| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "A terrifying voice whispered directly [directly]" |
| | dialogueSentences | 21 | | tagDensity | 0.476 | | leniency | 0.952 | | rawRatio | 0.1 | | effectiveRatio | 0.095 | |
| 76.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1490 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "completely" | | 1 | "sharply" | | 2 | "cautiously" | | 3 | "carefully" | | 4 | "perfectly" |
| |
| 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) | |
| 29.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1490 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | word | "practiced ease" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "weight" | | 1 | "chaotic" | | 2 | "practiced ease" | | 3 | "echoing" | | 4 | "oppressive" | | 5 | "gloom" | | 6 | "echoed" | | 7 | "footsteps" | | 8 | "clenching" | | 9 | "macabre" | | 10 | "mechanical" | | 11 | "firmly" | | 12 | "chill" | | 13 | "rhythmic" | | 14 | "vibrated" | | 15 | "symphony" | | 16 | "standard" |
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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 | 130 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 130 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1477 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1299 | | uniqueNames | 19 | | maxNameDensity | 1.31 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 17 | | Herrera | 14 | | Camden | 3 | | Saint | 1 | | Christopher | 1 | | High | 1 | | Street | 1 | | Metropolitan | 1 | | Police | 1 | | Tube | 1 | | London | 1 | | Morris | 4 | | Raven | 1 | | Nest | 1 | | Tomás | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Police" | | 6 | "Morris" | | 7 | "Raven" | | 8 | "Tomás" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.846 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | 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 | 1477 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 141 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 29.54 | | std | 18.75 | | cv | 0.635 | | sampleLengths | | 0 | 36 | | 1 | 59 | | 2 | 13 | | 3 | 62 | | 4 | 73 | | 5 | 67 | | 6 | 42 | | 7 | 14 | | 8 | 52 | | 9 | 46 | | 10 | 53 | | 11 | 30 | | 12 | 45 | | 13 | 11 | | 14 | 46 | | 15 | 15 | | 16 | 15 | | 17 | 12 | | 18 | 4 | | 19 | 5 | | 20 | 22 | | 21 | 25 | | 22 | 27 | | 23 | 7 | | 24 | 37 | | 25 | 11 | | 26 | 40 | | 27 | 20 | | 28 | 9 | | 29 | 8 | | 30 | 23 | | 31 | 30 | | 32 | 28 | | 33 | 11 | | 34 | 34 | | 35 | 40 | | 36 | 36 | | 37 | 57 | | 38 | 61 | | 39 | 40 | | 40 | 1 | | 41 | 29 | | 42 | 14 | | 43 | 26 | | 44 | 54 | | 45 | 3 | | 46 | 34 | | 47 | 13 | | 48 | 12 | | 49 | 25 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 218 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 141 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1312 | | adjectiveStacks | 2 | | stackExamples | | 0 | "thick, oppressive underground gloom." | | 1 | "glowing fungal-lit market" |
| | adverbCount | 44 | | adverbRatio | 0.03353658536585366 | | lyAdverbCount | 26 | | lyAdverbRatio | 0.019817073170731708 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 10.48 | | std | 5.64 | | cv | 0.538 | | sampleLengths | | 0 | 21 | | 1 | 15 | | 2 | 16 | | 3 | 6 | | 4 | 8 | | 5 | 13 | | 6 | 16 | | 7 | 13 | | 8 | 3 | | 9 | 15 | | 10 | 8 | | 11 | 8 | | 12 | 4 | | 13 | 24 | | 14 | 10 | | 15 | 5 | | 16 | 16 | | 17 | 12 | | 18 | 10 | | 19 | 5 | | 20 | 15 | | 21 | 19 | | 22 | 9 | | 23 | 13 | | 24 | 8 | | 25 | 5 | | 26 | 13 | | 27 | 8 | | 28 | 11 | | 29 | 5 | | 30 | 18 | | 31 | 4 | | 32 | 10 | | 33 | 6 | | 34 | 9 | | 35 | 21 | | 36 | 16 | | 37 | 20 | | 38 | 9 | | 39 | 9 | | 40 | 8 | | 41 | 21 | | 42 | 15 | | 43 | 17 | | 44 | 6 | | 45 | 8 | | 46 | 14 | | 47 | 1 | | 48 | 1 | | 49 | 14 |
| |
| 47.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3404255319148936 | | totalSentences | 141 | | uniqueOpeners | 48 | |
| 27.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 123 | | matches | | 0 | "Then the screaming started." |
| | ratio | 0.008 | |
| 93.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 123 | | matches | | 0 | "Her outstretched fingers grazed slick" | | 1 | "She skidded on the slickened" | | 2 | "He darted left down another" | | 3 | "He pushed forward, bursting out" | | 4 | "She slapped her free hand" | | 5 | "He zig-zagged through a mish-mash" | | 6 | "She tuned out the chaos." | | 7 | "She ignored the stinging cuts." | | 8 | "His wet trainers struggled for" | | 9 | "She launched herself forward, grabbing" | | 10 | "He kicked backward wildly." | | 11 | "She spat an angry red" | | 12 | "She dropped into the adjacent" | | 13 | "He wrenched open a rusted" | | 14 | "She descended the cracked stone" | | 15 | "He favored his right leg," | | 16 | "His smooth voice carried a" | | 17 | "He wiped cold rain from" | | 18 | "She tightened her grip on" | | 19 | "He produced a small yellowed" |
| | ratio | 0.317 | |
| 20.98% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 123 | | matches | | 0 | "Lungs burning, Detective Harlow Quinn" | | 1 | "Her outstretched fingers grazed slick" | | 2 | "She skidded on the slickened" | | 3 | "Tomás Herrera did not slow" | | 4 | "He darted left down another" | | 5 | "Streetlamps cut through the heavy" | | 6 | "A silver Saint Christopher medallion" | | 7 | "Quinn drew her heavy black" | | 8 | "Herrera ignored her." | | 9 | "He pushed forward, bursting out" | | 10 | "A crimson double-decker bus blared" | | 11 | "Herrera wove seamlessly through the" | | 12 | "Quinn followed without hesitation." | | 13 | "She slapped her free hand" | | 14 | "He zig-zagged through a mish-mash" | | 15 | "Pedestrians scattered in blind panic." | | 16 | "An angry street food vendor" | | 17 | "The pungent scent of sizzling" | | 18 | "She tuned out the chaos." | | 19 | "Herrera threw his entire body" |
| | ratio | 0.878 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | 0 | "Now she walked straight into" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 2 | | matches | | 0 | "She skidded on the slickened tarmac, her boots sliding, catching her balance with rigid military precision." | | 1 | "Now she walked straight into the literal nightmare underworld that tore her life apart." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 54.76% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Herrera pressed (press)" | | 1 | "A terrifying voice whispered directly (whisper)" |
| | dialogueSentences | 21 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.667 | | effectiveRatio | 0.19 | |