| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 36 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1329 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1329 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "gloom" | | 2 | "mosaic" | | 3 | "trembled" | | 4 | "pulsed" | | 5 | "silence" | | 6 | "flickered" |
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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 | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 208 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1329 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1003 | | uniqueNames | 6 | | maxNameDensity | 3.09 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 31 | | Quinn | 1 | | Tube | 1 | | Miller | 20 | | Eva | 19 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Eva" | | 4 | "Kowalski" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | 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 | 1329 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 208 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 18.46 | | std | 11.25 | | cv | 0.61 | | sampleLengths | | 0 | 53 | | 1 | 35 | | 2 | 20 | | 3 | 20 | | 4 | 38 | | 5 | 14 | | 6 | 36 | | 7 | 11 | | 8 | 32 | | 9 | 13 | | 10 | 31 | | 11 | 30 | | 12 | 6 | | 13 | 37 | | 14 | 1 | | 15 | 20 | | 16 | 28 | | 17 | 4 | | 18 | 9 | | 19 | 46 | | 20 | 32 | | 21 | 8 | | 22 | 13 | | 23 | 31 | | 24 | 26 | | 25 | 22 | | 26 | 19 | | 27 | 19 | | 28 | 5 | | 29 | 8 | | 30 | 17 | | 31 | 9 | | 32 | 32 | | 33 | 16 | | 34 | 25 | | 35 | 32 | | 36 | 9 | | 37 | 13 | | 38 | 14 | | 39 | 12 | | 40 | 8 | | 41 | 13 | | 42 | 8 | | 43 | 30 | | 44 | 6 | | 45 | 8 | | 46 | 8 | | 47 | 26 | | 48 | 23 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 184 | | matches | | |
| 75.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 214 | | matches | | 0 | "wasn't pointing" | | 1 | "were glowing" | | 2 | "wasn't coming" | | 3 | "was spinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 208 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1004 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.018924302788844622 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00597609561752988 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 208 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 208 | | mean | 6.39 | | std | 3.49 | | cv | 0.546 | | sampleLengths | | 0 | 15 | | 1 | 18 | | 2 | 5 | | 3 | 15 | | 4 | 5 | | 5 | 14 | | 6 | 6 | | 7 | 10 | | 8 | 12 | | 9 | 8 | | 10 | 3 | | 11 | 8 | | 12 | 9 | | 13 | 3 | | 14 | 9 | | 15 | 9 | | 16 | 9 | | 17 | 8 | | 18 | 5 | | 19 | 5 | | 20 | 4 | | 21 | 10 | | 22 | 5 | | 23 | 21 | | 24 | 11 | | 25 | 7 | | 26 | 9 | | 27 | 7 | | 28 | 9 | | 29 | 3 | | 30 | 10 | | 31 | 16 | | 32 | 5 | | 33 | 10 | | 34 | 4 | | 35 | 8 | | 36 | 5 | | 37 | 7 | | 38 | 6 | | 39 | 6 | | 40 | 21 | | 41 | 16 | | 42 | 1 | | 43 | 6 | | 44 | 3 | | 45 | 11 | | 46 | 15 | | 47 | 6 | | 48 | 7 | | 49 | 4 |
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| 37.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 26 | | diversityRatio | 0.2644230769230769 | | totalSentences | 208 | | uniqueOpeners | 55 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 148 | | matches | (empty) | | ratio | 0 | |
| 22.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 73 | | totalSentences | 148 | | matches | | 0 | "She switched on her torch." | | 1 | "They were chipped, grey with" | | 2 | "He spat a dark wad" | | 3 | "His voice sounded flat against" | | 4 | "She walked past the victim" | | 5 | "Her leather watch caught the" | | 6 | "She crouched and tapped the" | | 7 | "She reached for the wrist." | | 8 | "He held up a notebook." | | 9 | "It gleamed with a dirty" | | 10 | "She grabbed a pair of" | | 11 | "His boots scuffed the grime." | | 12 | "She saw the way he" | | 13 | "He gripped it too tight." | | 14 | "He looked at the compass," | | 15 | "He knew more than he" | | 16 | "He didn't move." | | 17 | "She pulled out a leather" | | 18 | "It was worn, cracked near" | | 19 | "She had been standing there" |
| | ratio | 0.493 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 142 | | totalSentences | 148 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "The heavy gate clanged against" | | 2 | "The smell hit her first." | | 3 | "She switched on her torch." | | 4 | "The beam cut through the" | | 5 | "They were chipped, grey with" | | 6 | "DS Miller leaned against the" | | 7 | "He spat a dark wad" | | 8 | "His voice sounded flat against" | | 9 | "Harlow ignored him." | | 10 | "She walked past the victim" | | 11 | "Her leather watch caught the" | | 12 | "She crouched and tapped the" | | 13 | "The fabric of the coat" | | 14 | "She reached for the wrist." | | 15 | "The skin was blue." | | 16 | "Miller pushed off the barrier" | | 17 | "He held up a notebook." | | 18 | "Harlow pointed her torch at" | | 19 | "The hand was open, fingers" |
| | ratio | 0.959 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 148 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 1 | | matches | | 0 | "The heavy gate clanged against the platform walls, a final thud that echoed through the abandoned Tube station." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 36 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 25 | | fancyCount | 4 | | fancyTags | | 0 | "Harlow ordered (order)" | | 1 | "Eva whispered (whisper)" | | 2 | "Miller barked (bark)" | | 3 | "Miller yelled (yell)" |
| | dialogueSentences | 60 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.16 | | effectiveRatio | 0.133 | |