| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1226 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | 0 | "perfectly" | | 1 | "completely" |
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| 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) | |
| 63.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1226 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "traced" | | 1 | "standard" | | 2 | "etched" | | 3 | "trembled" | | 4 | "magnetic" | | 5 | "scanned" | | 6 | "flickered" | | 7 | "weight" | | 8 | "etching" |
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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 | 203 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 203 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 203 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 17 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1226 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 8 | | matches | | 0 | "Check the brickwork around the frame, Quinn said." | | 1 | "Vandals patching holes, Lin muttered." | | 2 | "Evidence bag it, Lin ordered a tech crossing the tracks." | | 3 | "Suicide victims do not carry instruments that track magnetic anomalies, Quinn said." | | 4 | "Alchemical residue, Lin muttered." | | 5 | "Entry fee, she said quietly." | | 6 | "Something cheaper, Quinn said." | | 7 | "Bring me the archive files, Quinn said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1226 | | uniqueNames | 10 | | maxNameDensity | 1.55 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 19 | | Lin | 9 | | Vane | 2 | | Haverstock | 1 | | Hill | 1 | | Tuesday | 1 | | British | 1 | | Museum | 1 | | Camden | 1 | | Vandals | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Lin" | | 2 | "Vane" | | 3 | "Museum" | | 4 | "Camden" | | 5 | "Vandals" |
| | places | | 0 | "Haverstock" | | 1 | "Hill" | | 2 | "British" |
| | globalScore | 0.725 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | 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 | 1226 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 203 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 42.28 | | std | 26.43 | | cv | 0.625 | | sampleLengths | | 0 | 66 | | 1 | 48 | | 2 | 88 | | 3 | 58 | | 4 | 46 | | 5 | 10 | | 6 | 12 | | 7 | 40 | | 8 | 11 | | 9 | 19 | | 10 | 8 | | 11 | 60 | | 12 | 14 | | 13 | 38 | | 14 | 30 | | 15 | 82 | | 16 | 28 | | 17 | 91 | | 18 | 29 | | 19 | 56 | | 20 | 15 | | 21 | 70 | | 22 | 74 | | 23 | 26 | | 24 | 5 | | 25 | 12 | | 26 | 80 | | 27 | 52 | | 28 | 58 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 203 | | matches | | 0 | "been sealed" | | 1 | "was murdered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 226 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 203 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1227 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.022819885900570498 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007334963325183374 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 203 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 203 | | mean | 6.04 | | std | 3.01 | | cv | 0.498 | | sampleLengths | | 0 | 9 | | 1 | 13 | | 2 | 9 | | 3 | 5 | | 4 | 11 | | 5 | 7 | | 6 | 7 | | 7 | 5 | | 8 | 7 | | 9 | 9 | | 10 | 4 | | 11 | 5 | | 12 | 3 | | 13 | 5 | | 14 | 7 | | 15 | 8 | | 16 | 2 | | 17 | 12 | | 18 | 8 | | 19 | 9 | | 20 | 2 | | 21 | 2 | | 22 | 2 | | 23 | 6 | | 24 | 8 | | 25 | 4 | | 26 | 9 | | 27 | 3 | | 28 | 3 | | 29 | 10 | | 30 | 8 | | 31 | 17 | | 32 | 10 | | 33 | 9 | | 34 | 5 | | 35 | 5 | | 36 | 7 | | 37 | 5 | | 38 | 8 | | 39 | 10 | | 40 | 11 | | 41 | 6 | | 42 | 4 | | 43 | 4 | | 44 | 3 | | 45 | 8 | | 46 | 2 | | 47 | 2 | | 48 | 5 | | 49 | 5 |
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| 69.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.45320197044334976 | | totalSentences | 203 | | uniqueOpeners | 92 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 180 | | matches | | 0 | "Probably a kick while he" | | 1 | "Then they pushed it shut." | | 2 | "Even a tide pushes against" | | 3 | "Then they swapped the bricks" | | 4 | "Too heavy for ivory." | | 5 | "Too light for stone." |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 180 | | matches | | 0 | "She flipped her flashlight on." | | 1 | "He wiped a smear of" | | 2 | "His hands rested open on" | | 3 | "She ran a latex gloved" | | 4 | "She shifted the flashlight." | | 5 | "His shoes sat perfectly parallel" | | 6 | "She traced a faint trail" | | 7 | "She pressed her palm against" | | 8 | "She tapped the stone." | | 9 | "She stepped back." | | 10 | "He frowned at the wall." | | 11 | "We tagged the track edge." | | 12 | "She crouched again." | | 13 | "She lifted it with forceps." | | 14 | "It pointed straight at the" | | 15 | "She held the compass up." | | 16 | "She scanned the platform." | | 17 | "She followed the waterline." | | 18 | "It stopped abruptly at a" | | 19 | "His shoes touched the water's" |
| | ratio | 0.239 | |
| 87.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 134 | | totalSentences | 180 | | matches | | 0 | "Rain drummed against the rusted" | | 1 | "Harlow Quinn stepped down the" | | 2 | "The air tasted of wet" | | 3 | "She flipped her flashlight on." | | 4 | "The beam cut through the" | | 5 | "A body lay near the" | | 6 | "DS Lin looked up as" | | 7 | "He wiped a smear of" | | 8 | "Trench coat heavy with water." | | 9 | "Forensics estimates blunt force trauma." | | 10 | "Toxicology reports show heavy opioids" | | 11 | "The victim wore a tailored" | | 12 | "His hands rested open on" | | 13 | "She ran a latex gloved" | | 14 | "The station was a muck" | | 15 | "The dead man faced like" | | 16 | "She shifted the flashlight." | | 17 | "His shoes sat perfectly parallel" | | 18 | "Drifters who fell in here" | | 19 | "This man placed his feet" |
| | ratio | 0.744 | |
| 27.78% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 180 | | matches | | 0 | "If someone built a dry" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | 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 | |