| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 2 | | adverbTags | | 0 | "She bit back [back]" | | 1 | "He leaned back [back]" |
| | dialogueSentences | 70 | | tagDensity | 0.343 | | leniency | 0.686 | | rawRatio | 0.083 | | effectiveRatio | 0.057 | |
| 76.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1043 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "suddenly" | | 1 | "perfectly" | | 2 | "really" | | 3 | "slowly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 23.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1043 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "familiar" | | 1 | "chaotic" | | 2 | "unreadable" | | 3 | "weight" | | 4 | "eyebrow" | | 5 | "flicker" | | 6 | "whisper" | | 7 | "warmth" | | 8 | "simmering" | | 9 | "tension" | | 10 | "reminder" | | 11 | "silence" | | 12 | "intensity" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "clenched her jaw" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 80 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1040 | | ratio | 0.003 | | matches | | 0 | "didn’t" | | 1 | "running" | | 2 | "here" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 664 | | uniqueNames | 9 | | maxNameDensity | 1.51 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Rory | 10 | | November | 1 | | Silas | 5 | | Sarcasm | 1 | | Spymaster | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Sarcasm" | | 6 | "Spymaster" |
| | places | (empty) | | globalScore | 0.747 | | windowScore | 0.833 | |
| 47.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 2 | | matches | | 0 | "something akin to old paper pulling her inside" | | 1 | "felt like fact" |
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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 | 1040 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 16.51 | | std | 13.65 | | cv | 0.827 | | sampleLengths | | 0 | 67 | | 1 | 58 | | 2 | 50 | | 3 | 1 | | 4 | 25 | | 5 | 5 | | 6 | 21 | | 7 | 11 | | 8 | 16 | | 9 | 30 | | 10 | 4 | | 11 | 2 | | 12 | 27 | | 13 | 17 | | 14 | 40 | | 15 | 1 | | 16 | 18 | | 17 | 20 | | 18 | 18 | | 19 | 32 | | 20 | 3 | | 21 | 14 | | 22 | 17 | | 23 | 24 | | 24 | 16 | | 25 | 20 | | 26 | 22 | | 27 | 3 | | 28 | 21 | | 29 | 17 | | 30 | 22 | | 31 | 12 | | 32 | 5 | | 33 | 26 | | 34 | 14 | | 35 | 9 | | 36 | 24 | | 37 | 2 | | 38 | 31 | | 39 | 7 | | 40 | 15 | | 41 | 37 | | 42 | 17 | | 43 | 13 | | 44 | 17 | | 45 | 11 | | 46 | 21 | | 47 | 5 | | 48 | 9 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 120 | | ratio | 0.008 | | matches | | 0 | "A man occupied a booth near the back, nursing a drink—a booth Rory hadn't seen occupied during peak hours in months." |
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| 99.47% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 665 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.0406015037593985 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.016541353383458645 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 8.67 | | std | 5.54 | | cv | 0.639 | | sampleLengths | | 0 | 14 | | 1 | 8 | | 2 | 22 | | 3 | 23 | | 4 | 15 | | 5 | 4 | | 6 | 6 | | 7 | 6 | | 8 | 6 | | 9 | 21 | | 10 | 12 | | 11 | 8 | | 12 | 17 | | 13 | 7 | | 14 | 6 | | 15 | 1 | | 16 | 4 | | 17 | 6 | | 18 | 15 | | 19 | 5 | | 20 | 10 | | 21 | 11 | | 22 | 8 | | 23 | 3 | | 24 | 10 | | 25 | 6 | | 26 | 5 | | 27 | 13 | | 28 | 12 | | 29 | 4 | | 30 | 2 | | 31 | 6 | | 32 | 15 | | 33 | 6 | | 34 | 6 | | 35 | 7 | | 36 | 4 | | 37 | 25 | | 38 | 10 | | 39 | 5 | | 40 | 1 | | 41 | 8 | | 42 | 10 | | 43 | 13 | | 44 | 7 | | 45 | 7 | | 46 | 11 | | 47 | 4 | | 48 | 24 | | 49 | 4 |
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| 56.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.39166666666666666 | | totalSentences | 120 | | uniqueOpeners | 47 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 78 | | matches | | 0 | "Just a nod, a small" | | 1 | "Too perfectly, maybe" |
| | ratio | 0.026 | |
| 30.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 78 | | matches | | 0 | "She clocked out an hour" | | 1 | "He knew her schedule." | | 2 | "She preferred it that way," | | 3 | "He was broader now, shoulders" | | 4 | "He lifted his head." | | 5 | "She didn't move, her hand" | | 6 | "It felt weak, automatic" | | 7 | "He swirled the amber liquid" | | 8 | "She walked towards the bar," | | 9 | "He laughed, a dry, humorless" | | 10 | "He took a slow sip" | | 11 | "She hadn’t actively kept track," | | 12 | "He finally met her gaze," | | 13 | "He gestured to Silas" | | 14 | "He arched an eyebrow" | | 15 | "She hadn't spoken about it" | | 16 | "He leaned forward, his voice" | | 17 | "Her tone brooked no argument" | | 18 | "She felt Silas’s eyes on" | | 19 | "She regretted the sharpness immediately." |
| | ratio | 0.474 | |
| 4.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 78 | | matches | | 0 | "The green neon sign of" | | 1 | "Rain feathered the glass, distorting" | | 2 | "Rory wiped condensation from the" | | 3 | "She clocked out an hour" | | 4 | "Silas, leaning against the burnished" | | 5 | "He knew her schedule." | | 6 | "She preferred it that way," | | 7 | "Tonight, though, the bar wasn't" | | 8 | "A man occupied a booth" | | 9 | "He was broader now, shoulders" | | 10 | "The hair, once a chaotic" | | 11 | "He lifted his head." | | 12 | "Recognition sparked, followed by something" | | 13 | "A ghost of a smile" | | 14 | "She didn't move, her hand" | | 15 | "The air thickened, suddenly heavy," | | 16 | "It felt weak, automatic" | | 17 | "A stock phrase." | | 18 | "He swirled the amber liquid" | | 19 | "A long pause" |
| | ratio | 0.91 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 3 | | matches | | 0 | "It felt, automatic" | | 1 | "He leaned, his voice dropping to a near whisper" | | 2 | "he asked, his voice quiet" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 0 | | effectiveRatio | 0 | |