| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1592 | | 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) | |
| 90.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1592 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 129 | | matches | (empty) | |
| 98.56% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1572 | | ratio | 0.001 | | matches | | |
| 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 | 64 | | wordCount | 1514 | | uniqueNames | 25 | | maxNameDensity | 0.66 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Nest" | | discoveredNames | | London | 6 | | Harlow | 1 | | Quinn | 10 | | Camden | 3 | | High | 1 | | Street | 1 | | Tomás | 3 | | Herrera | 4 | | Seville | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 4 | | Silas | 6 | | Tube | 2 | | Victorian | 1 | | Glock | 2 | | Met | 1 | | Tuesday | 1 | | God | 1 | | Limehouse | 1 | | Raven | 2 | | Nest | 4 | | Soho | 3 | | Empire | 1 | | Twenty | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Silas" | | 8 | "God" | | 9 | "Raven" | | 10 | "Nest" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Seville" | | 5 | "Limehouse" | | 6 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 86.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it had been cut recently" | | 1 | "something like it once before, at a crime sc" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1572 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 42.49 | | std | 34.95 | | cv | 0.823 | | sampleLengths | | 0 | 8 | | 1 | 58 | | 2 | 37 | | 3 | 104 | | 4 | 59 | | 5 | 76 | | 6 | 7 | | 7 | 63 | | 8 | 63 | | 9 | 40 | | 10 | 65 | | 11 | 15 | | 12 | 33 | | 13 | 22 | | 14 | 45 | | 15 | 29 | | 16 | 19 | | 17 | 6 | | 18 | 124 | | 19 | 120 | | 20 | 22 | | 21 | 62 | | 22 | 68 | | 23 | 21 | | 24 | 7 | | 25 | 125 | | 26 | 58 | | 27 | 7 | | 28 | 48 | | 29 | 3 | | 30 | 61 | | 31 | 3 | | 32 | 25 | | 33 | 2 | | 34 | 57 | | 35 | 8 | | 36 | 2 |
| |
| 91.66% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 129 | | matches | | 0 | "was pulled" | | 1 | "been found" | | 2 | "been carved" | | 3 | "was populated" | | 4 | "were compromised" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 240 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 131 | | ratio | 0.122 | | matches | | 0 | "Her jacket—standard issue, wholly inadequate for this weather—had given up any pretense of keeping her dry three blocks ago." | | 1 | "The case had started as a routine missing persons report—an NHS paramedic named Tomás Herrera, twenty-nine years old, olive skin, curly dark hair, scar on his left forearm from a knife attack." | | 2 | "Maybe it was the Saint Christopher medallion in the evidence photo—worn, personal, the kind of thing a man didn't leave behind voluntarily." | | 3 | "The suspect—he called himself Silas, according to the witnesses she'd leaned on—vaulted a low railing and disappeared down a side street that dead-ended against a row of shuttered shops." | | 4 | "The smell hit her first—bins and standing water and something else underneath, something chemical and old, like antiseptic layered over rust." | | 5 | "The kind that led down to the abandoned infrastructure beneath Camden—the old Tube tunnels, the Victorian sewers, the parts of London that the city had built over and forgotten." | | 6 | "She drew her weapon—a Glock 17, standard Met issue—and held it low, her breathing controlled." | | 7 | "\"—urban search team on standby.\"" | | 8 | "Whatever operation was running down there—whatever had swallowed up Tomás Herrera and God knew how many others—would fold up and scatter like roaches when the light hit." | | 9 | "The air changed as she descended—warmer, thicker, laced with that chemical smell she'd caught in the alley, now joined by incense and sweat and the unmistakable reek of alchemical runoff." | | 10 | "This one was different—newer, heavier, reinforced steel with a deadbolt that required a specific kind of key." | | 11 | "She'd read about establishments like this—whispered about in the intelligence briefings that circulated among certain squads, never quite making it to official channels." | | 12 | "The murmur was louder now—voices, footsteps, the clink of glass, a sound that might have been chanting or might have been music played on instruments she'd never encountered." | | 13 | "She'd been there twice during this investigation—once to interview the owner, once to watch the comings and goings from a parked car across the street." | | 14 | "And she'd heard the whispers about the Nest's back room—a hidden space behind a bookshelf, used for the kind of meetings that didn't appear on any official calendar." | | 15 | "Water dripped somewhere behind the walls—old London, ancient London, the London that had been here long before the police or the Tube or the Empire." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 639 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.028169014084507043 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010954616588419406 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 12 | | std | 9.45 | | cv | 0.788 | | sampleLengths | | 0 | 8 | | 1 | 30 | | 2 | 6 | | 3 | 3 | | 4 | 19 | | 5 | 20 | | 6 | 3 | | 7 | 14 | | 8 | 7 | | 9 | 28 | | 10 | 32 | | 11 | 3 | | 12 | 6 | | 13 | 21 | | 14 | 7 | | 15 | 5 | | 16 | 4 | | 17 | 22 | | 18 | 12 | | 19 | 16 | | 20 | 10 | | 21 | 29 | | 22 | 5 | | 23 | 32 | | 24 | 3 | | 25 | 4 | | 26 | 12 | | 27 | 21 | | 28 | 12 | | 29 | 18 | | 30 | 7 | | 31 | 6 | | 32 | 29 | | 33 | 21 | | 34 | 2 | | 35 | 12 | | 36 | 15 | | 37 | 4 | | 38 | 7 | | 39 | 5 | | 40 | 5 | | 41 | 22 | | 42 | 5 | | 43 | 1 | | 44 | 27 | | 45 | 3 | | 46 | 12 | | 47 | 28 | | 48 | 5 | | 49 | 10 |
