| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1119 | | 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) | |
| 37.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1119 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | word | "down her spine" | | count | 1 |
| | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | |
| | highlights | | 0 | "tracing" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "reminder" | | 4 | "down her spine" | | 5 | "firmly" | | 6 | "rhythmic" | | 7 | "echoed" | | 8 | "flicker" | | 9 | "velvet" | | 10 | "silk" | | 11 | "pulse" | | 12 | "electric" |
| |
| 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 | 71 | | matches | (empty) | |
| 42.25% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "try to" | | 2 | "start to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1099 | | uniqueNames | 16 | | maxNameDensity | 0.73 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | London | 2 | | Soho | 1 | | Harlow | 1 | | Quinn | 5 | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 8 | | Morris | 3 | | Camden | 1 | | Veil | 1 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Detective | 2 | | You | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Market" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "You" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 3 | | matches | | 0 | "sounded like dry leaves skittering on pave" | | 1 | "felt like a vacuum, pulling at her, dra" | | 2 | "felt like walking into a wall of needle" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1112 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 71.47% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 65.41 | | std | 26.17 | | cv | 0.4 | | sampleLengths | | 0 | 59 | | 1 | 121 | | 2 | 112 | | 3 | 36 | | 4 | 64 | | 5 | 63 | | 6 | 88 | | 7 | 50 | | 8 | 60 | | 9 | 74 | | 10 | 13 | | 11 | 46 | | 12 | 90 | | 13 | 81 | | 14 | 47 | | 15 | 59 | | 16 | 49 |
| |
| 80.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 71 | | matches | | 0 | "was torn" | | 1 | "was plastered" | | 2 | "was rusted" | | 3 | "was filled" | | 4 | "was gone" |
| |
| 46.74% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 174 | | matches | | 0 | "were breathing" | | 1 | "was retreating" | | 2 | "wasn't running" | | 3 | "was leaving" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 3 | | flaggedSentences | 9 | | totalSentences | 71 | | ratio | 0.127 | | matches | | 0 | "The rain in London did not wash the city clean; it merely condensed the filth into a slick, shimmering film that caught the neon glare of Soho." | | 1 | "He was an easy mark in the crowd—a man who walked with the tentative hunch of someone who knew the city’s shadows better than its streets." | | 2 | "She felt the ghost of a weight on her belt, a reminder of the authority she carried—and the crushing helplessness she had felt when DS Morris was torn from her life three years ago." | | 3 | "The official report said an accident, a structural failure, but the memory of the cold air that night—the scent of ozone and something rotten—kept her awake long after the streetlights dimmed." | | 4 | "It wasn't just the smell of ozone; it was the heavy, metallic tang of copper and old wet earth." | | 5 | "She heard the faint, rhythmic scraping of metal against stone—a sound that echoed too deeply, as if the station itself were breathing." | | 6 | "He reached into his pocket and pulled out a small, jagged object—a sliver of white that looked far too much like human bone." | | 7 | "She saw the market—stalls draped in velvet and silk, selling vials that glowed with sickly violet light and artifacts that shifted shape when she looked directly at them." | | 8 | "He wasn't running anymore; he was retreating into his own world." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 116 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.008620689655172414 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.008620689655172414 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 15.66 | | std | 7.99 | | cv | 0.51 | | sampleLengths | | 0 | 27 | | 1 | 21 | | 2 | 10 | | 3 | 1 | | 4 | 25 | | 5 | 26 | | 6 | 18 | | 7 | 25 | | 8 | 27 | | 9 | 15 | | 10 | 9 | | 11 | 23 | | 12 | 34 | | 13 | 31 | | 14 | 16 | | 15 | 7 | | 16 | 13 | | 17 | 6 | | 18 | 17 | | 19 | 17 | | 20 | 3 | | 21 | 21 | | 22 | 4 | | 23 | 19 | | 24 | 18 | | 25 | 22 | | 26 | 13 | | 27 | 11 | | 28 | 18 | | 29 | 23 | | 30 | 23 | | 31 | 8 | | 32 | 25 | | 33 | 17 | | 34 | 17 | | 35 | 9 | | 36 | 3 | | 37 | 15 | | 38 | 16 | | 39 | 17 | | 40 | 12 | | 41 | 28 | | 42 | 17 | | 43 | 13 | | 44 | 15 | | 45 | 5 | | 46 | 7 | | 47 | 12 | | 48 | 7 | | 49 | 19 |
| |
| 31.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.28169014084507044 | | totalSentences | 71 | | uniqueOpeners | 20 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Instead, he ducked behind a" |
| | ratio | 0.014 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 70 | | matches | | 0 | "She checked the worn leather" | | 1 | "She had been tailing Tomás" | | 2 | "He was an easy mark" | | 3 | "She had spent months tracking" | | 4 | "She felt the ghost of" | | 5 | "He was moving faster now," | | 6 | "He didn't head toward the" | | 7 | "Her salt-and-pepper hair was plastered" | | 8 | "She reached the fence just" | | 9 | "She didn't hesitate." | | 10 | "She scrambled over the chain-link," | | 11 | "It wasn't just the smell" | | 12 | "She moved down the stairs," | | 13 | "She heard the faint, rhythmic" | | 14 | "She rounded a pillar covered" | | 15 | "It was rusted, ornate, and" | | 16 | "He reached into his pocket" | | 17 | "He pressed it into a" | | 18 | "It was filled with the" | | 19 | "He looked back once, his" |
| | ratio | 0.571 | |
| 24.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 70 | | matches | | 0 | "The rain in London did" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "She checked the worn leather" | | 3 | "She had been tailing Tomás" | | 4 | "He was an easy mark" | | 5 | "Herrera had slipped out of" | | 6 | "She had spent months tracking" | | 7 | "The NHS reports had been" | | 8 | "Herrera turned down a narrow" | | 9 | "Quinn followed, her footsteps silent" | | 10 | "She felt the ghost of" | | 11 | "The official report said an" | | 12 | "He was moving faster now," | | 13 | "He didn't head toward the" | | 14 | "Quinn broke into a controlled" | | 15 | "Her salt-and-pepper hair was plastered" | | 16 | "She reached the fence just" | | 17 | "She didn't hesitate." | | 18 | "She scrambled over the chain-link," | | 19 | "The air changed here." |
| | ratio | 0.871 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 70 | | matches | | 0 | "If she turned back, she" |
| | ratio | 0.014 | |
| 16.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 9 | | matches | | 0 | "He was an easy mark in the crowd—a man who walked with the tentative hunch of someone who knew the city’s shadows better than its streets." | | 1 | "She had spent months tracking the periphery of his life, tracing the oddities of his medical history back to the day he lost his license." | | 2 | "The NHS reports had been clinical, sanitized, but they didn’t explain the unauthorized treatments performed on patients who, according to the official coroner’s…" | | 3 | "When he paused, checking his shoulder, she pressed herself into the recess of a brick wall, her hand hovering instinctively near her holster." | | 4 | "She heard the faint, rhythmic scraping of metal against stone—a sound that echoed too deeply, as if the station itself were breathing." | | 5 | "It was rusted, ornate, and hummed with a low-frequency vibration that made the fillings in her teeth ache." | | 6 | "It was total, beautiful anarchy, a cesspool of things that had no place in the daylight world." | | 7 | "She had spent three years obsessed with the truth behind Morris’s death, haunting dusty archives and digging up cold cases that hinted at these hidden spaces." | | 8 | "The transition felt like walking into a wall of needles, a sharp, electric bite that shivered through her skin." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |