| 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 | 951 | | 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) | |
| 63.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 951 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "fractured" | | 2 | "mosaic" | | 3 | "raced" | | 4 | "echo" | | 5 | "footsteps" | | 6 | "measured" |
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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 | 70 | | matches | (empty) | |
| 81.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 70 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 951 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 951 | | uniqueNames | 12 | | maxNameDensity | 1.05 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 10 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Morris | 1 | | Camden | 2 | | Tube | 1 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Camden" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Market" |
| | places | | | globalScore | 0.974 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared ahead and the man vaulted it his landing unsteady on the grass beyond" |
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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 | 951 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 70 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 43.23 | | std | 25.57 | | cv | 0.592 | | sampleLengths | | 0 | 61 | | 1 | 60 | | 2 | 3 | | 3 | 8 | | 4 | 13 | | 5 | 55 | | 6 | 52 | | 7 | 7 | | 8 | 60 | | 9 | 63 | | 10 | 15 | | 11 | 7 | | 12 | 56 | | 13 | 53 | | 14 | 41 | | 15 | 56 | | 16 | 70 | | 17 | 106 | | 18 | 56 | | 19 | 48 | | 20 | 38 | | 21 | 23 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 70 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 956 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02510460251046025 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0031380753138075313 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 70 | | echoCount | 0 | | echoWords | (empty) | |
| 96.79% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 70 | | mean | 13.59 | | std | 5.33 | | cv | 0.392 | | sampleLengths | | 0 | 11 | | 1 | 14 | | 2 | 20 | | 3 | 16 | | 4 | 9 | | 5 | 16 | | 6 | 15 | | 7 | 20 | | 8 | 3 | | 9 | 8 | | 10 | 3 | | 11 | 10 | | 12 | 11 | | 13 | 3 | | 14 | 20 | | 15 | 21 | | 16 | 14 | | 17 | 11 | | 18 | 12 | | 19 | 15 | | 20 | 7 | | 21 | 11 | | 22 | 19 | | 23 | 11 | | 24 | 19 | | 25 | 7 | | 26 | 23 | | 27 | 18 | | 28 | 15 | | 29 | 9 | | 30 | 6 | | 31 | 7 | | 32 | 11 | | 33 | 12 | | 34 | 16 | | 35 | 17 | | 36 | 8 | | 37 | 14 | | 38 | 12 | | 39 | 19 | | 40 | 12 | | 41 | 9 | | 42 | 20 | | 43 | 11 | | 44 | 17 | | 45 | 12 | | 46 | 16 | | 47 | 7 | | 48 | 11 | | 49 | 9 |
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| 70.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.44285714285714284 | | totalSentences | 70 | | uniqueOpeners | 31 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Then tell me what happened" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 70 | | matches | | 0 | "Her frame cut through the" | | 1 | "She leaped over a fallen" | | 2 | "They raced along a narrow" | | 3 | "He grabbed a metal lid" | | 4 | "He darted between two cars" | | 5 | "Her brown eyes never left" | | 6 | "You think this is just" | | 7 | "He laughed once a short" | | 8 | "His path angled north the" | | 9 | "She cleared the barrier in" | | 10 | "He wrenched free and spilled" | | 11 | "He reached the boarded entrance" | | 12 | "He pressed the token to" | | 13 | "She wedged her shoulder against" | | 14 | "She glanced back up the" | | 15 | "Her fingers tightened on the" |
| | ratio | 0.229 | |
| 17.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 70 | | matches | | 0 | "Harlow Quinn burst from the" | | 1 | "The green neon sign buzzed" | | 2 | "Her frame cut through the" | | 3 | "Water streamed down her closely" | | 4 | "The worn leather watch on" | | 5 | "The suspect stayed twenty yards" | | 6 | "The man twisted his head" | | 7 | "This ends with you in" | | 8 | "Quinn lengthened her stride and" | | 9 | "Puddles exploded underfoot." | | 10 | "Neon reflections from nearby signs" | | 11 | "She leaped over a fallen" | | 12 | "They raced along a narrow" | | 13 | "The walls pressed close and" | | 14 | "He grabbed a metal lid" | | 15 | "The disc spun through the" | | 16 | "The suspect snarled over his" | | 17 | "The alley spilled them onto" | | 18 | "He darted between two cars" | | 19 | "Quinn waited one heartbeat then" |
| | ratio | 0.886 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 2 | | matches | | 0 | "Stalls lined the platform of the old station vendors calling out in low voices over tables laden with glowing vials and artifacts that hummed with unseen energy…" | | 1 | "The dangers felt immediate in the press of bodies and the gleam of items that should not exist." |
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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 | |