| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva stepped back [back]" |
| | dialogueSentences | 44 | | tagDensity | 0.432 | | leniency | 0.864 | | rawRatio | 0.053 | | effectiveRatio | 0.045 | |
| 97.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1697 | | totalAiIsmAdverbs | 1 | | 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) | |
| 35.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1697 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "navigated" | | 1 | "standard" | | 2 | "fractured" | | 3 | "pristine" | | 4 | "scanned" | | 5 | "weight" | | 6 | "stark" | | 7 | "silence" | | 8 | "fascinating" | | 9 | "intricate" | | 10 | "pawn" | | 11 | "magnetic" | | 12 | "etched" | | 13 | "vibrated" | | 14 | "traced" | | 15 | "analyzed" | | 16 | "perfect" | | 17 | "echoing" | | 18 | "facade" |
| |
| 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 | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1679 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 10.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1182 | | uniqueNames | 10 | | maxNameDensity | 2.79 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 33 | | London | 2 | | Davies | 10 | | Northern | 1 | | Line | 1 | | Eva | 13 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Eva" | | 4 | "Morris" |
| | places | | | globalScore | 0.104 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | 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 | 1679 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 144 | | matches | (empty) | |
| 97.67% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 30.53 | | std | 15.01 | | cv | 0.492 | | sampleLengths | | 0 | 57 | | 1 | 59 | | 2 | 32 | | 3 | 30 | | 4 | 38 | | 5 | 21 | | 6 | 55 | | 7 | 17 | | 8 | 36 | | 9 | 17 | | 10 | 60 | | 11 | 57 | | 12 | 10 | | 13 | 28 | | 14 | 21 | | 15 | 3 | | 16 | 3 | | 17 | 21 | | 18 | 21 | | 19 | 38 | | 20 | 29 | | 21 | 40 | | 22 | 32 | | 23 | 21 | | 24 | 39 | | 25 | 35 | | 26 | 12 | | 27 | 59 | | 28 | 33 | | 29 | 21 | | 30 | 52 | | 31 | 32 | | 32 | 27 | | 33 | 27 | | 34 | 18 | | 35 | 18 | | 36 | 15 | | 37 | 20 | | 38 | 33 | | 39 | 28 | | 40 | 35 | | 41 | 7 | | 42 | 31 | | 43 | 19 | | 44 | 49 | | 45 | 53 | | 46 | 23 | | 47 | 46 | | 48 | 14 | | 49 | 54 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 192 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 144 | | ratio | 0.014 | | matches | | 0 | "Flames spared the clothing—a heavy wool overcoat, pressed trousers, polished oxfords remained pristine." | | 1 | "A memory hijacked her focus—DS Morris bleeding out on a warehouse floor three years ago, surrounded by impossible, frost-covered symbols drawn in his own blood." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1198 | | adjectiveStacks | 2 | | stackExamples | | 0 | "pale, subterranean-lit complexion." | | 1 | "impossible, frost-covered symbols" |
| | adverbCount | 13 | | adverbRatio | 0.010851419031719533 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005008347245409015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 11.66 | | std | 6.71 | | cv | 0.575 | | sampleLengths | | 0 | 19 | | 1 | 17 | | 2 | 9 | | 3 | 12 | | 4 | 18 | | 5 | 16 | | 6 | 15 | | 7 | 10 | | 8 | 14 | | 9 | 10 | | 10 | 8 | | 11 | 7 | | 12 | 10 | | 13 | 13 | | 14 | 7 | | 15 | 31 | | 16 | 6 | | 17 | 15 | | 18 | 8 | | 19 | 13 | | 20 | 12 | | 21 | 11 | | 22 | 11 | | 23 | 2 | | 24 | 15 | | 25 | 11 | | 26 | 25 | | 27 | 4 | | 28 | 13 | | 29 | 20 | | 30 | 18 | | 31 | 22 | | 32 | 12 | | 33 | 15 | | 34 | 15 | | 35 | 6 | | 36 | 9 | | 37 | 7 | | 38 | 3 | | 39 | 18 | | 40 | 10 | | 41 | 5 | | 42 | 16 | | 43 | 3 | | 44 | 3 | | 45 | 21 | | 46 | 7 | | 47 | 14 | | 48 | 25 | | 49 | 8 |
| |
| 51.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3541666666666667 | | totalSentences | 144 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 116 | | matches | | 0 | "She smoothed a stray strand" | | 1 | "Her sharp jaw set as" | | 2 | "He gestured to the center" | | 3 | "She checked the worn leather" | | 4 | "She snapped on a pair" | | 5 | "She swept her brown eyes" | | 6 | "She scanned the floor, finding" | | 7 | "She shifted her weight, tucking" | | 8 | "She knelt and plucked a" | | 9 | "They shared zero resemblance with" | | 10 | "It spun in a frenetic" | | 11 | "It pointed straight at a" | | 12 | "She pivoted a full hundred" | | 13 | "She walked toward the bricked-up" | | 14 | "Her boots left sharp, fresh" | | 15 | "She examined the floor directly" | | 16 | "She turned back to the" | | 17 | "She analyzed the dragging marks" | | 18 | "She found a second anomaly" | | 19 | "She pried it out." |
| | ratio | 0.25 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 116 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The air down deep in" | | 2 | "Ozone and scorched copper masked" | | 3 | "Quinn squared her shoulders, her" | | 4 | "She smoothed a stray strand" | | 5 | "Her sharp jaw set as" | | 6 | "Sergeant Davies stood near the" | | 7 | "He gestured to the center" | | 8 | "An opaque plastic tarp covered" | | 9 | "Quinn approached the center of" | | 10 | "She checked the worn leather" | | 11 | "Davies flipped a page on" | | 12 | "Quinn crouched next to the" | | 13 | "She snapped on a pair" | | 14 | "The victim lay curled in" | | 15 | "Flames spared the clothing—a heavy" | | 16 | "She swept her brown eyes" | | 17 | "Quinn pointed a gloved finger" | | 18 | "Davies clicked his pen." | | 19 | "Quinn paced the perimeter of" |
| | ratio | 0.931 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "Quinn demanded (demand)" | | 1 | "Quinn addressed (address)" | | 2 | "Quinn outlined (outline)" | | 3 | "Quinn continued (continue)" |
| | dialogueSentences | 44 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 1 | | effectiveRatio | 0.182 | |