| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn stepped back [back]" |
| | dialogueSentences | 101 | | tagDensity | 0.129 | | leniency | 0.257 | | rawRatio | 0.077 | | effectiveRatio | 0.02 | |
| 97.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2350 | | 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) | |
| 85.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2350 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "measured" | | 2 | "etched" | | 3 | "quivered" | | 4 | "echoed" | | 5 | "scanned" | | 6 | "furrowed" |
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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 | 187 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 187 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 275 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2350 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 81 | | wordCount | 1615 | | uniqueNames | 12 | | maxNameDensity | 1.92 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 1 | | Quinn | 31 | | Colin | 1 | | Wren | 24 | | Cold | 1 | | Morris | 1 | | Limehouse | 1 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 17 | | Four | 1 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Colin" | | 3 | "Wren" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Four" |
| | places | | | globalScore | 0.54 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | glossingSentenceCount | 1 | | matches | | 0 | "as if expecting her to laugh" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.426 | | wordCount | 2350 | | matches | | 0 | "not at the body now but at the absences" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 275 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 144 | | mean | 16.32 | | std | 16.93 | | cv | 1.038 | | sampleLengths | | 0 | 74 | | 1 | 16 | | 2 | 71 | | 3 | 11 | | 4 | 24 | | 5 | 4 | | 6 | 25 | | 7 | 5 | | 8 | 3 | | 9 | 37 | | 10 | 11 | | 11 | 3 | | 12 | 17 | | 13 | 1 | | 14 | 33 | | 15 | 46 | | 16 | 5 | | 17 | 1 | | 18 | 31 | | 19 | 3 | | 20 | 44 | | 21 | 2 | | 22 | 2 | | 23 | 3 | | 24 | 2 | | 25 | 2 | | 26 | 6 | | 27 | 16 | | 28 | 10 | | 29 | 2 | | 30 | 66 | | 31 | 36 | | 32 | 5 | | 33 | 3 | | 34 | 12 | | 35 | 7 | | 36 | 16 | | 37 | 5 | | 38 | 65 | | 39 | 3 | | 40 | 11 | | 41 | 9 | | 42 | 4 | | 43 | 38 | | 44 | 7 | | 45 | 22 | | 46 | 5 | | 47 | 38 | | 48 | 7 | | 49 | 3 |
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| 99.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 187 | | matches | | 0 | "been disturbed" | | 1 | "been ripped" | | 2 | "been rubbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 259 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 275 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1616 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.019183168316831683 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018564356435643563 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 275 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 275 | | mean | 8.55 | | std | 7.4 | | cv | 0.866 | | sampleLengths | | 0 | 38 | | 1 | 10 | | 2 | 8 | | 3 | 18 | | 4 | 16 | | 5 | 24 | | 6 | 6 | | 7 | 8 | | 8 | 11 | | 9 | 12 | | 10 | 10 | | 11 | 11 | | 12 | 24 | | 13 | 4 | | 14 | 17 | | 15 | 8 | | 16 | 5 | | 17 | 3 | | 18 | 5 | | 19 | 32 | | 20 | 5 | | 21 | 6 | | 22 | 3 | | 23 | 17 | | 24 | 1 | | 25 | 6 | | 26 | 27 | | 27 | 5 | | 28 | 9 | | 29 | 2 | | 30 | 17 | | 31 | 13 | | 32 | 5 | | 33 | 1 | | 34 | 14 | | 35 | 1 | | 36 | 3 | | 37 | 13 | | 38 | 2 | | 39 | 1 | | 40 | 9 | | 41 | 9 | | 42 | 10 | | 43 | 12 | | 44 | 2 | | 45 | 2 | | 46 | 2 | | 47 | 2 | | 48 | 3 | | 49 | 2 |
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| 51.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.33090909090909093 | | totalSentences | 275 | | uniqueOpeners | 91 | |
| 21.93% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 152 | | matches | | 0 | "Then she scanned the rest" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 152 | | matches | | 0 | "His chin rested on his" | | 1 | "His coat had burned through" | | 2 | "She ignored it." | | 3 | "He drank instead and grimaced." | | 4 | "Her shoes crunched over old" | | 5 | "She touched the bench slat" | | 6 | "He took another drink, more" | | 7 | "They’d been ripped out years" | | 8 | "They weren’t police issue." | | 9 | "She stepped to the platform" | | 10 | "She gave the bag back" | | 11 | "She paced the platform, slow," | | 12 | "She scraped a bit with" | | 13 | "It came away greasy." | | 14 | "He signalled to a scene" | | 15 | "She swung the beam across" | | 16 | "She had boxed the report," | | 17 | "She rose and walked to" | | 18 | "She crouched again." | | 19 | "She picked it up with" |
| | ratio | 0.191 | |
| 58.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 152 | | matches | | 0 | "The platform lay two storeys" | | 1 | "Dust clung to the tiled" | | 2 | "Quinn stopped at the foot" | | 3 | "A dead man sat upright" | | 4 | "His chin rested on his" | | 5 | "His coat had burned through" | | 6 | "The station clock on the" | | 7 | "DS Colin Wren ducked under" | | 8 | "Quinn looked past him" | | 9 | "Wren held out the coffee." | | 10 | "She ignored it." | | 11 | "He drank instead and grimaced." | | 12 | "Quinn stepped under the tape." | | 13 | "Her shoes crunched over old" | | 14 | "Wren watched her circle the" | | 15 | "Quinn crouched by the bench." | | 16 | "The dead man’s hands sat" | | 17 | "A thin line of soot" | | 18 | "She touched the bench slat" | | 19 | "Quinn leaned closer to the" |
| | ratio | 0.803 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 152 | | matches | | 0 | "Before that, there had been" | | 1 | "Now that same sheen clung" |
| | ratio | 0.013 | |
| 85.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 5 | | matches | | 0 | "The platform lay two storeys beneath Camden, sealed on every map Quinn had seen, yet bright with police lamps and the weak shiver of old station bulbs that shou…" | | 1 | "And there, six feet from the body, a clean oval patch cut through the dust to the stone beneath, as if something heavy with a flat base had stood there for a wh…" | | 2 | "A draft that smelled of rain, mould, and something sweeter under it, like bruised herbs." | | 3 | "Eva reached into her coat, produced a lanyard from the British Museum and an expression that made younger men doubt themselves." | | 4 | "Halfway down the wall, at waist height, three more silver marks burned into view, spaced as if someone had steadied themselves while stepping out of thin air." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 101 | | tagDensity | 0.069 | | leniency | 0.139 | | rawRatio | 0 | | effectiveRatio | 0 | |