| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1205 | | 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) | |
| 46.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1205 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "synthetic" | | 1 | "stark" | | 2 | "gloom" | | 3 | "footsteps" | | 4 | "measured" | | 5 | "scanning" | | 6 | "etched" | | 7 | "standard" | | 8 | "pristine" | | 9 | "perfect" | | 10 | "intricate" | | 11 | "pulse" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 105 | | matches | | 0 | "g in surprise" | | 1 | "d in panic" |
| |
| 74.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1201 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 7 | | matches | | 0 | "Nasty business, Detective, Miller said, shaking his head." | | 1 | "Look at the walls, Miller, Quinn said, her voice a low, steady rumble." | | 2 | "Leave it, Quinn snapped." | | 3 | "Blue, he muttered." | | 4 | "And look at the floor, Quinn continued, gesturing with her chin to the dry, dusty concrete surrounding the body." | | 5 | "No, Miller, she said, her voice cutting through the distant rumble of the Northern Line." | | 6 | "They do, Quinn muttered, clicking her pen." |
| |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1200 | | uniqueNames | 15 | | maxNameDensity | 1.5 | | worstName | "Miller" | | maxWindowNameDensity | 3 | | worstWindowName | "Miller" | | discoveredNames | | Tube | 1 | | Harlow | 2 | | Quinn | 13 | | Metropolitan | 1 | | Police | 1 | | Camden | 1 | | Northern | 2 | | Line | 2 | | Miller | 18 | | Detective | 6 | | Graffiti | 1 | | Morris | 1 | | Underground | 1 | | British | 1 | | Do | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Line" | | 4 | "Miller" | | 5 | "Morris" | | 6 | "Underground" |
| | places | | 0 | "Metropolitan" | | 1 | "British" |
| | globalScore | 0.75 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.833 | | wordCount | 1201 | | matches | | 0 | "not just the body, but the negative space around it" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 34.31 | | std | 21.81 | | cv | 0.636 | | sampleLengths | | 0 | 65 | | 1 | 44 | | 2 | 41 | | 3 | 39 | | 4 | 41 | | 5 | 51 | | 6 | 52 | | 7 | 42 | | 8 | 13 | | 9 | 17 | | 10 | 4 | | 11 | 50 | | 12 | 14 | | 13 | 96 | | 14 | 25 | | 15 | 4 | | 16 | 16 | | 17 | 30 | | 18 | 10 | | 19 | 37 | | 20 | 20 | | 21 | 69 | | 22 | 9 | | 23 | 75 | | 24 | 51 | | 25 | 44 | | 26 | 58 | | 27 | 11 | | 28 | 29 | | 29 | 23 | | 30 | 15 | | 31 | 25 | | 32 | 14 | | 33 | 17 | | 34 | 50 |
| |
| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "were scored" | | 1 | "is covered" | | 2 | "were traded" | | 3 | "was governed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 206 | | matches | | 0 | "was kneeling" | | 1 | "was watching" |
| |
| 34.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 105 | | ratio | 0.038 | | matches | | 0 | "A young man, pale-faced and dressed in modern streetwear, except for a peculiar pendant clutched in his stiffening fingers—a circular brass trinket coated in a heavy patina of verdigris." | | 1 | "Etched into the soot-stained brick were geometric patterns—concentric circles intersected by jagged saltires." | | 2 | "It reminded her, uncomfortably, of the case notes locked away in her private desk drawer back at the station—the unsolved files from three years ago, the ones involving her former partner, DS Morris, and the peculiar anomalies the brass ordered her to forget." | | 3 | "Her mind flashed to the rumors whispered among the transit workers—legends of a secret bazaar that operated in the forgotten veins of the Underground, appearing only under the light of a full moon." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 417 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.023980815347721823 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.011990407673860911 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 11.44 | | std | 7.75 | | cv | 0.678 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 25 | | 3 | 16 | | 4 | 4 | | 5 | 24 | | 6 | 12 | | 7 | 29 | | 8 | 23 | | 9 | 16 | | 10 | 8 | | 11 | 7 | | 12 | 13 | | 13 | 12 | | 14 | 1 | | 15 | 5 | | 16 | 16 | | 17 | 15 | | 18 | 15 | | 19 | 6 | | 20 | 13 | | 21 | 22 | | 22 | 5 | | 23 | 6 | | 24 | 17 | | 25 | 5 | | 26 | 14 | | 27 | 6 | | 28 | 13 | | 29 | 13 | | 30 | 3 | | 31 | 1 | | 32 | 4 | | 33 | 9 | | 34 | 13 | | 35 | 6 | | 36 | 22 | | 37 | 3 | | 38 | 1 | | 39 | 10 | | 40 | 13 | | 41 | 16 | | 42 | 3 | | 43 | 21 | | 44 | 43 | | 45 | 8 | | 46 | 17 | | 47 | 4 | | 48 | 8 | | 49 | 6 |
| |
| 71.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45714285714285713 | | totalSentences | 105 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 98 | | matches | | 0 | "Probably robbed him after he" | | 1 | "Just bagging the personal effects," | | 2 | "Maybe he flew down here," | | 3 | "Somewhere out there in the" |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 98 | | matches | | 0 | "She checked the time, her" | | 1 | "He stepped gingerly over a" | | 2 | "We find these vagrants down" | | 3 | "She moved with military precision," | | 4 | "Her brown eyes narrowed, scanning" | | 5 | "She leaned in close, catching" | | 6 | "It didn't smell like heroin." | | 7 | "It didn't smell like street" | | 8 | "It is not graffiti." | | 9 | "They weren't spray-painted with aerosol" | | 10 | "They were scored deep into" | | 11 | "She knew the neat, looping" | | 12 | "It reminded her, uncomfortably, of" | | 13 | "She turned her attention back" | | 14 | "She kept her voice even," | | 15 | "He stared down at the" | | 16 | "We both walked over here" | | 17 | "She knelt again, her eyes" | | 18 | "It twitched nervously toward the" | | 19 | "Her mind flashed to the" |
| | ratio | 0.296 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 98 | | matches | | 0 | "The damp air of the" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "She checked the time, her" | | 3 | "The body sat propped against" | | 4 | "A young man, pale-faced and" | | 5 | "Sergeant Miller, a heavy-set uniform" | | 6 | "He stepped gingerly over a" | | 7 | "Looks like a drug deal" | | 8 | "Junkie probably wandered down here" | | 9 | "We find these vagrants down" | | 10 | "Quinn didn't answer right away." | | 11 | "She moved with military precision," | | 12 | "Her brown eyes narrowed, scanning" | | 13 | "Miller called it a drug" | | 14 | "Quinn crouched beside the victim," | | 15 | "She leaned in close, catching" | | 16 | "It didn't smell like heroin." | | 17 | "It didn't smell like street" | | 18 | "Pockets are empty, sure, but" | | 19 | "Miller blinked, sweeping his flashlight" |
| | ratio | 0.684 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 98 | | matches | (empty) | | ratio | 0 | |
| 50.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 7 | | matches | | 0 | "Above them, the Northern Line rumbled through the dark earth, a subterranean heartbeat that rattled the loose iron rivets in the curved tunnel walls." | | 1 | "They were scored deep into the masonry, the edges sharp and white, as if burned into the stone with intense, localized heat." | | 2 | "The lines possessed a mathematical perfection, a deliberate symmetry that made the hair on the back of her neck stand up." | | 3 | "Do you see a single drop of foam, or sweat, or any sign of the violent convulsions that accompany a synthetic narcotic overdose?" | | 4 | "It twitched nervously toward the black mouth of the tunnel leading deeper into the disused transit network, vibrating with a frequency that felt less like magne…" | | 5 | "Her mind flashed to the rumors whispered among the transit workers—legends of a secret bazaar that operated in the forgotten veins of the Underground, appearing…" | | 6 | "The air around it felt heavier, colder, humming with a static charge that made her skin tingle." |
| |
| 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 | |