| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 972 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 84.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 972 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "echoing" | | 1 | "porcelain" | | 2 | "footsteps" |
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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 | 1 | | narrationSentences | 72 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 80 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 9 | | totalWords | 972 | | ratio | 0.009 | | matches | | 0 | "Follow me if you dare." | | 1 | "unknown and unaccounted for." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 927 | | uniqueNames | 16 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Camden | 2 | | High | 1 | | Street | 1 | | Marcus | 1 | | Webb | 4 | | Parkway | 1 | | Morris | 4 | | Victorian | 1 | | Tube | 1 | | Town | 1 | | London | 1 | | Underground | 1 | | Met | 1 | | God | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Marcus" | | 2 | "Webb" | | 3 | "Morris" | | 4 | "Met" | | 5 | "God" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Town" | | 4 | "London" |
| | globalScore | 0.907 | | windowScore | 1 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "quite look right in the eyes, exchanging packages that never made sense for a mid-level courier" | | 1 | "looked like woven shadow argued with a cu" |
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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 | 972 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 26.27 | | std | 21.38 | | cv | 0.814 | | sampleLengths | | 0 | 17 | | 1 | 2 | | 2 | 6 | | 3 | 54 | | 4 | 66 | | 5 | 23 | | 6 | 19 | | 7 | 5 | | 8 | 48 | | 9 | 52 | | 10 | 32 | | 11 | 13 | | 12 | 38 | | 13 | 1 | | 14 | 6 | | 15 | 51 | | 16 | 68 | | 17 | 11 | | 18 | 80 | | 19 | 8 | | 20 | 42 | | 21 | 7 | | 22 | 52 | | 23 | 44 | | 24 | 23 | | 25 | 17 | | 26 | 3 | | 27 | 17 | | 28 | 7 | | 29 | 24 | | 30 | 7 | | 31 | 37 | | 32 | 42 | | 33 | 28 | | 34 | 1 | | 35 | 16 | | 36 | 5 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "been prised" | | 1 | "been trained" |
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| 80.24% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 167 | | matches | | 0 | "were telling" | | 1 | "were screaming" | | 2 | "was coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 80 | | ratio | 0 | | matches | (empty) | |
| 88.01% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 931 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 50 | | adverbRatio | 0.05370569280343716 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.00966702470461869 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 80 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 80 | | mean | 12.15 | | std | 10.23 | | cv | 0.842 | | sampleLengths | | 0 | 17 | | 1 | 2 | | 2 | 3 | | 3 | 3 | | 4 | 17 | | 5 | 30 | | 6 | 2 | | 7 | 5 | | 8 | 6 | | 9 | 30 | | 10 | 30 | | 11 | 2 | | 12 | 8 | | 13 | 13 | | 14 | 6 | | 15 | 10 | | 16 | 3 | | 17 | 5 | | 18 | 4 | | 19 | 23 | | 20 | 3 | | 21 | 18 | | 22 | 8 | | 23 | 44 | | 24 | 6 | | 25 | 21 | | 26 | 5 | | 27 | 13 | | 28 | 18 | | 29 | 15 | | 30 | 5 | | 31 | 1 | | 32 | 6 | | 33 | 4 | | 34 | 18 | | 35 | 16 | | 36 | 4 | | 37 | 9 | | 38 | 33 | | 39 | 15 | | 40 | 7 | | 41 | 13 | | 42 | 4 | | 43 | 7 | | 44 | 16 | | 45 | 22 | | 46 | 16 | | 47 | 26 | | 48 | 5 | | 49 | 3 |
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| 84.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.55 | | totalSentences | 80 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 69 | | matches | | 0 | "Then she heard it." | | 1 | "Maybe she wanted him to" | | 2 | "Of course he did." | | 3 | "Then she thought of the" | | 4 | "Somewhere ahead, footsteps splashed through" |
| | ratio | 0.072 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 69 | | matches | | 0 | "He didn't stop." | | 1 | "They never did." | | 2 | "She could hold twenty metres." | | 3 | "She rounded the corner after" | | 4 | "She jogged over." | | 5 | "She knew what her instincts" | | 6 | "He never came back up." | | 7 | "She drew her torch instead" | | 8 | "Her torch beam caught a" | | 9 | "Her voice came back flat," | | 10 | "She started down anyway." | | 11 | "It chose not to." | | 12 | "She spotted Webb near a" | | 13 | "He caught her eye across" | | 14 | "He ran again." | | 15 | "She didn't finish." | | 16 | "She didn't need to." | | 17 | "He glanced back once." | | 18 | "She thought of Morris's badge," | | 19 | "She thought of eighteen years" |
| | ratio | 0.29 | |
| 83.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 69 | | matches | | 0 | "The man's trainers slapped against" | | 1 | "He didn't stop." | | 2 | "They never did." | | 3 | "Rain came down in sheets," | | 4 | "Quinn ducked around a wheelie" | | 5 | "She could hold twenty metres." | | 6 | "Tonight he'd finally slipped, met" | | 7 | "The fear of a man" | | 8 | "She rounded the corner after" | | 9 | "A metal grate clanging somewhere" | | 10 | "She jogged over." | | 11 | "The board had been prised" | | 12 | "Quinn had been a detective" | | 13 | "She knew what her instincts" | | 14 | "Morris had followed instinct once" | | 15 | "He never came back up." | | 16 | "She drew her torch instead" | | 17 | "The passage beyond smelled of" | | 18 | "Her torch beam caught a" | | 19 | "Water trickled somewhere below, echoing." |
| | ratio | 0.754 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "Three weeks of surveillance logs, of grainy footage showing him meeting men who didn't quite look right in the eyes, exchanging packages that never made sense f…" | | 1 | "Lanterns hung from the old signal wires, casting warm light over a market that shouldn't exist." | | 2 | "She spotted Webb near a stall selling small bone tokens, speaking fast to a hooded figure who passed him something wrapped in newspaper." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 1 | | effectiveRatio | 0.167 | |