| |
| 46.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.33587786259541985 | | totalSentences | 131 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 118 | | matches | | 0 | "Then his license was pulled" | | 1 | "Maybe it was the Saint" | | 2 | "Maybe it was the unexplained" | | 3 | "Then she thought about the" | | 4 | "Then she waited." | | 5 | "Then she turned and climbed" |
| | ratio | 0.051 | |
| 87.80% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 118 | | matches | | 0 | "Her lungs burned." | | 1 | "Her jacket—standard issue, wholly inadequate" | | 2 | "He was fast." | | 3 | "She'd been tailing him for" | | 4 | "She couldn't explain why." | | 5 | "She rounded the corner and" | | 6 | "She cleared the railing without" | | 7 | "He ducked into an alley" | | 8 | "She turned sideways and pushed" | | 9 | "She drew her weapon—a Glock" | | 10 | "She was sure of it." | | 11 | "She'd seen the wet footprints" | | 12 | "Her radio crackled." | | 13 | "She pulled it from her" | | 14 | "She'd seen it before." | | 15 | "She'd waited for backup." | | 16 | "she said, her voice flat" | | 17 | "She didn't wait for a" | | 18 | "She'd smelled something like it" | | 19 | "She'd read about establishments like" |
| | ratio | 0.331 | |
| 91.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 118 | | matches | | 0 | "Detective Harlow Quinn sprinted through" | | 1 | "Water streamed down her sharp" | | 2 | "Her lungs burned." | | 3 | "Her jacket—standard issue, wholly inadequate" | | 4 | "He was fast." | | 5 | "She'd been tailing him for" | | 6 | "The case had started as" | | 7 | "She couldn't explain why." | | 8 | "The same kind of unexplained" | | 9 | "She rounded the corner and" | | 10 | "The suspect—he called himself Silas," | | 11 | "Quinn's boots slapped wet pavement." | | 12 | "She cleared the railing without" | | 13 | "Silas was fast." | | 14 | "He ducked into an alley" | | 15 | "The smell hit her first—bins" | | 16 | "The alley narrowed to a" | | 17 | "She turned sideways and pushed" | | 18 | "The alley opened into a" | | 19 | "An old service entrance, she" |
| | ratio | 0.737 | |
| 42.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 118 | | matches | | 0 | "Or, Quinn noticed as she" |
| | ratio | 0.008 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 13 | | matches | | 0 | "Maybe it was the unexplained circumstances that surrounded every lead she followed." | | 1 | "The same kind of unexplained circumstances that had taken DS Morris from her three years ago." | | 2 | "She cleared the railing without breaking stride, one hand catching the cold metal, her body swinging over with military precision that eighteen years of the job…" | | 3 | "The kind that led down to the abandoned infrastructure beneath Camden—the old Tube tunnels, the Victorian sewers, the parts of London that the city had built ov…" | | 4 | "A murmur of voices, a hum of activity that had no business existing beneath a dead-end alley in Camden at half past eleven on a Tuesday night." | | 5 | "The fluorescent tubes that lined the walls were dead, but someone had strung bare bulbs along the ceiling at intervals, casting pools of sick yellow light that …" | | 6 | "She'd read about establishments like this—whispered about in the intelligence briefings that circulated among certain squads, never quite making it to official …" | | 7 | "The murmur was louder now—voices, footsteps, the clink of glass, a sound that might have been chanting or might have been music played on instruments she'd neve…" | | 8 | "She'd sensed something off about it, the same way she sensed something off about this door, this staircase, this entire underground world that existed beneath t…" | | 9 | "And she'd heard the whispers about the Nest's back room—a hidden space behind a bookshelf, used for the kind of meetings that didn't appear on any official cale…" | | 10 | "She'd been building a map of connections in her head, a web that linked Herrera to Silas to the Nest to whatever lay beyond this door." | | 11 | "Water dripped somewhere behind the walls—old London, ancient London, the London that had been here long before the police or the Tube or the Empire." | | 12 | "And two words that made the skin on the back of her neck prickle." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice flat" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |