| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 621 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 621 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "scanned" | | 2 | "eyebrow" | | 3 | "pounding" | | 4 | "echoed" | | 5 | "racing" | | 6 | "depths" | | 7 | "footsteps" | | 8 | "echoing" | | 9 | "silence" | | 10 | "oppressive" | | 11 | "flicker" | | 12 | "flickered" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
| | 3 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "heart pounded in her chest" | | 1 | "eyes narrowed" | | 2 | "The air was thick with" | | 3 | "a flicker of recognition" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 52 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 623 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 6.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 559 | | uniqueNames | 13 | | maxNameDensity | 2.86 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 16 | | Quinn | 1 | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Gerrard | 1 | | Street | 1 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Tomás | 4 | | Herrera | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Market" | | 4 | "Tomás" | | 5 | "Herrera" |
| | places | | 0 | "Soho" | | 1 | "Gerrard" | | 2 | "Street" |
| | globalScore | 0.069 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 623 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 27.09 | | std | 15.76 | | cv | 0.582 | | sampleLengths | | 0 | 41 | | 1 | 65 | | 2 | 12 | | 3 | 10 | | 4 | 12 | | 5 | 39 | | 6 | 42 | | 7 | 54 | | 8 | 42 | | 9 | 40 | | 10 | 41 | | 11 | 34 | | 12 | 30 | | 13 | 8 | | 14 | 17 | | 15 | 20 | | 16 | 16 | | 17 | 18 | | 18 | 5 | | 19 | 9 | | 20 | 24 | | 21 | 24 | | 22 | 20 |
| |
| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 52 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 94 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 59 | | ratio | 0.051 | | matches | | 0 | "A flash of green neon caught her eye - The Raven's Nest, a dimly lit bar on the corner of Gerrard Street." | | 1 | "She'd heard stories about this place - the strange wares on offer, the ruthless traders who operated in the shadows." | | 2 | "A figure caught her eye - Tomás Herrera, the former paramedic, standing by a stall selling strange, glowing artifacts." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 557 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.012567324955116697 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00718132854578097 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 10.56 | | std | 5.32 | | cv | 0.504 | | sampleLengths | | 0 | 17 | | 1 | 24 | | 2 | 22 | | 3 | 20 | | 4 | 11 | | 5 | 12 | | 6 | 12 | | 7 | 4 | | 8 | 6 | | 9 | 5 | | 10 | 7 | | 11 | 14 | | 12 | 11 | | 13 | 14 | | 14 | 15 | | 15 | 15 | | 16 | 12 | | 17 | 20 | | 18 | 5 | | 19 | 15 | | 20 | 8 | | 21 | 6 | | 22 | 16 | | 23 | 13 | | 24 | 13 | | 25 | 14 | | 26 | 17 | | 27 | 9 | | 28 | 15 | | 29 | 3 | | 30 | 3 | | 31 | 20 | | 32 | 10 | | 33 | 15 | | 34 | 9 | | 35 | 19 | | 36 | 11 | | 37 | 3 | | 38 | 5 | | 39 | 9 | | 40 | 8 | | 41 | 11 | | 42 | 9 | | 43 | 3 | | 44 | 13 | | 45 | 11 | | 46 | 7 | | 47 | 5 | | 48 | 2 | | 49 | 7 |
| |
| 50.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3220338983050847 | | totalSentences | 59 | | uniqueOpeners | 19 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | 0 | "Suddenly, the suspect darted down" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 50 | | matches | | 0 | "She scanned the room, her" | | 1 | "She pushed through the crowd," | | 2 | "She sprinted after him, the" | | 3 | "She'd never been down here," | | 4 | "She fumbled for her flashlight," | | 5 | "She moved cautiously, her gun" | | 6 | "She'd heard stories about this" | | 7 | "He met her gaze, a" | | 8 | "She pushed through the crowd," | | 9 | "She had to see this" |
| | ratio | 0.2 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 50 | | matches | | 0 | "Detective Harlow Quinn pounded the" | | 1 | "The rain-soaked streets of Soho" | | 2 | "A flash of green neon" | | 3 | "The suspect ducked inside, and" | | 4 | "She scanned the room, her" | | 5 | "The air was thick with" | | 6 | "the bartender called out, his" | | 7 | "Harlow flashed her badge." | | 8 | "The bartender raised an eyebrow." | | 9 | "Harlow's gaze swept the room," | | 10 | "She pushed through the crowd," | | 11 | "The door led to a" | | 12 | "She sprinted after him, the" | | 13 | "The buildings seemed to close" | | 14 | "The sound of her boots" | | 15 | "Harlow hesitated, her heart racing." | | 16 | "The Veil Market, a hidden" | | 17 | "She'd never been down here," | | 18 | "The air grew colder, the" | | 19 | "She fumbled for her flashlight," |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "the bartender called out (call out)" | | 1 | "Harlow demanded (demand)" |
| | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 1 | | effectiveRatio | 0.444 | |