| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1253 | | 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) | |
| 48.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1253 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "calibrated" | | 2 | "pulse" | | 3 | "gloom" | | 4 | "silence" | | 5 | "weight" | | 6 | "familiar" | | 7 | "vibrated" | | 8 | "glint" |
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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 | 127 | | matches | (empty) | |
| 97.86% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1253 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1253 | | uniqueNames | 20 | | maxNameDensity | 0.88 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 11 | | Metropolitan | 1 | | Police | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Seville | 1 | | Raven | 1 | | Nest | 1 | | Town | 1 | | Stop | 1 | | Tube | 1 | | Morris | 3 | | Met | 1 | | Manchester | 1 | | Since | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Morris" | | 7 | "Met" | | 8 | "Since" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Metropolitan" | | 4 | "Seville" | | 5 | "Town" | | 6 | "Manchester" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | 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 | 1253 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 25.26% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 6 | | mean | 208.83 | | std | 49.79 | | cv | 0.238 | | sampleLengths | | |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 211 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1262 | | adjectiveStacks | 1 | | stackExamples | | 0 | "paramedic turned underground stitcher." |
| | adverbCount | 23 | | adverbRatio | 0.018225039619651346 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003961965134706815 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.87 | | std | 6.86 | | cv | 0.695 | | sampleLengths | | 0 | 10 | | 1 | 8 | | 2 | 28 | | 3 | 11 | | 4 | 22 | | 5 | 6 | | 6 | 13 | | 7 | 8 | | 8 | 34 | | 9 | 2 | | 10 | 10 | | 11 | 3 | | 12 | 3 | | 13 | 6 | | 14 | 5 | | 15 | 17 | | 16 | 3 | | 17 | 15 | | 18 | 9 | | 19 | 21 | | 20 | 6 | | 21 | 7 | | 22 | 19 | | 23 | 24 | | 24 | 14 | | 25 | 4 | | 26 | 3 | | 27 | 28 | | 28 | 13 | | 29 | 7 | | 30 | 17 | | 31 | 2 | | 32 | 11 | | 33 | 7 | | 34 | 10 | | 35 | 22 | | 36 | 7 | | 37 | 10 | | 38 | 11 | | 39 | 5 | | 40 | 13 | | 41 | 4 | | 42 | 8 | | 43 | 15 | | 44 | 2 | | 45 | 5 | | 46 | 19 | | 47 | 12 | | 48 | 4 | | 49 | 5 |
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| 57.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.4251968503937008 | | totalSentences | 127 | | uniqueOpeners | 54 | |
| 84.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 118 | | matches | | 0 | "Just her, the dark, and" | | 1 | "Then she slid it back" | | 2 | "Just the weight of the" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 118 | | matches | | 0 | "Her boots struck the slick" | | 1 | "Her breath plumed in the" | | 2 | "She kept her center low," | | 3 | "She had his file memorized." | | 4 | "She also knew he moved" | | 5 | "She pushed harder." | | 6 | "Her left wrist caught the" | | 7 | "She did not check the" | | 8 | "She used the sensation to" | | 9 | "He slipped into a narrow" | | 10 | "Her shoulder brushed damp stone" | | 11 | "He had slipped through a" | | 12 | "Her boots sank into mud." | | 13 | "She pressed flat against a" | | 14 | "She did not blink." | | 15 | "She twisted the strap, a" | | 16 | "He spoke to a hooded" | | 17 | "Her right hand found the" | | 18 | "She could call it in." | | 19 | "She could back out, report" |
| | ratio | 0.271 | |
| 91.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 118 | | matches | | 0 | "Rain fell in cold needled" | | 1 | "Detective Harlow Quinn did not" | | 2 | "Her boots struck the slick" | | 3 | "Her breath plumed in the" | | 4 | "Quinn’s hand drifted to her" | | 5 | "She kept her center low," | | 6 | "The streetlamp’s amber bleed caught" | | 7 | "The name surfaced in her" | | 8 | "She had his file memorized." | | 9 | "She also knew he moved" | | 10 | "She pushed harder." | | 11 | "The rain soaked through her" | | 12 | "Water ran in cold rivulets" | | 13 | "Her left wrist caught the" | | 14 | "She did not check the" | | 15 | "She used the sensation to" | | 16 | "Herrera ducked past a towering" | | 17 | "The sign buzzed like an" | | 18 | "Quinn knew the place." | | 19 | "Silas owned it." |
| | ratio | 0.737 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 118 | | matches | | 0 | "Now they held stalls." | | 1 | "If she brought a squad" | | 2 | "To wait for backup." | | 3 | "To follow procedure." | | 4 | "If she let him pass," | | 5 | "If she followed, she stepped" |
| | ratio | 0.051 | |
| 58.11% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 7 | | matches | | 0 | "Eighteen years of chasing men who knew how to disappear taught you to trust your body before your eyes." | | 1 | "A chain-link fence blocked the path, sagging on rusted hinges and secured with a padlock that had long since forfeited its bite." | | 2 | "Cloaked vendors stood behind tables laden with curved glass vials, bundles of dried roots, and metal instruments that caught the lantern light." | | 3 | "She twisted the strap, a nervous habit that had become a grounding ritual since the warehouse three years ago." | | 4 | "Just her, the dark, and a market that moved every full moon, that required bone tokens for entry, that existed because the daylight world needed somewhere to du…" | | 5 | "She could hear the murmur of haggling below, the clink of glass, the low thrum of something that vibrated in her teeth." | | 6 | "Something that required off-book medics and hidden markets and men who vanished into concrete tombs." |
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| 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 | |