| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1337 | | 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) | |
| 66.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1337 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "streaming" | | 1 | "standard" | | 2 | "jaw clenched" | | 3 | "footsteps" | | 4 | "glint" | | 5 | "stomach" | | 6 | "electric" | | 7 | "anticipation" | | 8 | "crystallized" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 115 | | matches | (empty) | |
| 55.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 5 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | 0 | "appear to" | | 1 | "seemed to" | | 2 | "seem to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1323 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1306 | | uniqueNames | 17 | | maxNameDensity | 1.07 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 14 | | Herrera | 10 | | Raven | 1 | | Nest | 2 | | Bateman | 1 | | Street | 1 | | London | 2 | | Morris | 4 | | Metropolitan | 1 | | Police | 1 | | Tube | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Police" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Bateman" | | 3 | "Street" | | 4 | "London" | | 5 | "Market" |
| | globalScore | 0.964 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a dozen steps, the concrete g" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1323 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 33.92 | | std | 25.9 | | cv | 0.763 | | sampleLengths | | 0 | 56 | | 1 | 25 | | 2 | 48 | | 3 | 51 | | 4 | 45 | | 5 | 12 | | 6 | 13 | | 7 | 45 | | 8 | 34 | | 9 | 77 | | 10 | 30 | | 11 | 5 | | 12 | 45 | | 13 | 46 | | 14 | 26 | | 15 | 2 | | 16 | 101 | | 17 | 8 | | 18 | 92 | | 19 | 11 | | 20 | 53 | | 21 | 23 | | 22 | 3 | | 23 | 3 | | 24 | 12 | | 25 | 59 | | 26 | 8 | | 27 | 65 | | 28 | 48 | | 29 | 9 | | 30 | 36 | | 31 | 8 | | 32 | 57 | | 33 | 8 | | 34 | 6 | | 35 | 59 | | 36 | 27 | | 37 | 6 | | 38 | 61 |
| |
| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 115 | | matches | | 0 | "were covered" | | 1 | "been trained" | | 2 | "were shaped" | | 3 | "been trained" | | 4 | "been trained" |
| |
| 20.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 223 | | matches | | 0 | "was panicking" | | 1 | "wasn't hearing" | | 2 | "was hearing" | | 3 | "were expecting" | | 4 | "wasn't listening" | | 5 | "was talking" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 117 | | ratio | 0.085 | | matches | | 0 | "Quinn's worn leather watch caught the light from a passing taxi—11:47 PM—as she lengthened her stride." | | 1 | "Quinn knew these alleys intimately—every corner, every fire escape, every dead end that would trip up someone unfamiliar with the geography." | | 2 | "Then she saw it—the grate." | | 3 | "Water trickled down the walls, carrying the smell of earth and something else—something that made her sinuses prickle." | | 4 | "After what felt like a dozen steps, the concrete gave way to something older—stone, worn smooth by time and footsteps." | | 5 | "Not a modern Tube tunnel—she'd grown up in London, knew the maps of the Underground the way other people knew their own homes—but something older." | | 6 | "But something in the tone of those voices—the cadence, the underlying current of anticipation—made the hair on her arms stand on end." | | 7 | "\"—token for entry?\"" | | 8 | "And everywhere—everywhere—there were people." | | 9 | "Her eyes tracked Herrera as he backed away, moving deeper into the impossible crowd, and she followed—a detective chasing a suspect into territory where the rules of her world no longer applied, where the only thing that still made sense was the pursuit itself." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 877 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.03078677309007982 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.012542759407069556 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 11.31 | | std | 8.14 | | cv | 0.72 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 2 | | 3 | 14 | | 4 | 25 | | 5 | 16 | | 6 | 21 | | 7 | 7 | | 8 | 4 | | 9 | 14 | | 10 | 21 | | 11 | 9 | | 12 | 7 | | 13 | 3 | | 14 | 14 | | 15 | 28 | | 16 | 9 | | 17 | 3 | | 18 | 13 | | 19 | 3 | | 20 | 8 | | 21 | 6 | | 22 | 17 | | 23 | 11 | | 24 | 5 | | 25 | 11 | | 26 | 13 | | 27 | 5 | | 28 | 14 | | 29 | 10 | | 30 | 26 | | 31 | 9 | | 32 | 18 | | 33 | 14 | | 34 | 7 | | 35 | 9 | | 36 | 5 | | 37 | 14 | | 38 | 11 | | 39 | 18 | | 40 | 1 | | 41 | 1 | | 42 | 5 | | 43 | 2 | | 44 | 15 | | 45 | 16 | | 46 | 8 | | 47 | 4 | | 48 | 14 | | 49 | 8 |
| |
| 71.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.46153846153846156 | | totalSentences | 117 | | uniqueOpeners | 54 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 104 | | matches | | 0 | "Then she saw it—the grate." | | 1 | "Especially in impossible places." |
| | ratio | 0.019 | |
| 93.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 104 | | matches | | 0 | "He'd been tailing the entrance" | | 1 | "He cut left onto a" | | 2 | "She'd run down dozens of" | | 3 | "She could do it in" | | 4 | "Her short salt-and-pepper hair clung" | | 5 | "He reached the corner of" | | 6 | "She'd run this exact grid" | | 7 | "She rounded the corner hard," | | 8 | "She'd been thirty-four years old" | | 9 | "She'd spent those three years" | | 10 | "She could call for backup." | | 11 | "He was part of something" | | 12 | "Her sharp jaw clenched." | | 13 | "Her flashlight beam caught the" | | 14 | "They were covered in mold," | | 15 | "Her breathing sounded loud in" | | 16 | "Her flashlight beam played across" | | 17 | "She could hear voices ahead." | | 18 | "She wasn't hearing normal conversation." | | 19 | "She was hearing the sound" |
| | ratio | 0.317 | |
| 70.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 104 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn's shoes splashed" | | 2 | "The paramedic who'd vanished from" | | 3 | "He'd been tailing the entrance" | | 4 | "Quinn's worn leather watch caught" | | 5 | "Herrera was fast, but he" | | 6 | "He cut left onto a" | | 7 | "Quinn knew these alleys intimately—every" | | 8 | "She'd run down dozens of" | | 9 | "She could do it in" | | 10 | "The rain intensified." | | 11 | "Her short salt-and-pepper hair clung" | | 12 | "Herrera's silhouette was only twenty" | | 13 | "Quinn shouted, more out of" | | 14 | "Herrera didn't slow." | | 15 | "He reached the corner of" | | 16 | "Quinn's muscles tensed." | | 17 | "She'd run this exact grid" | | 18 | "She rounded the corner hard," | | 19 | "The rain drummed against the" |
| | ratio | 0.779 | |
| 48.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 104 | | matches | | 0 | "If she called for backup," |
| | ratio | 0.01 | |
| 30.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 9 | | matches | | 0 | "Quinn knew these alleys intimately—every corner, every fire escape, every dead end that would trip up someone unfamiliar with the geography." | | 1 | "Six months into that case, he'd gone into somewhere he shouldn't have, investigating leads that made no sense, talking about things that couldn't possibly be re…" | | 2 | "Water trickled down the walls, carrying the smell of earth and something else—something that made her sinuses prickle." | | 3 | "He was part of something larger, something that moved faster than the Metropolitan Police could react." | | 4 | "Her flashlight beam played across one that still functioned, casting deep shadows that made the tunnel seem to breathe." | | 5 | "She was hearing the sound of people who were expecting something." | | 6 | "An underground market that moved locations every full moon, where you could buy things that didn't officially exist." | | 7 | "That was the thought that crystallized in her mind as she stood at the threshold of the tunnel, one hand on her weapon, the other still holding her phone's flas…" | | 8 | "Staring her in the face across a market that shouldn't exist in a tunnel beneath a city she thought she understood." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |