| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1540 | | 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) | |
| 64.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1540 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "measured" | | 1 | "pounding" | | 2 | "familiar" | | 3 | "echo" | | 4 | "footsteps" | | 5 | "structure" | | 6 | "weight" | | 7 | "could feel" | | 8 | "beacon" |
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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 | 106 | | matches | (empty) | |
| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | 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 | 1548 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1495 | | uniqueNames | 17 | | maxNameDensity | 0.74 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 11 | | Quinn | 1 | | Soho | 1 | | Greek | 1 | | Street | 2 | | London | 2 | | Wardour | 1 | | Raven | 2 | | Nest | 4 | | Silas | 1 | | King | 1 | | Cross | 1 | | Victorian | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Silas" | | 5 | "King" | | 6 | "Cross" | | 7 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "London" | | 4 | "Wardour" |
| | globalScore | 1 | | windowScore | 1 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 4 | | matches | | 0 | "felt like a held breath" | | 1 | "something like ozone after a lightning strik" | | 2 | "felt like half a mile, branching twice" | | 3 | "thrum that seemed to come from the rock itself" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.646 | | wordCount | 1548 | | matches | | 0 | "not the panicked flailing of a common thief, but the measured sprint of someone who’d run from authorities be" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 97.79% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 45.53 | | std | 22.41 | | cv | 0.492 | | sampleLengths | | 0 | 57 | | 1 | 36 | | 2 | 65 | | 3 | 58 | | 4 | 49 | | 5 | 50 | | 6 | 99 | | 7 | 29 | | 8 | 65 | | 9 | 28 | | 10 | 11 | | 11 | 58 | | 12 | 52 | | 13 | 23 | | 14 | 63 | | 15 | 24 | | 16 | 45 | | 17 | 40 | | 18 | 72 | | 19 | 51 | | 20 | 48 | | 21 | 50 | | 22 | 3 | | 23 | 67 | | 24 | 9 | | 25 | 101 | | 26 | 58 | | 27 | 36 | | 28 | 29 | | 29 | 38 | | 30 | 26 | | 31 | 37 | | 32 | 12 | | 33 | 59 |
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| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "were tied" | | 1 | "been carved" | | 2 | "been transformed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 249 | | matches | | 0 | "was swinging" | | 1 | "was giving" | | 2 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 2 | | flaggedSentences | 10 | | totalSentences | 108 | | ratio | 0.093 | | matches | | 0 | "He moved with a strange economy — not the panicked flailing of a common thief, but the measured sprint of someone who’d run from authorities before." | | 1 | "Her service weapon stayed snapped in its holster; a crowded street, even one drowning in rain, was no place for a drawn firearm." | | 2 | "It wasn’t one of her regular spots — a detective in her position didn’t keep regular spots — but she’d been inside twice on community outreach assignments." | | 3 | "At the end, she caught a flash of movement — the suspect disappearing through what should have been a solid wall of bookshelves." | | 4 | "The radio crackled again — units were tied up on a barricade situation near King’s Cross." | | 5 | "Waiting wasn’t caution; it was giving him up." | | 6 | "His body found on a rain-slicked rooftop three years ago, case unsolved, file flagged with anomalies no one could explain—the absence of certain physical evidence, the impossible angles of entry." | | 7 | "It filled an abandoned Tube station — she recognized the curved walls of a platform, the arched ceiling, the ghostly outlines of old advertisements still clinging to the tiles." | | 8 | "Others made no pretense at all — figures in hooded robes, faces hidden, hands with too many fingers." | | 9 | "She kept the suspect’s waxed jacket in sight — a dull beacon among the strange and impossible commerce — and she followed him deeper into the dark." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1497 | | adjectiveStacks | 1 | | stackExamples | | 0 | "strange blue-white flame" |
| | adverbCount | 45 | | adverbRatio | 0.03006012024048096 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004676018704074816 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 14.33 | | std | 7.24 | | cv | 0.505 | | sampleLengths | | 0 | 13 | | 1 | 16 | | 2 | 28 | | 3 | 15 | | 4 | 13 | | 5 | 8 | | 6 | 19 | | 7 | 26 | | 8 | 20 | | 9 | 3 | | 10 | 18 | | 11 | 14 | | 12 | 23 | | 13 | 9 | | 14 | 14 | | 15 | 19 | | 16 | 7 | | 17 | 15 | | 18 | 15 | | 19 | 20 | | 20 | 19 | | 21 | 15 | | 22 | 5 | | 23 | 27 | | 24 | 14 | | 25 | 19 | | 26 | 15 | | 27 | 14 | | 28 | 25 | | 29 | 16 | | 30 | 24 | | 31 | 12 | | 32 | 16 | | 33 | 11 | | 34 | 19 | | 35 | 23 | | 36 | 16 | | 37 | 3 | | 38 | 19 | | 39 | 18 | | 40 | 12 | | 41 | 6 | | 42 | 17 | | 43 | 7 | | 44 | 33 | | 45 | 6 | | 46 | 5 | | 47 | 12 | | 48 | 16 | | 49 | 8 |
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| 50.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.35185185185185186 | | totalSentences | 108 | | uniqueOpeners | 38 | |
| 63.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 105 | | matches | | 0 | "Then she stepped onto the" | | 1 | "Then the tunnel opened up," |
| | ratio | 0.019 | |
| 94.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 105 | | matches | | 0 | "Her voice cut through the" | | 1 | "He veered left, knocking over" | | 2 | "She’d been tracking him for" | | 3 | "He moved with a strange" | | 4 | "She pumped her arms harder," | | 5 | "Her service weapon stayed snapped" | | 6 | "She knew the Raven’s Nest." | | 7 | "It wasn’t one of her" | | 8 | "She descended the stairs with" | | 9 | "He vaulted a table, scattering" | | 10 | "She hit the bookshelf with" | | 11 | "Her shoulder light swept the" | | 12 | "They curved down into blackness," | | 13 | "She keyed the radio on" | | 14 | "She paused, listening to the" | | 15 | "She looked down the spiral" | | 16 | "They had exits she couldn’t" | | 17 | "His body found on a" | | 18 | "She’d learned then that London" | | 19 | "She’d learned that no one" |
| | ratio | 0.314 | |
| 36.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 105 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "The suspect was twenty meters" | | 3 | "Her voice cut through the" | | 4 | "He veered left, knocking over" | | 5 | "Cabbages rolled into the gutter" | | 6 | "She’d been tracking him for" | | 7 | "He moved with a strange" | | 8 | "Harlow’s lungs burned." | | 9 | "She pumped her arms harder," | | 10 | "Her service weapon stayed snapped" | | 11 | "The suspect cut across Wardour" | | 12 | "A black cab swerved, horn" | | 13 | "Harlow followed without hesitation, one" | | 14 | "The driver shouted something she" | | 15 | "The bar’s entrance was set" | | 16 | "The suspect took the steps" | | 17 | "Harlow reached the top of" | | 18 | "Water streamed from the brim" | | 19 | "She knew the Raven’s Nest." |
| | ratio | 0.848 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 72 | | technicalSentenceCount | 9 | | matches | | 0 | "The suspect was twenty meters ahead, a lean figure in a waxed jacket that slicked the neon glow of Soho’s signs across his back like oil on water." | | 1 | "He moved with a strange economy — not the panicked flailing of a common thief, but the measured sprint of someone who’d run from authorities before." | | 2 | "A handful of patrons looked up from their glasses, then looked away with the practiced disinterest of people who’d learned not to notice things." | | 3 | "The air that rushed up carried a different scent, damp and mineral, like the tunnels beneath the city." | | 4 | "They had exits she couldn’t see, passages that didn’t exist on any city survey." | | 5 | "A string of bare bulbs ran along the ceiling, their glow faint and yellow, swinging slightly as if something had passed by." | | 6 | "Stalls built of salvaged wood and corrugated metal lined both sides of the platform, lit by lanterns of a strange blue-white flame that guttered without smoke." | | 7 | "Merchants hawked goods she didn’t recognize: jars of swirling liquid, racks of bones inscribed with symbols, cages covered in black cloth that rustled even when…" | | 8 | "The crowd didn’t part, but it shifted, accommodating her passage with an ease that felt somehow more dangerous than resistance." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |