| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1472 | | 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) | |
| 42.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1472 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "measured" | | 2 | "facade" | | 3 | "vibrated" | | 4 | "footsteps" | | 5 | "constructed" | | 6 | "cacophony" | | 7 | "scanned" | | 8 | "methodical" | | 9 | "scanning" | | 10 | "stomach" | | 11 | "standard" | | 12 | "absolutely" | | 13 | "mechanical" | | 14 | "oppressive" | | 15 | "silence" | | 16 | "ensuring" |
| |
| 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 | 103 | | matches | (empty) | |
| 87.38% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1472 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.26% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1472 | | uniqueNames | 23 | | maxNameDensity | 1.15 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 17 | | Quinn | 2 | | Metropolitan | 1 | | Police | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tube | 2 | | London | 2 | | Underground | 1 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Herrera | 4 | | Seville | 1 | | Morris | 2 | | Glock | 1 | | Detective | 2 | | Eighteen | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Nest" | | 4 | "Market" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" | | 8 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Metropolitan" | | 4 | "Soho" | | 5 | "Raven" | | 6 | "London" | | 7 | "Veil" | | 8 | "Seville" |
| | globalScore | 0.923 | | windowScore | 1 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like copper on the back of her ton" | | 1 | "glow that seemed to emanate from nowhere and everywhere at once" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1472 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 59.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 70.1 | | std | 24.99 | | cv | 0.357 | | sampleLengths | | 0 | 90 | | 1 | 112 | | 2 | 65 | | 3 | 73 | | 4 | 72 | | 5 | 63 | | 6 | 84 | | 7 | 101 | | 8 | 53 | | 9 | 89 | | 10 | 58 | | 11 | 80 | | 12 | 2 | | 13 | 105 | | 14 | 49 | | 15 | 93 | | 16 | 70 | | 17 | 52 | | 18 | 72 | | 19 | 31 | | 20 | 58 |
| |
| 98.45% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 103 | | matches | | 0 | "was rumored" | | 1 | "was bathed" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 11 | | totalVerbs | 238 | | matches | | 0 | "was closing" | | 1 | "was standing" | | 2 | "was handing" | | 3 | "was trying" | | 4 | "were blocking" | | 5 | "was standing" | | 6 | "was tracking" | | 7 | "were finishing" | | 8 | "were fighting" | | 9 | "was treating" | | 10 | "was keeping" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 103 | | ratio | 0.01 | | matches | | 0 | "The suspects she was tracking were not just thieves and thugs; they dealt in forces that could snap a human spine without breaking a sweat." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1489 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 33 | | adverbRatio | 0.02216252518468771 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.01208865010073875 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 14.29 | | std | 7.39 | | cv | 0.517 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 27 | | 3 | 4 | | 4 | 22 | | 5 | 20 | | 6 | 15 | | 7 | 34 | | 8 | 29 | | 9 | 14 | | 10 | 10 | | 11 | 4 | | 12 | 15 | | 13 | 16 | | 14 | 12 | | 15 | 5 | | 16 | 2 | | 17 | 1 | | 18 | 20 | | 19 | 13 | | 20 | 21 | | 21 | 5 | | 22 | 14 | | 23 | 8 | | 24 | 8 | | 25 | 6 | | 26 | 13 | | 27 | 10 | | 28 | 11 | | 29 | 16 | | 30 | 12 | | 31 | 18 | | 32 | 12 | | 33 | 21 | | 34 | 13 | | 35 | 13 | | 36 | 6 | | 37 | 24 | | 38 | 23 | | 39 | 5 | | 40 | 31 | | 41 | 17 | | 42 | 10 | | 43 | 7 | | 44 | 14 | | 45 | 22 | | 46 | 4 | | 47 | 21 | | 48 | 11 | | 49 | 17 |
| |
| 41.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.30097087378640774 | | totalSentences | 103 | | uniqueOpeners | 31 | |
| 33.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 100 | | matches | | 0 | "Instead, he reached into his" |
| | ratio | 0.01 | |
| 48.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 100 | | matches | | 0 | "She ignored the ache." | | 1 | "She had watched him slip" | | 2 | "His face had been briefly" | | 3 | "She lengthened her stride, her" | | 4 | "Her sharp jaw was set" | | 5 | "She was closing the gap." | | 6 | "He sprinted toward a heavy," | | 7 | "She watched as the man" | | 8 | "He did not try the" | | 9 | "He pressed it against the" | | 10 | "She jammed her shoulder against" | | 11 | "She squeezed through the narrow" | | 12 | "She stood in the suffocating" | | 13 | "She drew her flashlight, keeping" | | 14 | "It was heavy, thick with" | | 15 | "She was standing at the" | | 16 | "They said it sold enchanted" | | 17 | "They also said it required" | | 18 | "She looked down at the" | | 19 | "She picked it up." |
| | ratio | 0.43 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 100 | | matches | | 0 | "Detective Harlow Quinn pumped her" | | 1 | "She ignored the ache." | | 2 | "The suspect was fast, a" | | 3 | "Harlow had tailed him for" | | 4 | "She had watched him slip" | | 5 | "His face had been briefly" | | 6 | "Harlow checked the worn leather" | | 7 | "The streets were largely deserted," | | 8 | "She lengthened her stride, her" | | 9 | "Her sharp jaw was set" | | 10 | "She was closing the gap." | | 11 | "The suspect took a hard" | | 12 | "Harlow followed, her hand instinctively" | | 13 | "The alley opened into a" | | 14 | "The suspect did not hesitate." | | 15 | "He sprinted toward a heavy," | | 16 | "Harlow pushed her burning lungs" | | 17 | "She watched as the man" | | 18 | "He did not try the" | | 19 | "He pressed it against the" |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 100 | | matches | | 0 | "Now, the chase had dragged" | | 1 | "Now, he was the off-the-books" | | 2 | "If she stepped out from" |
| | ratio | 0.03 | |
| 38.10% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 11 | | matches | | 0 | "She had watched him slip out from the alley beside the Raven's Nest, emerging from the direction of the bar's hidden back room that was rumored to be accessible…" | | 1 | "The suspect took a hard right down a narrow, unlit alleyway that smelled of rotting vegetable matter and stale urine." | | 2 | "Harlow had heard the whispers in the squad room, the hushed, half-drunk confessions of veteran officers who claimed there was an underground black market that m…" | | 3 | "They said it sold enchanted goods, banned alchemical substances, and information that could get a person killed." | | 4 | "It was warm to the touch, vibrating with a faint, unnatural energy that made the hairs on her arms stand up." | | 5 | "Born in Seville, a former paramedic for the NHS who had lost his medical license after administering unauthorized treatments to supernatural patients." | | 6 | "He looked up, scanning the crowd with warm brown eyes that betrayed none of the paranoia she expected from a black-market buyer." | | 7 | "Three years ago, she had stood in a similarly dark, damp alleyway, waiting for backup that had arrived too late." | | 8 | "Her partner, DS Morris, had bled out on the pavement, his chest torn open by something that defied forensic logic." | | 9 | "The supernatural origins of his death remained a closed, heavily redacted file, a ghost that haunted her every waking hour." | | 10 | "If she stepped out from behind the pillar, she would be walking into a den of monsters, armed with nothing but a standard-issue Glock and a badge that meant abs…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |