| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned slowly [slowly]" |
| | dialogueSentences | 9 | | tagDensity | 0.889 | | leniency | 1 | | rawRatio | 0.125 | | effectiveRatio | 0.125 | |
| 92.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1354 | | totalAiIsmAdverbs | 2 | | 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) | |
| 77.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1354 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "calibrated" | | 1 | "throbbed" | | 2 | "glistening" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "pounding" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 96.26% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 29 | | matches | | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 29 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "managed to" | | 2 | "tend to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 29 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 110 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1349 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1225 | | uniqueNames | 22 | | maxNameDensity | 0.82 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 10 | | Quinn | 1 | | Frith | 1 | | Street | 2 | | Soho | 1 | | Sandhurst | 1 | | Met | 1 | | Tomás | 9 | | Herrera | 1 | | Raven | 2 | | Nest | 2 | | Cockney | 1 | | Old | 1 | | Compton | 1 | | Veil | 1 | | Market | 1 | | Tube | 2 | | Camden | 1 | | Morris | 3 | | Go | 1 | | Romani | 1 | | Welsh | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Morris" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Sandhurst" | | 4 | "Met" | | 5 | "Old" | | 6 | "Compton" | | 7 | "Market" | | 8 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 26 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like damp earth and coal smoke" | | 1 | "smelled like sandalwood, rot, and fermente" |
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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 | 1349 | | matches | (empty) | |
| 51.72% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 29 | | matches | | |
| 87.72% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 74.94 | | std | 34.25 | | cv | 0.457 | | sampleLengths | | 0 | 107 | | 1 | 77 | | 2 | 154 | | 3 | 23 | | 4 | 83 | | 5 | 91 | | 6 | 73 | | 7 | 100 | | 8 | 18 | | 9 | 81 | | 10 | 56 | | 11 | 95 | | 12 | 105 | | 13 | 96 | | 14 | 81 | | 15 | 50 | | 16 | 28 | | 17 | 31 |
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| 81.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 29 | | matches | | |
| 70.55% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 206 | | matches | | 0 | "was running" | | 1 | "was standing" | | 2 | "was holding" | | 3 | "was handing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 29 | | ratio | 0.138 | | matches | | 0 | "Her breath came faster than she’d like—she was 41, 5’9”, sharp jawline sharp enough to slice paper, but the years of late nights and cheap coffee had taken their toll. She caught sight of Tomás again, his short curly dark brown hair matted to his forehead, his olive skin glistening with rain. When he reached up to yank his hood back, the scar along his left forearm peeked out from under his sleeve, thin and silver, and the Saint Christopher medallion around his neck caught the glow of a nearby streetlamp. She’d memorized that scar, that medallion, from the surveillance photos she’d taken outside his flat two weeks prior." | | 1 | "He didn’t slow. He veered into an alley off Old Compton Street, the mouth of the alley wrapped in peeling graffiti of ravens, their wings outstretched—the same symbol she’d seen painted on the side of The Raven’s Nest that morning. Harlow followed, her flashlight bobbing in her hand, the alley’s air thick with the smell of mold and spilled lager. At the far end, a set of concrete stairs descended into darkness, a rusted “NO ENTRY” sign hanging crookedly above the top step." | | 2 | "Harlow’s throat went dry. She’d heard rumors of this place—the Veil Market, an underground supernatural black market that moved locations every full moon. Word on the force had it that it operated out of an abandoned Tube station in Camden, but no cop had ever managed to get inside. The entry required a bone token, an offering to the market’s keepers, and anyone who tried to follow without one vanished without a trace." | | 3 | "When she reached the bottom of the stairs, she stepped into a long, abandoned Tube platform. The tracks were overgrown with patches of moss and weeds, and the platform was lined with makeshift stalls made from old wooden crates and folding tables. Vendors called out in languages she didn’t recognize—Romani, Welsh, a guttural language that might have been fae—and the air smelled like sandalwood, rot, and fermented black tea. A vendor with a stall covered in glowing vials waved at her, holding up a purple potion that bubbled softly, and Harlow ducked behind a stack of rusted metal suitcases, her heart pounding in her chest." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 258 | | adjectiveStacks | 1 | | stackExamples | | 0 | "known supernatural black market" |
| | adverbCount | 3 | | adverbRatio | 0.011627906976744186 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 29 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 29 | | mean | 46.52 | | std | 35.2 | | cv | 0.757 | | sampleLengths | | 0 | 19 | | 1 | 25 | | 2 | 39 | | 3 | 24 | | 4 | 8 | | 5 | 27 | | 6 | 31 | | 7 | 11 | | 8 | 20 | | 9 | 25 | | 10 | 109 | | 11 | 23 | | 12 | 83 | | 13 | 91 | | 14 | 73 | | 15 | 100 | | 16 | 16 | | 17 | 2 | | 18 | 81 | | 19 | 56 | | 20 | 95 | | 21 | 105 | | 22 | 96 | | 23 | 81 | | 24 | 50 | | 25 | 23 | | 26 | 2 | | 27 | 3 | | 28 | 31 |
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| 62.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.41379310344827586 | | totalSentences | 29 | | uniqueOpeners | 12 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 50.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 26 | | matches | | 0 | "She tracked the flash of" | | 1 | "Her left wrist throbbed, the" | | 2 | "She’d been trailing Tomás Herrera" | | 3 | "Her breath came faster than" | | 4 | "she yelled, her voice carrying" | | 5 | "He didn’t slow. He veered" | | 6 | "She thought of DS Morris," | | 7 | "She reached into her inner" | | 8 | "She stepped forward, her boots" | | 9 | "She’d seen this pattern before:" | | 10 | "She turned slowly, her hand" |
| | ratio | 0.423 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 24 | | totalSentences | 26 | | matches | | 0 | "The rain bit through Harlow" | | 1 | "She tracked the flash of" | | 2 | "The suspect darted between a" | | 3 | "Her left wrist throbbed, the" | | 4 | "She’d been trailing Tomás Herrera" | | 5 | "The former paramedic, the one" | | 6 | "Tonight, she’d spotted the green" | | 7 | "A taxi rounded the corner," | | 8 | "The driver leaned out the" | | 9 | "Her breath came faster than" | | 10 | "she yelled, her voice carrying" | | 11 | "He didn’t slow. He veered" | | 12 | "Tomás stopped at the base" | | 13 | "Harlow’s throat went dry. She’d" | | 14 | "She thought of DS Morris," | | 15 | "Tomás called back over his" | | 16 | "Harlow took a step forward," | | 17 | "She reached into her inner" | | 18 | "She stepped forward, her boots" | | 19 | "Tomás was standing at the" |
| | ratio | 0.923 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 26 | | matches | | 0 | "Now he was running, and" |
| | ratio | 0.038 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 9 | | matches | | 0 | "She tracked the flash of black leather around a corner of Frith Street, her boots squelching through puddles that mirrored the neon of Soho’s storefronts." | | 1 | "Tonight, she’d spotted the green neon sign of The Raven’s Nest glowing through the rain half an hour prior, parked two blocks away, watching as he slipped insid…" | | 2 | "Tomás stopped at the base of the stairs, fumbling in his jacket pocket. Harlow pressed her back to the brick wall of the alley, her hand hovering over the taser…" | | 3 | "Harlow’s throat went dry. She’d heard rumors of this place—the Veil Market, an underground supernatural black market that moved locations every full moon. Word …" | | 4 | "She thought of DS Morris, her partner of seven years, who’d died three years ago during a case that had started with a tip about a hidden market. She’d spent ev…" | | 5 | "She reached into her inner trench coat pocket, her fingers brushing the smooth surface of the bone she’d carried for three years. She pulled it out, holding it …" | | 6 | "She stepped forward, her boots hitting the first wet concrete step of the stairwell. The rain dripped off the edge of her trench coat onto the stairs behind her…" | | 7 | "When she reached the bottom of the stairs, she stepped into a long, abandoned Tube platform. The tracks were overgrown with patches of moss and weeds, and the p…" | | 8 | "Tomás was standing at the far end of the platform, talking to a woman with skin like polished obsidian and hair that flowed like shadow. The woman was holding a…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás called back, his voice tight with urgency:" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she yelled (yell)" | | 1 | "Tomás called back (call back)" |
| | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 1 | | effectiveRatio | 0.444 | |