| 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 | 1085 | | 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) | |
| 40.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1085 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "navigated" | | 1 | "calculated" | | 2 | "jaw clenched" | | 3 | "pulse" | | 4 | "loomed" | | 5 | "glint" | | 6 | "calculate" | | 7 | "potential" | | 8 | "flickered" | | 9 | "charged" | | 10 | "scanned" | | 11 | "weight" | | 12 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1085 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.48% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1085 | | uniqueNames | 13 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 14 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Tomás | 6 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Tomás" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 0.855 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 1 | | matches | | 0 | "figures that seemed to move in the shadows" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1085 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 40.19 | | std | 20.42 | | cv | 0.508 | | sampleLengths | | 0 | 70 | | 1 | 54 | | 2 | 5 | | 3 | 68 | | 4 | 65 | | 5 | 41 | | 6 | 6 | | 7 | 61 | | 8 | 51 | | 9 | 44 | | 10 | 23 | | 11 | 4 | | 12 | 36 | | 13 | 57 | | 14 | 32 | | 15 | 7 | | 16 | 42 | | 17 | 10 | | 18 | 15 | | 19 | 50 | | 20 | 42 | | 21 | 53 | | 22 | 60 | | 23 | 58 | | 24 | 46 | | 25 | 52 | | 26 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 179 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1087 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02207911683532659 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004599816007359705 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.43 | | std | 4.22 | | cv | 0.448 | | sampleLengths | | 0 | 9 | | 1 | 18 | | 2 | 8 | | 3 | 9 | | 4 | 4 | | 5 | 9 | | 6 | 13 | | 7 | 12 | | 8 | 7 | | 9 | 4 | | 10 | 11 | | 11 | 13 | | 12 | 7 | | 13 | 5 | | 14 | 14 | | 15 | 11 | | 16 | 5 | | 17 | 6 | | 18 | 10 | | 19 | 7 | | 20 | 15 | | 21 | 17 | | 22 | 5 | | 23 | 8 | | 24 | 11 | | 25 | 2 | | 26 | 8 | | 27 | 14 | | 28 | 9 | | 29 | 5 | | 30 | 11 | | 31 | 8 | | 32 | 8 | | 33 | 2 | | 34 | 4 | | 35 | 6 | | 36 | 7 | | 37 | 15 | | 38 | 6 | | 39 | 13 | | 40 | 7 | | 41 | 7 | | 42 | 8 | | 43 | 15 | | 44 | 9 | | 45 | 8 | | 46 | 11 | | 47 | 7 | | 48 | 6 | | 49 | 14 |
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| 53.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3565217391304348 | | totalSentences | 115 | | uniqueOpeners | 41 | |
| 29.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 112 | | matches | | 0 | "Instead he picked up speed" |
| | ratio | 0.009 | |
| 98.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 112 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She kept one hand near" | | 2 | "Her military precision allowed her" | | 3 | "She skirted the edge without" | | 4 | "She calculated her next move." | | 5 | "She took it to cut" | | 6 | "Her boots pounded against the" | | 7 | "She burst back onto the" | | 8 | "He glanced over his shoulder." | | 9 | "He veered toward a narrower" | | 10 | "She noted the change in" | | 11 | "Her sharp jaw clenched when" | | 12 | "He slowed for a second" | | 13 | "She called out across the" | | 14 | "He crossed into Camden's territory" | | 15 | "Her pulse increased but she" | | 16 | "She shook off the distraction" | | 17 | "He headed straight for a" | | 18 | "She reached the turn as" | | 19 | "He offered it to the" |
| | ratio | 0.304 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 112 | | matches | | 0 | "The rain lashed the streets" | | 1 | "Detective Harlow Quinn fixed her" | | 2 | "The man clutched his side" | | 3 | "Water from the downpour drenched" | | 4 | "Quinn followed without hesitation." | | 5 | "Her closely cropped salt-and-pepper hair" | | 6 | "She kept one hand near" | | 7 | "The suspect navigated the crowded" | | 8 | "Quinn matched his movements." | | 9 | "Her military precision allowed her" | | 10 | "A taxi roared past and" | | 11 | "She skirted the edge without" | | 12 | "The suspect shouted at a" | | 13 | "Quinn heard the words clearly" | | 14 | "She calculated her next move." | | 15 | "An alley opened to her" | | 16 | "She took it to cut" | | 17 | "Her boots pounded against the" | | 18 | "The leather watch on her" | | 19 | "She burst back onto the" |
| | ratio | 0.857 | |
| 44.64% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 112 | | matches | | 0 | "Now she faced the choice." |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn fixed her brown eyes on the fleeing figure who had just exited The Raven's Nest." |
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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 | |