| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2261 | | 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) | |
| 77.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2261 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "shattered" | | 2 | "flickered" | | 3 | "charged" | | 4 | "pawn" | | 5 | "trembled" | | 6 | "velvet" |
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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 | 1 | | narrationSentences | 221 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 221 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 302 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2261 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 28.44% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 94 | | wordCount | 1892 | | uniqueNames | 17 | | maxNameDensity | 2.43 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 46 | | Vauxhall | 1 | | Raven | 1 | | Nest | 2 | | Wardour | 1 | | Street | 1 | | Tomás | 1 | | Herrera | 25 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 2 | | Underground | 1 | | Morris | 4 | | Whitechapel | 1 | | Services | 1 | | Rain | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Services" | | 6 | "Rain" |
| | places | | 0 | "Vauxhall" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Whitechapel" |
| | globalScore | 0.284 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 155 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2261 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 302 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 213 | | mean | 10.62 | | std | 11.35 | | cv | 1.07 | | sampleLengths | | 0 | 22 | | 1 | 57 | | 2 | 7 | | 3 | 31 | | 4 | 3 | | 5 | 32 | | 6 | 9 | | 7 | 23 | | 8 | 19 | | 9 | 9 | | 10 | 9 | | 11 | 2 | | 12 | 23 | | 13 | 7 | | 14 | 17 | | 15 | 5 | | 16 | 8 | | 17 | 2 | | 18 | 2 | | 19 | 9 | | 20 | 42 | | 21 | 12 | | 22 | 1 | | 23 | 10 | | 24 | 3 | | 25 | 2 | | 26 | 22 | | 27 | 6 | | 28 | 7 | | 29 | 30 | | 30 | 28 | | 31 | 11 | | 32 | 9 | | 33 | 2 | | 34 | 2 | | 35 | 21 | | 36 | 6 | | 37 | 47 | | 38 | 9 | | 39 | 15 | | 40 | 36 | | 41 | 5 | | 42 | 22 | | 43 | 8 | | 44 | 5 | | 45 | 30 | | 46 | 2 | | 47 | 3 | | 48 | 7 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 221 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 328 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 302 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1900 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.013157894736842105 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005263157894736842 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 302 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 302 | | mean | 7.49 | | std | 5.08 | | cv | 0.679 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 2 | | 3 | 9 | | 4 | 28 | | 5 | 7 | | 6 | 8 | | 7 | 4 | | 8 | 19 | | 9 | 3 | | 10 | 16 | | 11 | 8 | | 12 | 8 | | 13 | 9 | | 14 | 8 | | 15 | 2 | | 16 | 9 | | 17 | 4 | | 18 | 19 | | 19 | 9 | | 20 | 9 | | 21 | 2 | | 22 | 11 | | 23 | 12 | | 24 | 7 | | 25 | 7 | | 26 | 2 | | 27 | 8 | | 28 | 5 | | 29 | 8 | | 30 | 2 | | 31 | 2 | | 32 | 9 | | 33 | 7 | | 34 | 11 | | 35 | 24 | | 36 | 12 | | 37 | 1 | | 38 | 4 | | 39 | 6 | | 40 | 3 | | 41 | 2 | | 42 | 3 | | 43 | 7 | | 44 | 12 | | 45 | 6 | | 46 | 7 | | 47 | 22 | | 48 | 5 | | 49 | 3 |
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| 51.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.34105960264900664 | | totalSentences | 302 | | uniqueOpeners | 103 | |
| 16.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 208 | | matches | | 0 | "Then Morris spoke through the" |
| | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 208 | | matches | | 0 | "He had carried nothing." | | 1 | "He came out at ten" | | 2 | "He turned north." | | 3 | "His reflection watched her from" | | 4 | "She had requested a surveillance" | | 5 | "Her inspector had refused." | | 6 | "He cut through an alley" | | 7 | "His body changed before his" | | 8 | "He vaulted a stack of" | | 9 | "He hooked left across the" | | 10 | "Its horn punched through the" | | 11 | "Its windows passed in bright" | | 12 | "She slapped the roof of" | | 13 | "Her breath rasped in the" | | 14 | "He had picked the route" | | 15 | "She reached for her radio." | | 16 | "He reached a motorcycle waiting" | | 17 | "She flashed her warrant card" | | 18 | "He saw the cab." | | 19 | "He struck the rider’s shoulder" |
| | ratio | 0.226 | |
| 12.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 186 | | totalSentences | 208 | | matches | | 0 | "Rain glazed the bonnet of" | | 1 | "Shirt sleeves rolled to the" | | 2 | "A pale scar ran along" | | 3 | "Quinn lowered herself behind the" | | 4 | "Herrera had entered the Nest" | | 5 | "He had carried nothing." | | 6 | "He came out at ten" | | 7 | "He turned north." | | 8 | "Quinn counted to three, left" | | 9 | "Rain tapped the shoulders of" | | 10 | "Herrera kept a brisk pace" | | 11 | "His reflection watched her from" | | 12 | "Quinn touched the leather strap" | | 13 | "She had requested a surveillance" | | 14 | "Her inspector had refused." | | 15 | "The blood on Herrera’s sleeve" | | 16 | "He cut through an alley" | | 17 | "The alley stank of wet" | | 18 | "Herrera reached the far end," | | 19 | "His body changed before his" |
| | ratio | 0.894 | |
| 24.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 208 | | matches | | 0 | "By the time it cleared," |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 1 | | matches | | 0 | "Or she could follow the wet footprints into a place that recognised evidence from a dead man’s pocket." |
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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 | |