| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1242 | | 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) | |
| 11.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1242 | | totalAiIsms | 22 | | found | | | highlights | | 0 | "gloom" | | 1 | "traced" | | 2 | "whisper" | | 3 | "echoed" | | 4 | "etched" | | 5 | "trembled" | | 6 | "magnetic" | | 7 | "unwavering" | | 8 | "measured" | | 9 | "calculated" | | 10 | "mechanical" | | 11 | "vibrated" | | 12 | "intricate" | | 13 | "silence" | | 14 | "shattered" | | 15 | "pulsed" | | 16 | "rhythmic" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 295 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 295 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 295 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1242 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 10 | | matches | | 0 | "Rough work, Vance said." | | 1 | "Look closer, Quinn said." | | 2 | "Ritualistic, Vance said." | | 3 | "A compass, Quinn said." | | 4 | "A broken one, Vance corrected." | | 5 | "The blood channels, she said." | | 6 | "Six feet, Vance murmured." | | 7 | "Stop, Vance said." | | 8 | "I told you, Vance called down." | | 9 | "Quinn, he stammered." |
| |
| 53.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1242 | | uniqueNames | 12 | | maxNameDensity | 1.93 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Met | 1 | | Vance | 17 | | Quinn | 24 | | Morris | 2 | | Harlow | 1 | | Camden | 2 | | Veil | 1 | | Market | 1 | | Clean | 3 | | Look | 4 | | Slow | 3 | | Three | 3 |
| | persons | | 0 | "Met" | | 1 | "Vance" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Look" |
| | places | (empty) | | globalScore | 0.534 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | 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 | 1242 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 295 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 21.05 | | std | 13.67 | | cv | 0.649 | | sampleLengths | | 0 | 64 | | 1 | 20 | | 2 | 23 | | 3 | 28 | | 4 | 7 | | 5 | 34 | | 6 | 44 | | 7 | 4 | | 8 | 16 | | 9 | 15 | | 10 | 25 | | 11 | 53 | | 12 | 51 | | 13 | 14 | | 14 | 46 | | 15 | 12 | | 16 | 4 | | 17 | 17 | | 18 | 33 | | 19 | 5 | | 20 | 13 | | 21 | 10 | | 22 | 42 | | 23 | 14 | | 24 | 17 | | 25 | 6 | | 26 | 37 | | 27 | 14 | | 28 | 31 | | 29 | 18 | | 30 | 26 | | 31 | 37 | | 32 | 15 | | 33 | 16 | | 34 | 25 | | 35 | 29 | | 36 | 18 | | 37 | 34 | | 38 | 23 | | 39 | 30 | | 40 | 15 | | 41 | 30 | | 42 | 5 | | 43 | 15 | | 44 | 3 | | 45 | 7 | | 46 | 5 | | 47 | 19 | | 48 | 18 | | 49 | 8 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 295 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 258 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 295 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1243 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.016090104585679808 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004022526146419952 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 295 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 295 | | mean | 4.21 | | std | 2.79 | | cv | 0.663 | | sampleLengths | | 0 | 11 | | 1 | 13 | | 2 | 2 | | 3 | 5 | | 4 | 14 | | 5 | 11 | | 6 | 6 | | 7 | 1 | | 8 | 1 | | 9 | 6 | | 10 | 14 | | 11 | 4 | | 12 | 4 | | 13 | 5 | | 14 | 1 | | 15 | 3 | | 16 | 6 | | 17 | 3 | | 18 | 6 | | 19 | 7 | | 20 | 2 | | 21 | 1 | | 22 | 1 | | 23 | 8 | | 24 | 7 | | 25 | 7 | | 26 | 4 | | 27 | 12 | | 28 | 11 | | 29 | 8 | | 30 | 8 | | 31 | 8 | | 32 | 2 | | 33 | 8 | | 34 | 1 | | 35 | 1 | | 36 | 5 | | 37 | 3 | | 38 | 4 | | 39 | 3 | | 40 | 2 | | 41 | 8 | | 42 | 2 | | 43 | 1 | | 44 | 3 | | 45 | 6 | | 46 | 3 | | 47 | 3 | | 48 | 7 | | 49 | 4 |
| |
| 52.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 27 | | diversityRatio | 0.3728813559322034 | | totalSentences | 295 | | uniqueOpeners | 110 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 213 | | matches | | 0 | "Probably stolen from a tourist" | | 1 | "Then it snapped upright." | | 2 | "Then the stone bounced back." |
| | ratio | 0.014 | |
| 62.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 84 | | totalSentences | 213 | | matches | | 0 | "She kept her boots steady." | | 1 | "His chest bore a precise" | | 2 | "He flipped a small black" | | 3 | "They wanted to hurt him" | | 4 | "She knelt and examined the" | | 5 | "It followed the grooves between" | | 6 | "It's a ruined tube station," | | 7 | "She noted the spacing between" | | 8 | "They carve symbols." | | 9 | "They use crude blades." | | 10 | "She stood and paced the" | | 11 | "Her left wrist caught the" | | 12 | "She adjusted it." | | 13 | "You're seeing ghosts again, Vance" | | 14 | "He closed his notebook with" | | 15 | "She walked toward the collapsed" | | 16 | "She lifted the lid." | | 17 | "He approached and peered over" | | 18 | "They left it as a" | | 19 | "She turned it over." |
| | ratio | 0.394 | |
| 37.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 180 | | totalSentences | 213 | | matches | | 0 | "Quinn kicked the rusted barrier" | | 1 | "The metallic tang of fresh" | | 2 | "She kept her boots steady." | | 3 | "His chest bore a precise" | | 4 | "DS Vance crouched near the" | | 5 | "He flipped a small black" | | 6 | "Amateurs slash and pray." | | 7 | "Hesitation marks everywhere." | | 8 | "They wanted to hurt him" | | 9 | "Quinn didn't answer." | | 10 | "She knelt and examined the" | | 11 | "The blood pooled in the" | | 12 | "It followed the grooves between" | | 13 | "This isn't a flat surface," | | 14 | "It's a ruined tube station," | | 15 | "Camden line used to run" | | 16 | "The council sold the land" | | 17 | "Quinn traced the blood with" | | 18 | "The liquid tracked toward a" | | 19 | "The ink had faded, but" |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 213 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 12 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |