| 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 | 357 | | 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) | |
| 15.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 357 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "echoes" | | 1 | "footsteps" | | 2 | "standard" | | 3 | "weight" | | 4 | "etched" | | 5 | "magnetic" |
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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 | 40 | | matches | (empty) | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 40 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 40 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 357 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.97% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 357 | | uniqueNames | 8 | | maxNameDensity | 1.4 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 5 | | Quinn | 1 | | Camden | 1 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Vance | 4 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Vance" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.8 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | 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 | 357 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 40 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 19.83 | | std | 16.12 | | cv | 0.813 | | sampleLengths | | 0 | 79 | | 1 | 13 | | 2 | 16 | | 3 | 15 | | 4 | 17 | | 5 | 8 | | 6 | 14 | | 7 | 16 | | 8 | 17 | | 9 | 9 | | 10 | 9 | | 11 | 26 | | 12 | 38 | | 13 | 11 | | 14 | 11 | | 15 | 20 | | 16 | 11 | | 17 | 27 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 40 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 59 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 360 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.019444444444444445 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.011111111111111112 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 40 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 40 | | mean | 8.93 | | std | 4.56 | | cv | 0.511 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 15 | | 3 | 12 | | 4 | 17 | | 5 | 13 | | 6 | 4 | | 7 | 12 | | 8 | 6 | | 9 | 5 | | 10 | 4 | | 11 | 17 | | 12 | 8 | | 13 | 4 | | 14 | 10 | | 15 | 6 | | 16 | 10 | | 17 | 13 | | 18 | 4 | | 19 | 5 | | 20 | 4 | | 21 | 9 | | 22 | 7 | | 23 | 3 | | 24 | 5 | | 25 | 11 | | 26 | 8 | | 27 | 12 | | 28 | 10 | | 29 | 8 | | 30 | 11 | | 31 | 11 | | 32 | 6 | | 33 | 7 | | 34 | 7 | | 35 | 11 | | 36 | 18 | | 37 | 3 | | 38 | 2 | | 39 | 4 |
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| 97.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.625 | | totalSentences | 40 | | uniqueOpeners | 25 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 39 | | matches | | 0 | "Ever since Morris died, you" |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 39 | | matches | | 0 | "You are seeing ghosts again." | | 1 | "She lifted it, noting the" | | 2 | "He used what he had" | | 3 | "He materialized up there." |
| | ratio | 0.103 | |
| 88.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 39 | | matches | | 0 | "Water dripped from rusted iron" | | 1 | "Detective Harlow Quinn stepped past" | | 2 | "Salt-and-pepper hair caught the harsh" | | 3 | "A worn leather watch on" | | 4 | "A body hung suspended from" | | 5 | "Footsteps crunched over ballast." | | 6 | "Inspector Vance joined her, wiping" | | 7 | "The drop snapped the neck" | | 8 | "Harlow ignored him, fixing her" | | 9 | "Vance checked his notebook, flipping" | | 10 | "Standard fraying from the knot" | | 11 | "The weight of the man" | | 12 | "Harlow approached the gantry pillar," | | 13 | "A sharp jaw tightened." | | 14 | "Someone removed them recently." | | 15 | "Vance scoffed, stepping closer to" | | 16 | "The station has been abandoned" | | 17 | "Dust settles everywhere." | | 18 | "You are seeing ghosts again." | | 19 | "Harlow kept silent, crouching beside" |
| | ratio | 0.744 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow ignored him, fixing her brown eyes on the victim's boots dangling six feet above the ground." |
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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 | |