 11 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Seville | 1 | | Spain | 1 | | Glock | 2 | | St | 1 | | Detective | 2 | | Tube | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Glock" | | 6 | "Morris" | | 7 | "Market" | | 8 | "You" |
| | places | | 0 | "Metropolitan" | | 1 | "Soho" | | 2 | "Raven" | | 3 | "Seville" | | 4 | "Spain" | | 5 | "St" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1234 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 35.26 | | std | 27.3 | | cv | 0.774 | | sampleLengths | | 0 | 94 | | 1 | 49 | | 2 | 66 | | 3 | 124 | | 4 | 29 | | 5 | 26 | | 6 | 6 | | 7 | 67 | | 8 | 13 | | 9 | 10 | | 10 | 28 | | 11 | 16 | | 12 | 19 | | 13 | 39 | | 14 | 8 | | 15 | 31 | | 16 | 27 | | 17 | 35 | | 18 | 26 | | 19 | 89 | | 20 | 13 | | 21 | 52 | | 22 | 35 | | 23 | 31 | | 24 | 10 | | 25 | 29 | | 26 | 11 | | 27 | 17 | | 28 | 7 | | 29 | 25 | | 30 | 71 | | 31 | 22 | | 32 | 11 | | 33 | 61 | | 34 | 37 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 202 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 124 | | ratio | 0.008 | | matches | | 0 | "As his wet jacket sleeve retreated, a jagged white scar on his left forearm stood stark against his skin—the remnant of a knife attack he refused to explain to the local constabulary." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1244 | | adjectiveStacks | 1 | | stackExamples | | 0 | "unauthorized, bizarre medical treatments" |
| | adverbCount | 19 | | adverbRatio | 0.01527331189710611 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007234726688102894 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 9.95 | | std | 6.23 | | cv | 0.626 | | sampleLengths | | 0 | 5 | | 1 | 18 | | 2 | 11 | | 3 | 19 | | 4 | 15 | | 5 | 26 | | 6 | 10 | | 7 | 13 | | 8 | 1 | | 9 | 25 | | 10 | 9 | | 11 | 5 | | 12 | 18 | | 13 | 4 | | 14 | 19 | | 15 | 11 | | 16 | 4 | | 17 | 9 | | 18 | 11 | | 19 | 14 | | 20 | 21 | | 21 | 19 | | 22 | 17 | | 23 | 4 | | 24 | 4 | | 25 | 3 | | 26 | 18 | | 27 | 15 | | 28 | 14 | | 29 | 12 | | 30 | 4 | | 31 | 10 | | 32 | 4 | | 33 | 2 | | 34 | 5 | | 35 | 6 | | 36 | 3 | | 37 | 3 | | 38 | 4 | | 39 | 10 | | 40 | 4 | | 41 | 32 | | 42 | 6 | | 43 | 7 | | 44 | 10 | | 45 | 10 | | 46 | 5 | | 47 | 13 | | 48 | 4 | | 49 | 6 |
| |
| 50.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3790322580645161 | | totalSentences | 124 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 67.93% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 121 | | matches | | 0 | "She kept her brown eyes" | | 1 | "She moved with rigid efficiency," | | 2 | "She checked the worn leather" | | 3 | "His boots slapped the mud." | | 4 | "He hurdled a discarded wooden" | | 5 | "He pushed off immediately." | | 6 | "His olive skin shone with" | | 7 | "She had planted herself across" | | 8 | "She had watched Herrera approach" | | 9 | "She knew his file." | | 10 | "He hit the chain-link fence" | | 11 | "Her sharp jaw set." | | 12 | "She kept the muzzle of" | | 13 | "He let go of the" | | 14 | "He turned slowly." | | 15 | "His chest heaved." | | 16 | "He raised his hands." | | 17 | "You need to step back," | | 18 | "You lack the context for" | | 19 | "His warm brown eyes darted." |
| | ratio | 0.38 | |
| 46.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 121 | | matches | | 0 | "Rain slashed the Camden cobblestones." | | 1 | "Water poured from the canvas" | | 2 | "Detective Harlow Quinn ignored the" | | 3 | "She kept her brown eyes" | | 4 | "She moved with rigid efficiency," | | 5 | "Water flattened her closely cropped" | | 6 | "She checked the worn leather" | | 7 | "The full moon remained hidden" | | 8 | "His boots slapped the mud." | | 9 | "He hurdled a discarded wooden" | | 10 | "He pushed off immediately." | | 11 | "His olive skin shone with" | | 12 | "Quinn closed the distance." | | 13 | "The chase had started two" | | 14 | "She had planted herself across" | | 15 | "The bar's distinctive green neon" | | 16 | "She had watched Herrera approach" | | 17 | "She knew his file." | | 18 | "Herrera ducked into a dead-end" | | 19 | "He hit the chain-link fence" |
| | ratio | 0.826 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |