| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.191 | | leniency | 0.382 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1563 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 32.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1563 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "traced" | | 1 | "familiar" | | 2 | "scanned" | | 3 | "silence" | | 4 | "navigated" | | 5 | "structure" | | 6 | "measured" | | 7 | "chaotic" | | 8 | "warmth" | | 9 | "fractured" | | 10 | "tension" | | 11 | "pulse" | | 12 | "velvet" | | 13 | "shattered" | | 14 | "mechanical" | | 15 | "affection" | | 16 | "potential" | | 17 | "flickered" |
| |
| 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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 146 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1552 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 44.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 999 | | uniqueNames | 12 | | maxNameDensity | 2.1 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Rory | 21 | | Cardiff | 1 | | London | 2 | | Underground | 1 | | Silas | 8 | | Eva | 21 | | Dickens | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Eva" | | 3 | "Dickens" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Cardiff" | | 3 | "London" |
| | globalScore | 0.449 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | 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 | 1552 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 146 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 93 | | mean | 16.69 | | std | 15.36 | | cv | 0.92 | | sampleLengths | | 0 | 53 | | 1 | 89 | | 2 | 22 | | 3 | 4 | | 4 | 49 | | 5 | 1 | | 6 | 22 | | 7 | 30 | | 8 | 6 | | 9 | 52 | | 10 | 8 | | 11 | 24 | | 12 | 13 | | 13 | 4 | | 14 | 13 | | 15 | 9 | | 16 | 11 | | 17 | 10 | | 18 | 3 | | 19 | 12 | | 20 | 7 | | 21 | 6 | | 22 | 4 | | 23 | 62 | | 24 | 46 | | 25 | 35 | | 26 | 12 | | 27 | 2 | | 28 | 5 | | 29 | 20 | | 30 | 10 | | 31 | 16 | | 32 | 6 | | 33 | 8 | | 34 | 25 | | 35 | 23 | | 36 | 21 | | 37 | 5 | | 38 | 12 | | 39 | 2 | | 40 | 5 | | 41 | 50 | | 42 | 4 | | 43 | 3 | | 44 | 5 | | 45 | 9 | | 46 | 4 | | 47 | 18 | | 48 | 5 | | 49 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 155 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 146 | | ratio | 0.007 | | matches | | 0 | "Her perfume flooded the air, overriding the smell of stale beer—a sharp, aggressive scent of jasmine and cracked black pepper." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1008 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 8 | | adverbRatio | 0.007936507936507936 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000992063492063492 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 146 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 146 | | mean | 10.63 | | std | 6.29 | | cv | 0.591 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 11 | | 3 | 14 | | 4 | 9 | | 5 | 21 | | 6 | 18 | | 7 | 16 | | 8 | 16 | | 9 | 9 | | 10 | 7 | | 11 | 15 | | 12 | 4 | | 13 | 14 | | 14 | 8 | | 15 | 7 | | 16 | 20 | | 17 | 1 | | 18 | 2 | | 19 | 20 | | 20 | 4 | | 21 | 26 | | 22 | 6 | | 23 | 5 | | 24 | 11 | | 25 | 16 | | 26 | 20 | | 27 | 8 | | 28 | 14 | | 29 | 10 | | 30 | 13 | | 31 | 4 | | 32 | 13 | | 33 | 4 | | 34 | 5 | | 35 | 11 | | 36 | 10 | | 37 | 3 | | 38 | 12 | | 39 | 7 | | 40 | 6 | | 41 | 4 | | 42 | 9 | | 43 | 12 | | 44 | 20 | | 45 | 13 | | 46 | 8 | | 47 | 5 | | 48 | 17 | | 49 | 15 |
| |
| 42.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.2808219178082192 | | totalSentences | 146 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 87 | | matches | | 0 | "Her delivery jacket reeked of" | | 1 | "She traced the condensation forming" | | 2 | "His slight limp favored his" | | 3 | "He caught her bright blue" | | 4 | "Her fingers found the small" | | 5 | "Her gaze swept past the" | | 6 | "She claimed the stool two" | | 7 | "Her perfume flooded the air," | | 8 | "Her manicured fingernails tapped the" | | 9 | "He pushed a toothpick loaded" | | 10 | "He scooped a handful of" | | 11 | "His hazel gaze held firm," | | 12 | "He navigated past the till" | | 13 | "He stepped through, pulling the" | | 14 | "Her skin pulled tight around" | | 15 | "Her posture deteriorated, shoulders slumping" | | 16 | "She chewed the olive with" | | 17 | "He gave them a wide" | | 18 | "Her manicured fingers hovered inches" | | 19 | "She looked a decade older" |
| | ratio | 0.241 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 87 | | matches | | 0 | "Rain lashed the Soho pavement," | | 1 | "Rory sat at the far" | | 2 | "Her delivery jacket reeked of" | | 3 | "She traced the condensation forming" | | 4 | "Silas swiped a crushed rag" | | 5 | "His slight limp favored his" | | 6 | "The silver signet ring on" | | 7 | "He caught her bright blue" | | 8 | "Rory shook her head, turning" | | 9 | "The heavy oak front door" | | 10 | "A gust of raw, wet" | | 11 | "Rory gripped her glass." | | 12 | "A woman stood in the" | | 13 | "Platinum blonde hair, severe and" | | 14 | "Her fingers found the small" | | 15 | "Eva scanned the room." | | 16 | "Her gaze swept past the" | | 17 | "Silence swallowed the space between" | | 18 | "Eva moved across the room." | | 19 | "The strike of her heels" |
| | ratio | 0.954 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 1 | | matches | | 0 | "The girl who used to steal traffic cones and smoke roll-ups on the roof of the student union existed nowhere in the striking woman sitting beside her." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Rory addressed (address)" | | 1 | "Rory pressed (press)" | | 2 | "Rory noted (note)" |
| | dialogueSentences | 68 | | tagDensity | 0.044 | | leniency | 0.088 | | rawRatio | 1 | | effectiveRatio | 0.088 | |