| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 47 | | tagDensity | 0.234 | | leniency | 0.468 | | rawRatio | 0.091 | | effectiveRatio | 0.043 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1174 | | 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) | |
| 87.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1174 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulse" | | 1 | "weight" | | 2 | "standard" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 708 | | uniqueNames | 6 | | maxNameDensity | 1.41 | | worstName | "Tanner" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tanner" | | discoveredNames | | Buck | 1 | | Street | 1 | | Tube | 1 | | Ollie | 1 | | Tanner | 10 | | Quinn | 4 |
| | persons | | 0 | "Buck" | | 1 | "Street" | | 2 | "Ollie" | | 3 | "Tanner" | | 4 | "Quinn" |
| | places | (empty) | | globalScore | 0.794 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1174 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 22.58 | | std | 17.28 | | cv | 0.765 | | sampleLengths | | 0 | 48 | | 1 | 32 | | 2 | 17 | | 3 | 3 | | 4 | 14 | | 5 | 26 | | 6 | 1 | | 7 | 6 | | 8 | 7 | | 9 | 9 | | 10 | 42 | | 11 | 45 | | 12 | 29 | | 13 | 1 | | 14 | 25 | | 15 | 8 | | 16 | 48 | | 17 | 20 | | 18 | 5 | | 19 | 11 | | 20 | 19 | | 21 | 5 | | 22 | 20 | | 23 | 22 | | 24 | 40 | | 25 | 14 | | 26 | 44 | | 27 | 2 | | 28 | 13 | | 29 | 54 | | 30 | 3 | | 31 | 43 | | 32 | 46 | | 33 | 4 | | 34 | 12 | | 35 | 52 | | 36 | 11 | | 37 | 19 | | 38 | 44 | | 39 | 58 | | 40 | 10 | | 41 | 19 | | 42 | 1 | | 43 | 26 | | 44 | 10 | | 45 | 56 | | 46 | 17 | | 47 | 32 | | 48 | 2 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 119 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 123 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 711 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.015471167369901548 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 9.54 | | std | 7.94 | | cv | 0.832 | | sampleLengths | | 0 | 21 | | 1 | 4 | | 2 | 1 | | 3 | 8 | | 4 | 14 | | 5 | 8 | | 6 | 10 | | 7 | 11 | | 8 | 3 | | 9 | 17 | | 10 | 3 | | 11 | 7 | | 12 | 6 | | 13 | 1 | | 14 | 26 | | 15 | 1 | | 16 | 6 | | 17 | 7 | | 18 | 6 | | 19 | 3 | | 20 | 22 | | 21 | 6 | | 22 | 14 | | 23 | 11 | | 24 | 7 | | 25 | 2 | | 26 | 2 | | 27 | 23 | | 28 | 2 | | 29 | 13 | | 30 | 14 | | 31 | 1 | | 32 | 2 | | 33 | 14 | | 34 | 9 | | 35 | 8 | | 36 | 3 | | 37 | 15 | | 38 | 6 | | 39 | 2 | | 40 | 2 | | 41 | 7 | | 42 | 6 | | 43 | 5 | | 44 | 2 | | 45 | 3 | | 46 | 17 | | 47 | 5 | | 48 | 2 | | 49 | 9 |
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| 75.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4796747967479675 | | totalSentences | 123 | | uniqueOpeners | 59 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 62.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 71 | | matches | | 0 | "She pulled the suit over" | | 1 | "He walked her through the" | | 2 | "His face was turned three" | | 3 | "She leaned closer." | | 4 | "His fingers lay relaxed beneath" | | 5 | "She lifted the disc with" | | 6 | "She bagged the token and" | | 7 | "She followed his arm." | | 8 | "She stopped at the northern" | | 9 | "He nodded at the tunnel" | | 10 | "She pointed to the arm" | | 11 | "She skirted the salt and" | | 12 | "She snapped a glove off" | | 13 | "She stood and studied the" | | 14 | "She lifted a wrist and" | | 15 | "She opened a hand at" | | 16 | "She turned to the tunnel" | | 17 | "She photographed it, bagged it," | | 18 | "She held the bag to" | | 19 | "Its casing was a crust" |
| | ratio | 0.394 | |
| 16.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 71 | | matches | | 0 | "The service stair behind the" | | 1 | "Quinn counted the landings." | | 2 | "The handrail iced her palm" | | 3 | "Scene-of-crime lights threw hard white" | | 4 | "Uniforms clustered by the old" | | 5 | "A paramedic leaned on a" | | 6 | "Nobody needed her." | | 7 | "DC Ollie Tanner met Quinn" | | 8 | "She pulled the suit over" | | 9 | "The cuffs hung past her" | | 10 | "Tanner shrugged inside his own" | | 11 | "He walked her through the" | | 12 | "The platform stretched long and" | | 13 | "The body lay on the" | | 14 | "Charcoal wool suit, cut close," | | 15 | "His face was turned three" | | 16 | "The tiles around him carried" | | 17 | "Ozone hung over the man," | | 18 | "She leaned closer." | | 19 | "A bone disc rested on" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.021 | | leniency | 0.043 | | rawRatio | 0 | | effectiveRatio | 0 | |