| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1789 | | 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) | |
| 72.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1789 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "chill" | | 1 | "standard" | | 2 | "silence" | | 3 | "etched" | | 4 | "magnetic" | | 5 | "echoed" | | 6 | "traced" | | 7 | "vibrated" | | 8 | "scanned" |
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| 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 | 199 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 199 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 199 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1789 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1789 | | uniqueNames | 13 | | maxNameDensity | 0.89 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Tube | 1 | | Camden | 3 | | Lang | 13 | | Morris | 3 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 4 | | Shade | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Lang" | | 3 | "Morris" | | 4 | "Kowalski" | | 5 | "Museum" | | 6 | "Eva" | | 7 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 153 | | 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 | 1789 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 199 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 43.63 | | std | 23.87 | | cv | 0.547 | | sampleLengths | | 0 | 76 | | 1 | 46 | | 2 | 20 | | 3 | 42 | | 4 | 68 | | 5 | 28 | | 6 | 20 | | 7 | 13 | | 8 | 37 | | 9 | 68 | | 10 | 63 | | 11 | 6 | | 12 | 21 | | 13 | 42 | | 14 | 12 | | 15 | 45 | | 16 | 79 | | 17 | 30 | | 18 | 26 | | 19 | 46 | | 20 | 24 | | 21 | 42 | | 22 | 101 | | 23 | 65 | | 24 | 84 | | 25 | 6 | | 26 | 42 | | 27 | 62 | | 28 | 40 | | 29 | 35 | | 30 | 54 | | 31 | 54 | | 32 | 13 | | 33 | 31 | | 34 | 59 | | 35 | 68 | | 36 | 6 | | 37 | 18 | | 38 | 51 | | 39 | 66 | | 40 | 80 |
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| 99.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 199 | | matches | | 0 | "been sealed" | | 1 | "been carried" | | 2 | "were added" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 312 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 199 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1793 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.026213050752928055 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.005577244841048522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 199 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 199 | | mean | 8.99 | | std | 4.66 | | cv | 0.519 | | sampleLengths | | 0 | 14 | | 1 | 13 | | 2 | 16 | | 3 | 33 | | 4 | 10 | | 5 | 10 | | 6 | 11 | | 7 | 15 | | 8 | 6 | | 9 | 14 | | 10 | 6 | | 11 | 5 | | 12 | 8 | | 13 | 10 | | 14 | 6 | | 15 | 7 | | 16 | 5 | | 17 | 10 | | 18 | 11 | | 19 | 5 | | 20 | 3 | | 21 | 18 | | 22 | 5 | | 23 | 11 | | 24 | 13 | | 25 | 4 | | 26 | 11 | | 27 | 6 | | 28 | 8 | | 29 | 6 | | 30 | 13 | | 31 | 5 | | 32 | 4 | | 33 | 4 | | 34 | 10 | | 35 | 3 | | 36 | 11 | | 37 | 2 | | 38 | 5 | | 39 | 17 | | 40 | 9 | | 41 | 11 | | 42 | 15 | | 43 | 3 | | 44 | 6 | | 45 | 14 | | 46 | 9 | | 47 | 6 | | 48 | 11 | | 49 | 8 |
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| 73.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.46733668341708545 | | totalSentences | 199 | | uniqueOpeners | 93 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 188 | | matches | | 0 | "Maybe he lifted it from" | | 1 | "Instead faint circular impressions suggested" | | 2 | "Even the position of the" | | 3 | "Maybe the market angle holds" | | 4 | "Instead he became a warning" | | 5 | "Only this one man failed" | | 6 | "At least a dozen." |
| | ratio | 0.037 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 188 | | matches | | 0 | "Her boots met the platform" | | 1 | "Their glare carved hard lines" | | 2 | "She tugged on gloves, the" | | 3 | "She lifted the sleeve higher." | | 4 | "They did not arrange them" | | 5 | "Your interpretation skips the contradictions," | | 6 | "We bag it, tag it," | | 7 | "Her sharp jaw flexed once." | | 8 | "She circled the body, boots" | | 9 | "She returned to the body" | | 10 | "She crossed the threshold." | | 11 | "His flashlight beam danced over" | | 12 | "He came for information or" | | 13 | "She moved to the next" | | 14 | "She tucked stray curls behind" | | 15 | "They matched the pattern of" | | 16 | "She stood and swept her" | | 17 | "Its needle locked upward." | | 18 | "Her outstretched hand met resistance," | | 19 | "You planning to explain any" |
| | ratio | 0.165 | |
| 90.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 139 | | totalSentences | 188 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "Her boots met the platform" | | 2 | "Brown eyes swept the curved" | | 3 | "The call had pulled her" | | 4 | "Their glare carved hard lines" | | 5 | "A body sprawled near the" | | 6 | "Blood spread outward in a" | | 7 | "Sergeant Lang straightened from his" | | 8 | "Notebook dangled from one hand" | | 9 | "Looks like your standard overdose," | | 10 | "Needle by the right hand." | | 11 | "Track marks running the length" | | 12 | "Station's been sealed since the" | | 13 | "Quinn approached without breaking stride." | | 14 | "She tugged on gloves, the" | | 15 | "The victim's face stared upward," | | 16 | "Those track marks showed perfectly" | | 17 | "She lifted the sleeve higher." | | 18 | "The syringe rested two inches" | | 19 | "Junkies dropped their tools." |
| | ratio | 0.739 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 188 | | matches | | 0 | "If this market traded in" | | 1 | "If this is more than" | | 2 | "If we pursue this your" | | 3 | "Whoever runs this place expects" | | 4 | "Now those circumstances had names." |
| | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 89 | | technicalSentenceCount | 3 | | matches | | 0 | "Blood spread outward in a deliberate fan that caught the light with an unnatural sheen." | | 1 | "His flashlight beam danced over empty shelves and a banner stitched with symbols that matched the compass face." | | 2 | "Her outstretched hand met resistance, a cold membrane that sent pins up her arm." |
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| 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 | |