| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Tomás hesitates his fingers tightening around [around]" |
| | dialogueSentences | 23 | | tagDensity | 0.739 | | leniency | 1 | | rawRatio | 0.059 | | effectiveRatio | 0.059 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1204 | | 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) | |
| 66.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1204 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silence" | | 1 | "glinting" | | 2 | "familiar" | | 3 | "encounter" | | 4 | "simmering" | | 5 | "scanning" | | 6 | "throb" | | 7 | "looms" |
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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 | 58 | | matches | (empty) | |
| 68.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 61 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1182 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1074 | | uniqueNames | 15 | | maxNameDensity | 1.02 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Morris | 2 | | Soho | 2 | | Harlow | 11 | | Tube | 1 | | Camden | 2 | | Veil | 1 | | Market | 1 | | Transport | 1 | | London | 1 | | Raven | 2 | | Nest | 2 | | Tomás | 9 | | Herrera | 2 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Morris" | | 1 | "Harlow" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Market" | | 3 | "London" |
| | globalScore | 0.988 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1182 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 61 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 45.46 | | std | 35.46 | | cv | 0.78 | | sampleLengths | | 0 | 18 | | 1 | 96 | | 2 | 105 | | 3 | 97 | | 4 | 143 | | 5 | 11 | | 6 | 4 | | 7 | 88 | | 8 | 35 | | 9 | 17 | | 10 | 23 | | 11 | 63 | | 12 | 26 | | 13 | 49 | | 14 | 33 | | 15 | 17 | | 16 | 24 | | 17 | 23 | | 18 | 52 | | 19 | 45 | | 20 | 57 | | 21 | 65 | | 22 | 8 | | 23 | 64 | | 24 | 9 | | 25 | 10 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 58 | | matches | | 0 | "been found" | | 1 | "was swept" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 173 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 14 | | totalSentences | 61 | | ratio | 0.23 | | matches | | 0 | "Harlow’s military precision falters—three days of tailing this clique courier have frayed her focus, the unresolved ghost of DS Morris’s death nipping at her heels like a rabid dog." | | 1 | "She rounds the corner into a narrow Soho alley she’s mapped a dozen times—its walls streaked with faded graffiti, lined with overflowing bin bags that reek of rotting mangoes and stale lager." | | 2 | "That’s no ordinary door—Harlow’s confidential intel links it to the abandoned Tube station beneath Camden, the current home of the Veil Market." | | 3 | "A bone token—she’s read witness statements of the market’s non-negotiable entry requirement, though she’s never seen one firsthand." | | 4 | "The door slams shut behind him, leaving a faint, thrumming hum to hang in the damp air—like a beehive stirred from sleep." | | 5 | "A tiny, carved raven adorns its surface—she recognizes the symbol from The Raven’s Nest, the dimly lit Soho bar she’d staked out last month." | | 6 | "She’d watched through a cracked window as Tomás Herrera, the former paramedic, patched up a clique member in the bar’s hidden back room—accessible only through a sliding bookshelf stacked with dusty leather tomes." | | 7 | "She’s encountered him twice before—once at The Raven’s Nest, once outside a Camden flat where a clique member had been found with unexplained burn marks." | | 8 | "Enchanted goods that twist minds, banned alchemical substances that melt flesh—things that could’ve killed my partner.”" | | 9 | "The market’s leaders don’t tolerate snitches—they feed them to the stone hounds guarding the main hall.”" | | 10 | "They’ll smell the fake entry—their guards have scaled noses that pick up fear and foreign scents." | | 11 | "She turns back to the metal door, her eyes scanning its surface for a hidden indentation to place the token. She’d watched the courier press the token to a small, circular mark just above the handle—she spots it now, a faint groove hidden by ivy. She brushes the ivy aside, presses the bone token to the mark." | | 12 | "The door rumbles, its metal hinges groaning in protest. It swings open a crack, releasing a wave of cold air that makes her elbow throb. The scent of burnt herbs and iron hits her nostrils, a sharp contrast to the alley’s rot. Inside, she hears the murmur of voices, the clink of glass vials, the scratch of claws on stone—sounds that make her skin prickle." | | 13 | "She steps over the threshold, the door slamming shut behind her before she can glance back. The darkness swallows her, the murmur of voices cutting off abruptly. Then, a shadow looms over her—tall, scaled, with glowing red eyes that pierce the dark. A stone hound, Tomás had called it. It sniffs the air, its tongue flicking out to taste the blood on her elbow." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 818 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.017114914425427872 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007334963325183374 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 61 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 61 | | mean | 19.38 | | std | 12.79 | | cv | 0.66 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 29 | | 3 | 35 | | 4 | 14 | | 5 | 32 | | 6 | 24 | | 7 | 22 | | 8 | 27 | | 9 | 19 | | 10 | 18 | | 11 | 15 | | 12 | 23 | | 13 | 22 | | 14 | 15 | | 15 | 17 | | 16 | 24 | | 17 | 27 | | 18 | 33 | | 19 | 27 | | 20 | 11 | | 21 | 4 | | 22 | 12 | | 23 | 29 | | 24 | 31 | | 25 | 16 | | 26 | 25 | | 27 | 10 | | 28 | 17 | | 29 | 23 | | 30 | 23 | | 31 | 21 | | 32 | 3 | | 33 | 16 | | 34 | 26 | | 35 | 3 | | 36 | 33 | | 37 | 7 | | 38 | 6 | | 39 | 10 | | 40 | 7 | | 41 | 16 | | 42 | 2 | | 43 | 15 | | 44 | 15 | | 45 | 9 | | 46 | 23 | | 47 | 14 | | 48 | 12 | | 49 | 16 |
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| 50.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3442622950819672 | | totalSentences | 61 | | uniqueOpeners | 21 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 51.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 57 | | matches | | 0 | "She yanks a crumpled linen" | | 1 | "She rounds the corner into" | | 2 | "She’d spent two weeks hacking" | | 3 | "She skids to a halt" | | 4 | "He curses in a guttural" | | 5 | "It’s smooth, worn thin from" | | 6 | "She’d watched through a cracked" | | 7 | "She’d been unable to get" | | 8 | "His olive skin glistens with" | | 9 | "His boots squelch in a" | | 10 | "She’s encountered him twice before—once" | | 11 | "She’s know what’s in that" | | 12 | "His death was swept under" | | 13 | "You know the darkness they" | | 14 | "They’ll skin me alive if" | | 15 | "They’ll tear you apart before" | | 16 | "They’ll smell the fake entry—their" | | 17 | "You’re a cop." | | 18 | "They’ll taste your badge on" | | 19 | "I fear letting Morris’s killer" |
| | ratio | 0.421 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 57 | | matches | | 0 | "Harlow slams into a brick" | | 1 | "The man bolts around the" | | 2 | "Harlow’s military precision falters—three days" | | 3 | "She yanks a crumpled linen" | | 4 | "The handkerchief soaks through fast," | | 5 | "She rounds the corner into" | | 6 | "The suspect’s scuffed leather shoes" | | 7 | "That’s no ordinary door—Harlow’s confidential" | | 8 | "She’d spent two weeks hacking" | | 9 | "She skids to a halt" | | 10 | "A bone token—she’s read witness" | | 11 | "The man’s fingers slip (rain" | | 12 | "He curses in a guttural" | | 13 | "The door slams shut behind" | | 14 | "Harlow kneels, her knees scraping" | | 15 | "It’s smooth, worn thin from" | | 16 | "A tiny, carved raven adorns" | | 17 | "The bar’s distinctive green neon" | | 18 | "She’d watched through a cracked" | | 19 | "She’d been unable to get" |
| | ratio | 0.947 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow’s military precision falters—three days of tailing this clique courier have frayed her focus, the unresolved ghost of DS Morris’s death nipping at her he…" | | 1 | "She’d been unable to get inside without a warrant, the secret room’s door clicking shut before she could catch a clear look at what they’d been hiding." | | 2 | "The door rumbles, its metal hinges groaning in protest. It swings open a crack, releasing a wave of cold air that makes her elbow throb. The scent of burnt herb…" | | 3 | "She steps over the threshold, the door slamming shut behind her before she can glance back. The darkness swallows her, the murmur of voices cutting off abruptly…" |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "brown eyes narrow, his voice steady despite the rain dripping off his chin" | | 1 | "Harlow steps, her military bearing making the narrow alley feel smaller" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0.333 | | effectiveRatio | 0.087 | |