| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.424 | | leniency | 0.848 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1156 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 78.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1156 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "familiar" | | 1 | "perfect" | | 2 | "analyzed" | | 3 | "aftermath" | | 4 | "silence" |
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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 | 35 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 35 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 54 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 5 | | totalWords | 1164 | | ratio | 0.004 | | matches | | 0 | "something" | | 1 | "ask the museum girl." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 745 | | uniqueNames | 15 | | maxNameDensity | 1.48 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Town | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Priya | 1 | | Anand | 5 | | High | 1 | | Street | 1 | | Webb | 3 | | West | 1 | | End | 1 | | Eleven | 1 | | Farrow | 3 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Anand" | | 3 | "Webb" | | 4 | "Farrow" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "High" | | 3 | "Street" | | 4 | "West" | | 5 | "End" | | 6 | "Eleven" |
| | globalScore | 0.762 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 27 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 28.18% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.718 | | wordCount | 1164 | | matches | | 0 | "not chalked, not painted, but scored, as if with something sharp" | | 1 | "not painted, but scored, as if with something sharp" |
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| 43.21% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 54 | | matches | | 0 | "let that sit" | | 1 | "learned that lesson" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 40.14 | | std | 31.73 | | cv | 0.79 | | sampleLengths | | 0 | 72 | | 1 | 40 | | 2 | 7 | | 3 | 4 | | 4 | 64 | | 5 | 76 | | 6 | 3 | | 7 | 38 | | 8 | 5 | | 9 | 4 | | 10 | 78 | | 11 | 7 | | 12 | 65 | | 13 | 6 | | 14 | 62 | | 15 | 21 | | 16 | 70 | | 17 | 5 | | 18 | 17 | | 19 | 74 | | 20 | 13 | | 21 | 81 | | 22 | 96 | | 23 | 26 | | 24 | 48 | | 25 | 5 | | 26 | 85 | | 27 | 77 | | 28 | 15 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 35 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 112 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 54 | | ratio | 0.074 | | matches | | 0 | "Webb lay on his back at the center of a rough circle scratched into the grime of the tile floor, arms and legs spread so that his body pointed north, south, east, west — if the tunnel had a north at all, forty feet under Camden High Street." | | 1 | "\"You didn't have to.\" Quinn walked the circle's perimeter slowly, hands in the pockets of her coat, studying the way the lines had been cut into the tile — not chalked, not painted, but scored, as if with something sharp and deliberate." | | 2 | "\"Of course it doesn't.\" Quinn stood, joints protesting, and turned in a slow circle to take in the whole platform — the peeling advertisement for a West End show that had closed in 2011, the rusted signal box, the absence, she now registered, of any rats." | | 3 | "She thought, not for the first time, of the antiques dealers she'd quietly flagged over the past eighteen months — men who dealt in objects that shouldn't have existed, who kept ledgers written in symbols her cryptographer couldn't place." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 743 | | adjectiveStacks | 1 | | stackExamples | | 0 | "north, south, east, west —" |
| | adverbCount | 19 | | adverbRatio | 0.02557200538358008 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006729475100942127 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 54 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 54 | | mean | 21.56 | | std | 16.64 | | cv | 0.772 | | sampleLengths | | 0 | 28 | | 1 | 44 | | 2 | 21 | | 3 | 19 | | 4 | 3 | | 5 | 4 | | 6 | 4 | | 7 | 12 | | 8 | 40 | | 9 | 12 | | 10 | 16 | | 11 | 48 | | 12 | 4 | | 13 | 4 | | 14 | 4 | | 15 | 3 | | 16 | 20 | | 17 | 18 | | 18 | 5 | | 19 | 4 | | 20 | 42 | | 21 | 36 | | 22 | 7 | | 23 | 51 | | 24 | 14 | | 25 | 6 | | 26 | 58 | | 27 | 4 | | 28 | 21 | | 29 | 46 | | 30 | 13 | | 31 | 11 | | 32 | 5 | | 33 | 7 | | 34 | 10 | | 35 | 34 | | 36 | 40 | | 37 | 13 | | 38 | 38 | | 39 | 43 | | 40 | 7 | | 41 | 33 | | 42 | 56 | | 43 | 5 | | 44 | 6 | | 45 | 15 | | 46 | 27 | | 47 | 21 | | 48 | 5 | | 49 | 49 |
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| 88.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.6296296296296297 | | totalSentences | 54 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 32 | | matches | (empty) | | ratio | 0 | |
| 82.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 32 | | matches | | 0 | "His eyes were open." | | 1 | "His mouth was open." | | 2 | "She crouched at last, close" | | 3 | "She let that sit" | | 4 | "She'd learned three years ago," | | 5 | "She had learned that lesson" | | 6 | "She hadn't understood it then." | | 7 | "She didn't fully understand it" | | 8 | "she told Anand, quiet enough" | | 9 | "She thought, not for the" | | 10 | "She thought of a name" |
| | ratio | 0.344 | |
| 53.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 26 | | totalSentences | 32 | | matches | | 0 | "The Camden Town platform hadn't" | | 1 | "Quinn descended without answering, her" | | 2 | "This scene had been wrong" | | 3 | "The platform smelled of ozone" | | 4 | "Webb lay on his back" | | 5 | "His eyes were open." | | 6 | "His mouth was open." | | 7 | "Anand crouched beside the body," | | 8 | "Quinn walked the circle's perimeter" | | 9 | "She crouched at last, close" | | 10 | "Quinn's eyes moved to the" | | 11 | "Quinn stood, joints protesting, and" | | 12 | "She let that sit" | | 13 | "Anand had the good sense" | | 14 | "DS Farrow arrived a few" | | 15 | "Farrow crouched where Anand had," | | 16 | "Quinn didn't argue with him," | | 17 | "She'd learned three years ago," | | 18 | "She had learned that lesson" | | 19 | "She hadn't understood it then." |
| | ratio | 0.813 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 32 | | matches | | | ratio | 0.031 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 3 | | matches | | 0 | "Now she stood at the top of the access stairs, breath fogging in the cold that had no business being this cold in a tunnel forty feet underground, and looked do…" | | 1 | "She'd learned three years ago, in a different tunnel not unlike this one, that arguing with men who needed the world to make ordinary sense was a waste of breat…" | | 2 | "She thought of a name that kept surfacing in the margins of three unsolved cases: a market that moved, that no ordinary informant could find, that left behind e…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn repeated (repeat)" |
| | dialogueSentences | 33 | | tagDensity | 0.152 | | leniency | 0.303 | | rawRatio | 0.2 | | effectiveRatio | 0.061 | |