| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 729 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 3.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 729 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "silence" | | 1 | "weight" | | 2 | "traced" | | 3 | "fractured" | | 4 | "echoed" | | 5 | "tracing" | | 6 | "flicked" | | 7 | "quivered" | | 8 | "etched" | | 9 | "flickered" | | 10 | "pulse" | | 11 | "chill" | | 12 | "raced" | | 13 | "pounding" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "sent a chill through" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 59 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 726 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 32.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 554 | | uniqueNames | 6 | | maxNameDensity | 2.35 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 13 | | Quinn | 1 | | Camden | 1 | | Tube | 1 | | Eva | 7 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Eva" | | 4 | "Kowalski" |
| | places | (empty) | | globalScore | 0.327 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | 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 | 726 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 34.57 | | std | 23.39 | | cv | 0.677 | | sampleLengths | | 0 | 99 | | 1 | 66 | | 2 | 55 | | 3 | 30 | | 4 | 43 | | 5 | 31 | | 6 | 54 | | 7 | 4 | | 8 | 31 | | 9 | 58 | | 10 | 10 | | 11 | 34 | | 12 | 46 | | 13 | 10 | | 14 | 32 | | 15 | 4 | | 16 | 46 | | 17 | 35 | | 18 | 10 | | 19 | 7 | | 20 | 21 |
| |
| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 59 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 99 | | matches | (empty) | |
| 65.64% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 74 | | ratio | 0.027 | | matches | | 0 | "These weren’t the crude scratches of a desperate man—they were precise, almost…" | | 1 | "The symbols weren’t just a map—they were a warning." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 557 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02154398563734291 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00718132854578097 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 9.81 | | std | 5.66 | | cv | 0.577 | | sampleLengths | | 0 | 25 | | 1 | 21 | | 2 | 20 | | 3 | 18 | | 4 | 15 | | 5 | 16 | | 6 | 16 | | 7 | 18 | | 8 | 16 | | 9 | 7 | | 10 | 11 | | 11 | 27 | | 12 | 10 | | 13 | 8 | | 14 | 22 | | 15 | 11 | | 16 | 14 | | 17 | 12 | | 18 | 6 | | 19 | 8 | | 20 | 3 | | 21 | 14 | | 22 | 6 | | 23 | 7 | | 24 | 15 | | 25 | 3 | | 26 | 12 | | 27 | 1 | | 28 | 10 | | 29 | 6 | | 30 | 2 | | 31 | 2 | | 32 | 12 | | 33 | 9 | | 34 | 10 | | 35 | 4 | | 36 | 10 | | 37 | 10 | | 38 | 17 | | 39 | 5 | | 40 | 12 | | 41 | 3 | | 42 | 7 | | 43 | 5 | | 44 | 6 | | 45 | 15 | | 46 | 8 | | 47 | 6 | | 48 | 11 | | 49 | 5 |
| |
| 69.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.43243243243243246 | | totalSentences | 74 | | uniqueOpeners | 32 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 56 | | matches | | 0 | "Somewhere in the tunnel, a" | | 1 | "Somewhere in the distance, a" |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 56 | | matches | | 0 | "Her leather watch glinted dully" | | 1 | "She leaned against a crumbling" | | 2 | "Her fingers traced a circle" | | 3 | "She lifted a shard of" | | 4 | "She pressed the compass to" | | 5 | "They moved forward, boots crunching" | | 6 | "she murmured, tracing a line" | | 7 | "They reached the fissure." | | 8 | "They smelled of smoke and" | | 9 | "She glanced at the sarcophagus" | | 10 | "She turned to Eva" | | 11 | "They moved toward the source" | | 12 | "Its face was obscured, but" |
| | ratio | 0.232 | |
| 13.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 56 | | matches | | 0 | "Detective Harlow Quinn stepped into" | | 1 | "Her leather watch glinted dully" | | 2 | "The compass in her pocket" | | 3 | "A single bulb swung overhead," | | 4 | "She leaned against a crumbling" | | 5 | "The satchel slung across her" | | 6 | "Harlow knelt, brushing grime from" | | 7 | "Her fingers traced a circle" | | 8 | "She lifted a shard of" | | 9 | "Eva crouched beside her, squinting" | | 10 | "Harlow interrupted, her voice low" | | 11 | "She pressed the compass to" | | 12 | "The needle spun wildly before" | | 13 | "A sudden clatter echoed from" | | 14 | "Both women froze." | | 15 | "Harlow’s hand flew to her" | | 16 | "They moved forward, boots crunching" | | 17 | "The fissure yawned wider now," | | 18 | "Harlow’s breath hitched." | | 19 | "These weren’t the crude scratches" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "She lifted, its edges sharp enough to draw blood" | | 1 | "Harlow interrupted, her voice low and steady" |
| |
| 19.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow interrupted (interrupt)" | | 1 | "she murmured (murmur)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 23 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.75 | | effectiveRatio | 0.261 | |