| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.75 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1277 | | 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) | |
| 80.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1277 | | totalAiIsms | 5 | | found | | 0 | | | 1 | | word | "down her spine" | | count | 1 |
| | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "tension" | | 1 | "down her spine" | | 2 | "stomach" | | 3 | "streaming" | | 4 | "glint" |
| |
| 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 | 1 | | narrationSentences | 88 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1294 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1285 | | uniqueNames | 12 | | maxNameDensity | 0.86 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Herrera | 9 | | Shaftesbury | 1 | | Avenue | 1 | | Camden | 1 | | Tube | 1 | | Morris | 4 | | Barking | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Camden" | | 4 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "Barking" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1294 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 38.06 | | std | 29.96 | | cv | 0.787 | | sampleLengths | | 0 | 17 | | 1 | 59 | | 2 | 79 | | 3 | 7 | | 4 | 78 | | 5 | 10 | | 6 | 3 | | 7 | 68 | | 8 | 5 | | 9 | 7 | | 10 | 84 | | 11 | 73 | | 12 | 10 | | 13 | 50 | | 14 | 5 | | 15 | 51 | | 16 | 15 | | 17 | 74 | | 18 | 34 | | 19 | 31 | | 20 | 32 | | 21 | 4 | | 22 | 14 | | 23 | 65 | | 24 | 10 | | 25 | 12 | | 26 | 99 | | 27 | 32 | | 28 | 15 | | 29 | 53 | | 30 | 83 | | 31 | 73 | | 32 | 2 | | 33 | 40 |
| |
| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "been stabbed" | | 1 | "been placed" | | 2 | "been taught" |
| |
| 11.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 212 | | matches | | 0 | "was trying" | | 1 | "wasn't carrying" | | 2 | "was walking" | | 3 | "was flagging " | | 4 | "was counting" | | 5 | "was selling" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 0 | | flaggedSentences | 14 | | totalSentences | 89 | | ratio | 0.157 | | matches | | 0 | "She'd made him two days ago — the former paramedic, the disgraced NHS man who'd lost his license for reasons buried under sealed disciplinary records." | | 1 | "Eighteen years on the job had taught her the rhythm of a tail — match the target's pace, never his stops, let the crowd be your wall." | | 2 | "It wasn't a build-up, no tension in the shoulders first — one moment he was walking, the next he was a dark shape sprinting through the downpour, satchel swinging." | | 3 | "He cut left into an alley, and Quinn took the corner wide, expecting a swing or a blade — he'd been stabbed once, she knew that from his file, a scar along his left forearm, and men who'd been cut learned to cut first." | | 4 | "Herrera was thirty meters ahead now, but he was flagging — she could see it in the hitch of his stride, the way the satchel dragged at his shoulder." | | 5 | "She reached for her radio and got static — a thick, wrong-sounding static, almost textured, almost like whispering." | | 6 | "They'd found him four hours later with his eyes open and no mark on him, and the coroner had used the phrase \"inconclusive\" eleven times in a single report, and nobody — nobody — had ever given her a straight answer about what killed a healthy thirty-four-year-old man mid-sentence." | | 7 | "And there was a smell — copper and candle wax and something sweetly rotten, like fruit gone soft in the sun." | | 8 | "Herrera was going to disappear into whatever this was, and she'd spend another three weeks — three months — watching a green neon sign and learning nothing." | | 9 | "Light came from somewhere ahead — warm, flickering, alive." | | 10 | "People moved between them — people, she told herself, though a man at the nearest stall had eyes like a cat's caught in headlights, and a woman haggling over a jar of cloudy liquid had too many fingers on the hand she was counting coins with." | | 11 | "Herrera was ahead of her — she caught a flash of his olive skin, the satchel, the glint of a medallion at his throat as he pushed through the crowd, thirty meters in and moving fast." | | 12 | "The sensible part of her — the part with the sharp jaw and the military bearing and the eighteen years of decorated service — told her to memorize everything and walk away." | | 13 | "Quinn squared her shoulders, stepped down onto the platform, and went in after Herrera — into the lantern light, into the crowd, into the dark heart of a city she'd policed her whole life without ever once seeing it true." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1278 | | adjectiveStacks | 1 | | stackExamples | | 0 | "under sealed disciplinary records." |
| | adverbCount | 44 | | adverbRatio | 0.03442879499217527 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004694835680751174 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 14.54 | | std | 12.25 | | cv | 0.842 | | sampleLengths | | 0 | 17 | | 1 | 24 | | 2 | 4 | | 3 | 20 | | 4 | 11 | | 5 | 21 | | 6 | 19 | | 7 | 25 | | 8 | 14 | | 9 | 7 | | 10 | 20 | | 11 | 27 | | 12 | 31 | | 13 | 5 | | 14 | 4 | | 15 | 1 | | 16 | 3 | | 17 | 29 | | 18 | 21 | | 19 | 8 | | 20 | 10 | | 21 | 3 | | 22 | 2 | | 23 | 4 | | 24 | 3 | | 25 | 44 | | 26 | 23 | | 27 | 3 | | 28 | 7 | | 29 | 3 | | 30 | 4 | | 31 | 29 | | 32 | 29 | | 33 | 15 | | 34 | 10 | | 35 | 41 | | 36 | 9 | | 37 | 5 | | 38 | 20 | | 39 | 18 | | 40 | 4 | | 41 | 2 | | 42 | 2 | | 43 | 5 | | 44 | 2 | | 45 | 13 | | 46 | 25 | | 47 | 49 | | 48 | 6 | | 49 | 2 |
| |
| 73.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.47191011235955055 | | totalSentences | 89 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 79 | | matches | | 0 | "Then he ran." | | 1 | "Maybe they had." | | 2 | "Maybe a hundred." | | 3 | "Then she saw a stall" |
| | ratio | 0.051 | |
| 83.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 79 | | matches | | 0 | "He glanced left, then right," | | 1 | "She'd made him two days" | | 2 | "He wasn't carrying a medical" | | 3 | "He checked his shoulder twice." | | 4 | "She'd made him nervous." | | 5 | "It wasn't a build-up, no" | | 6 | "Her lungs remembered the drill" | | 7 | "He didn't look back." | | 8 | "They never did." | | 9 | "He cut left into an" | | 10 | "He'd planned for this." | | 11 | "She went over the pallets" | | 12 | "she called out, breath steaming" | | 13 | "He darted right, through a" | | 14 | "She reached for her radio" | | 15 | "She tried her phone." | | 16 | "She thought of DS Morris" | | 17 | "They'd found him four hours" | | 18 | "She should call it in." | | 19 | "Her shadow stretched and shivered" |
| | ratio | 0.342 | |
| 80.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 79 | | matches | | 0 | "The rain came down on" | | 1 | "Detective Harlow Quinn stood across" | | 2 | "Tomás Herrera came out the" | | 3 | "He glanced left, then right," | | 4 | "She'd made him two days" | | 5 | "The one who patched up" | | 6 | "He wasn't carrying a medical" | | 7 | "Herrera set off north, and" | | 8 | "He checked his shoulder twice." | | 9 | "She'd made him nervous." | | 10 | "It wasn't a build-up, no" | | 11 | "Quinn swore and went after" | | 12 | "Her lungs remembered the drill" | | 13 | "He didn't look back." | | 14 | "They never did." | | 15 | "He cut left into an" | | 16 | "The thought slid cold down" | | 17 | "A prepared route." | | 18 | "He'd planned for this." | | 19 | "She went over the pallets" |
| | ratio | 0.759 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "She'd made him two days ago — the former paramedic, the disgraced NHS man who'd lost his license for reasons buried under sealed disciplinary records." | | 1 | "Old tiles lined the walls, cracked and grimed, but someone had strung wires along the ceiling, and lanterns hung from them at intervals, burning with a greenish…" | | 2 | "Then she saw a stall where a man was selling bone tokens from a brass bowl, and a woman buying one, and she understood somehow, with a detective's instinct that…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she called out (call out)" |
| | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |