| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 956 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 73.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 956 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "could feel" | | 1 | "warmth" | | 2 | "pulse" |
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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 | 63 | | matches | (empty) | |
| 97.51% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 956 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 51.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 761 | | uniqueNames | 5 | | maxNameDensity | 1.97 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Isolde | 9 | | Aurora | 15 | | Heartstone | 2 | | Richmond | 1 | | Nyx | 4 |
| | persons | | 0 | "Isolde" | | 1 | "Aurora" | | 2 | "Heartstone" | | 3 | "Nyx" |
| | places | | | globalScore | 0.514 | | windowScore | 0.833 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite a heartbeat, more like the rise and fall of breath in a sleeping animal" |
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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 | 956 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 32.97 | | std | 24.46 | | cv | 0.742 | | sampleLengths | | 0 | 60 | | 1 | 12 | | 2 | 52 | | 3 | 12 | | 4 | 47 | | 5 | 20 | | 6 | 74 | | 7 | 30 | | 8 | 6 | | 9 | 11 | | 10 | 54 | | 11 | 27 | | 12 | 2 | | 13 | 87 | | 14 | 8 | | 15 | 16 | | 16 | 57 | | 17 | 39 | | 18 | 22 | | 19 | 72 | | 20 | 5 | | 21 | 19 | | 22 | 62 | | 23 | 20 | | 24 | 36 | | 25 | 69 | | 26 | 10 | | 27 | 14 | | 28 | 13 |
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| 94.12% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 63 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 122 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 74 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 762 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.031496062992125984 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0026246719160104987 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 12.92 | | std | 8.11 | | cv | 0.628 | | sampleLengths | | 0 | 15 | | 1 | 24 | | 2 | 5 | | 3 | 16 | | 4 | 7 | | 5 | 5 | | 6 | 8 | | 7 | 29 | | 8 | 11 | | 9 | 4 | | 10 | 12 | | 11 | 16 | | 12 | 9 | | 13 | 22 | | 14 | 11 | | 15 | 9 | | 16 | 14 | | 17 | 28 | | 18 | 32 | | 19 | 10 | | 20 | 12 | | 21 | 8 | | 22 | 6 | | 23 | 11 | | 24 | 7 | | 25 | 28 | | 26 | 19 | | 27 | 3 | | 28 | 24 | | 29 | 2 | | 30 | 9 | | 31 | 6 | | 32 | 14 | | 33 | 11 | | 34 | 7 | | 35 | 18 | | 36 | 22 | | 37 | 8 | | 38 | 10 | | 39 | 6 | | 40 | 7 | | 41 | 30 | | 42 | 6 | | 43 | 14 | | 44 | 11 | | 45 | 28 | | 46 | 18 | | 47 | 4 | | 48 | 14 | | 49 | 15 |
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| 79.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5 | | totalSentences | 74 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "They were oak, not granite." | | 1 | "It had been cold all" | | 2 | "Her heartbeat slowed, then sped," | | 3 | "It did not dim or" | | 4 | "It thickened, like honey poured" | | 5 | "She pressed her palm against" | | 6 | "It was warm, and under" | | 7 | "Its bed was lined with" | | 8 | "It ran against its own" | | 9 | "She felt the Heartstone flare" | | 10 | "She did not look up." | | 11 | "She had walked perhaps two" | | 12 | "Her watch read a little" |
| | ratio | 0.26 | |
| 30.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 50 | | matches | | 0 | "The standing stones rose out" | | 1 | "Aurora counted nine before she" | | 2 | "They were oak, not granite." | | 3 | "Aurora shoved her hands into" | | 4 | "The Heartstone sat against her" | | 5 | "It had been cold all" | | 6 | "Isolde's silver hair moved in" | | 7 | "The grass under her bare" | | 8 | "Aurora watched the seer step" | | 9 | "The shade had come along" | | 10 | "The voice seemed to come" | | 11 | "Isolde stepped between two of" | | 12 | "The gap between them shivered," | | 13 | "Her heartbeat slowed, then sped," | | 14 | "The light changed the moment" | | 15 | "It did not dim or" | | 16 | "It thickened, like honey poured" | | 17 | "Wildflowers covered the ground in" | | 18 | "Some were blue as a" | | 19 | "Others were the pale green" |
| | ratio | 0.86 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 50 | | matches | | 0 | "Now it was not." | | 1 | "Now they unspooled from the" |
| | ratio | 0.04 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "Now they unspooled from the base of the nearest stone and stood solid, roughly six foot two, a figure cut from ink and held together by something that was almos…" | | 1 | "She felt the Heartstone flare against her breastbone, a sudden bloom of heat that made her breath catch." |
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| 58.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 2 | | matches | | 0 | "said Isolde, without turning" | | 1 | "Aurora said, quieter" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 2 | | fancyTags | | 0 | "Nyx whispered (whisper)" | | 1 | "Isolde corrected (correct)" |
| | dialogueSentences | 27 | | tagDensity | 0.519 | | leniency | 1 | | rawRatio | 0.143 | | effectiveRatio | 0.143 | |