| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1680 | | totalAiIsmAdverbs | 3 | | 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) | |
| 64.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1680 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "pulsed" | | 5 | "pulse" | | 6 | "predator" | | 7 | "calculating" | | 8 | "facade" |
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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 | 182 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 182 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 196 | | 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 | 1680 | | ratio | 0 | | matches | (empty) | |
| 93.75% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 1 | | matches | | 0 | "Had been worse up close, she corrected herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1538 | | uniqueNames | 20 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 3 | | Leather | 1 | | Lane | 1 | | Hatton | 1 | | Garden | 1 | | Tube | 1 | | Morris | 2 | | Bermondsey | 2 | | Met | 4 | | Whitechapel | 2 | | Tuesday | 1 | | Underground | 1 | | Victorian | 1 | | Chinatown | 1 | | Hackney | 1 | | Croydon | 1 | | Thames | 1 | | Quinn | 12 | | Curious | 1 | | Three | 3 |
| | persons | | 0 | "Morris" | | 1 | "Met" | | 2 | "Underground" | | 3 | "Victorian" | | 4 | "Quinn" |
| | places | | 0 | "London" | | 1 | "Leather" | | 2 | "Lane" | | 3 | "Hatton" | | 4 | "Garden" | | 5 | "Bermondsey" | | 6 | "Whitechapel" | | 7 | "Chinatown" | | 8 | "Croydon" | | 9 | "Thames" | | 10 | "Three" |
| | globalScore | 1 | | windowScore | 1 | |
| 79.91% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like a setup" | | 1 | "quite punch through" | | 2 | "emolished, now apparently home to something h" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1680 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 196 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 23.33 | | std | 19.06 | | cv | 0.817 | | sampleLengths | | 0 | 46 | | 1 | 27 | | 2 | 32 | | 3 | 9 | | 4 | 37 | | 5 | 17 | | 6 | 2 | | 7 | 14 | | 8 | 2 | | 9 | 4 | | 10 | 57 | | 11 | 9 | | 12 | 67 | | 13 | 32 | | 14 | 4 | | 15 | 44 | | 16 | 8 | | 17 | 42 | | 18 | 2 | | 19 | 41 | | 20 | 27 | | 21 | 51 | | 22 | 24 | | 23 | 2 | | 24 | 5 | | 25 | 8 | | 26 | 36 | | 27 | 33 | | 28 | 36 | | 29 | 14 | | 30 | 11 | | 31 | 10 | | 32 | 28 | | 33 | 12 | | 34 | 16 | | 35 | 66 | | 36 | 6 | | 37 | 81 | | 38 | 7 | | 39 | 39 | | 40 | 15 | | 41 | 52 | | 42 | 10 | | 43 | 28 | | 44 | 50 | | 45 | 13 | | 46 | 40 | | 47 | 5 | | 48 | 8 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 182 | | matches | | |
| 14.81% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 252 | | matches | | 0 | "wasn't looking" | | 1 | "was breathing" | | 2 | "was talking" | | 3 | "was happening" | | 4 | "was beginning" | | 5 | "was simply rearranging" | | 6 | "was still smiling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1543 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 55 | | adverbRatio | 0.0356448476992871 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.008425145819831496 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 196 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 196 | | mean | 8.57 | | std | 6.46 | | cv | 0.753 | | sampleLengths | | 0 | 17 | | 1 | 29 | | 2 | 18 | | 3 | 5 | | 4 | 4 | | 5 | 9 | | 6 | 10 | | 7 | 13 | | 8 | 9 | | 9 | 13 | | 10 | 12 | | 11 | 5 | | 12 | 7 | | 13 | 6 | | 14 | 11 | | 15 | 2 | | 16 | 1 | | 17 | 3 | | 18 | 10 | | 19 | 2 | | 20 | 4 | | 21 | 9 | | 22 | 24 | | 23 | 9 | | 24 | 6 | | 25 | 9 | | 26 | 6 | | 27 | 3 | | 28 | 14 | | 29 | 17 | | 30 | 3 | | 31 | 5 | | 32 | 9 | | 33 | 19 | | 34 | 3 | | 35 | 2 | | 36 | 19 | | 37 | 8 | | 38 | 4 | | 39 | 13 | | 40 | 11 | | 41 | 20 | | 42 | 3 | | 43 | 5 | | 44 | 11 | | 45 | 2 | | 46 | 15 | | 47 | 7 | | 48 | 7 | | 49 | 2 |
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| 69.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.45408163265306123 | | totalSentences | 196 | | uniqueOpeners | 89 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 159 | | matches | | 0 | "Instead of cutting left toward" | | 1 | "Then a metallic screech that" | | 2 | "More sigils on the walls," | | 3 | "Somewhere ahead, she could hear" | | 4 | "More than once." | | 5 | "More like the underlying scaffolding" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 159 | | matches | | 0 | "His mistake came at the" | | 1 | "Her phone buzzed in her" | | 2 | "She ignored it." | | 3 | "She thought of Morris." | | 4 | "Her suspect had been here" | | 5 | "Her suspect had killed a" | | 6 | "She stepped into the corridor." | | 7 | "She paused, listened." | | 8 | "She killed the torch." | | 9 | "She'd heard rumours." | | 10 | "Her old guv'nor, who still" | | 11 | "She watched a woman with" | | 12 | "Her suspect stood at a" | | 13 | "He was talking to someone" | | 14 | "She pocketed it again." | | 15 | "She'd done it before." | | 16 | "She thought of Morris again." | | 17 | "I'll just be a minute." | | 18 | "She thought of the Whitechapel" | | 19 | "His wife, who'd found him" |
| | ratio | 0.22 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 159 | | matches | | 0 | "The suspect's shoulder caught a" | | 1 | "Quinn vaulted the rattan chairs" | | 2 | "The trick was patience." | | 3 | "His mistake came at the" | | 4 | "The kind of mistake that" | | 5 | "Quinn pressed her back against" | | 6 | "The leather grip creaked in" | | 7 | "The weight of the thing" | | 8 | "Water found its way down" | | 9 | "The bastard had vanished." | | 10 | "Quinn holstered the weapon and" | | 11 | "The beam cut through the" | | 12 | "Someone had painted a sigil" | | 13 | "Her phone buzzed in her" | | 14 | "She ignored it." | | 15 | "The air changed." | | 16 | "Something that tickled the back" | | 17 | "She thought of Morris." | | 18 | "The corridor breathed." | | 19 | "Quinn's torch beam caught something" |
| | ratio | 0.629 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 159 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn vaulted the rattan chairs that spilled across the alley mouth, her boots finding purchase on wet cobblestones slick with something that reeked of last wee…" | | 1 | "Carried a sweetness that had no business underground, like incense and old copper and something else she couldn't name." | | 2 | "Bone-white, smooth, carved with characters that made her eyes water if she looked too long." | | 3 | "Unarmed by Met standards, though the silver-capped stakes sewn into her coat lining had stopped feeling like superstition two cases back." | | 4 | "London geology didn't allow for it, and the woman who'd grown up listening to her father's stories about the Underground knew the city's bones better than most." | | 5 | "A faint greenish glow pulsed from the leftmost passage, as though the tunnel itself was breathing." | | 6 | "Stalls crowded every inch of available floor space, lit by oil lamps and candles and things that glowed without flame." | | 7 | "His wife, who'd found him sitting in his favourite chair with a cup of tea still warm in his hand." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |