| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1683 | | 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) | |
| 22.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1683 | | totalAiIsms | 26 | | found | | | highlights | | 0 | "electric" | | 1 | "weight" | | 2 | "scanned" | | 3 | "echoed" | | 4 | "output" |
| |
| 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 | 266 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 266 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 335 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 17 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1683 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 67.63% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 116 | | wordCount | 1214 | | uniqueNames | 23 | | maxNameDensity | 1.65 | | worstName | "Boots" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Boots" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Metropolitan | 2 | | Police | 2 | | Quinn | 6 | | Boots | 20 | | Water | 4 | | Military | 14 | | Brick | 7 | | Steam | 7 | | Rats | 7 | | Chest | 4 | | Blood | 4 | | Raised | 4 | | Aimed | 4 | | Marched | 4 | | Ran | 4 | | Concrete | 4 | | Damp | 4 | | Smelled | 4 | | Heavy | 4 | | Stale | 4 |
| | persons | | 0 | "Police" | | 1 | "Quinn" | | 2 | "Boots" | | 3 | "Water" | | 4 | "Chest" | | 5 | "Blood" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Brick" |
| | globalScore | 0.676 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | 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 | 1683 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 335 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 18.91 | | std | 32.93 | | cv | 1.742 | | sampleLengths | | 0 | 97 | | 1 | 1 | | 2 | 2 | | 3 | 1 | | 4 | 2 | | 5 | 1 | | 6 | 17 | | 7 | 4 | | 8 | 87 | | 9 | 1 | | 10 | 19 | | 11 | 8 | | 12 | 9 | | 13 | 2 | | 14 | 13 | | 15 | 4 | | 16 | 10 | | 17 | 4 | | 18 | 16 | | 19 | 11 | | 20 | 4 | | 21 | 17 | | 22 | 1 | | 23 | 14 | | 24 | 3 | | 25 | 13 | | 26 | 1 | | 27 | 160 | | 28 | 1 | | 29 | 19 | | 30 | 8 | | 31 | 9 | | 32 | 2 | | 33 | 13 | | 34 | 4 | | 35 | 10 | | 36 | 4 | | 37 | 16 | | 38 | 11 | | 39 | 4 | | 40 | 17 | | 41 | 1 | | 42 | 14 | | 43 | 3 | | 44 | 13 | | 45 | 1 | | 46 | 72 | | 47 | 109 | | 48 | 1 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 266 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 277 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 335 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1216 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.018092105263157895 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003289473684210526 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 335 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 335 | | mean | 5.02 | | std | 3.33 | | cv | 0.662 | | sampleLengths | | 0 | 4 | | 1 | 10 | | 2 | 11 | | 3 | 7 | | 4 | 4 | | 5 | 7 | | 6 | 4 | | 7 | 4 | | 8 | 2 | | 9 | 1 | | 10 | 7 | | 11 | 3 | | 12 | 4 | | 13 | 2 | | 14 | 4 | | 15 | 2 | | 16 | 3 | | 17 | 3 | | 18 | 2 | | 19 | 4 | | 20 | 2 | | 21 | 7 | | 22 | 1 | | 23 | 2 | | 24 | 1 | | 25 | 2 | | 26 | 1 | | 27 | 6 | | 28 | 11 | | 29 | 4 | | 30 | 3 | | 31 | 5 | | 32 | 5 | | 33 | 2 | | 34 | 12 | | 35 | 4 | | 36 | 5 | | 37 | 9 | | 38 | 7 | | 39 | 2 | | 40 | 4 | | 41 | 5 | | 42 | 2 | | 43 | 6 | | 44 | 4 | | 45 | 3 | | 46 | 4 | | 47 | 5 | | 48 | 1 | | 49 | 7 |
| |
| 41.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 28 | | diversityRatio | 0.14626865671641792 | | totalSentences | 335 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 211 | | matches | (empty) | | ratio | 0 | |
| 32.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 99 | | totalSentences | 211 | | matches | | 0 | "I'll generate it now." | | 1 | "His left heel dragged through" | | 2 | "She closed the gap." | | 3 | "She planted her feet and" | | 4 | "She grabbed his elbow." | | 5 | "She tightened her grip." | | 6 | "She hauled him upright by" | | 7 | "He spat blood into the" | | 8 | "He broke free." | | 9 | "She matched his stride." | | 10 | "She kept her weight forward." | | 11 | "He scrambled over a rusted" | | 12 | "She vaulted after him." | | 13 | "She scanned the dark." | | 14 | "He vanished around a corner." | | 15 | "He leaned against the wall." | | 16 | "She pulled her taser." | | 17 | "She lowered the taser." | | 18 | "She kicked the door." | | 19 | "They dropped into a narrow" |
| | ratio | 0.469 | |
| 45.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 175 | | totalSentences | 211 | | matches | | 0 | "I'll generate it now." | | 1 | "Rain lashed Camden High Street" | | 2 | "Neon signs bled pink and" | | 3 | "Quinn tracked the runner three" | | 4 | "The man moved fast." | | 5 | "His left heel dragged through" | | 6 | "Mud caked his boots." | | 7 | "She closed the gap." | | 8 | "She planted her feet and" | | 9 | "Water sprayed her knees." | | 10 | "She grabbed his elbow." | | 11 | "Elbow to jaw." | | 12 | "Knuckles met bone." | | 13 | "She tightened her grip." | | 14 | "She hauled him upright by" | | 15 | "He spat blood into the" | | 16 | "He broke free." | | 17 | "Glass rattled in its frames." | | 18 | "She matched his stride." | | 19 | "She kept her weight forward." |
| | ratio | 0.829 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 211 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 6 | | 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 | |