| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.531 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 99.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 11317 | | 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) | |
| 88.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 11317 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "echoed" | | 1 | "reminder" | | 2 | "unreadable" | | 3 | "lilt" | | 4 | "velvet" | | 5 | "unspoken" | | 6 | "raced" | | 7 | "electric" | | 8 | "echo" | | 9 | "weight" | | 10 | "navigate" | | 11 | "glinting" | | 12 | "flicker" | | 13 | "whisper" | | 14 | "tension" | | 15 | "pawn" | | 16 | "reverberated" | | 17 | "familiar" | | 18 | "pulsed" | | 19 | "resonated" | | 20 | "flicked" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "hung in the air" | | count | 1 |
| | 2 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "hung in the air" | | 3 | "a flash of amusement" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 4857 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 5 | | narrationSentences | 4857 | | filterMatches | (empty) | | hedgeMatches | | |
| 0.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 4871 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 1 | | runOnParagraphCount | 0 | | giantParagraphCount | 1 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 10756 | | ratio | 0 | | matches | | 0 | "“We’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’ll… we’l…" |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 11317 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 5675 | | uniqueNames | 7 | | maxNameDensity | 0.26 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 15 | | French | 1 | | Lucien | 12 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | London | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Lucien" | | 2 | "Cheung" |
| | places | | | globalScore | 1 | | windowScore | 0.667 | |
| 5.77% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 3 | | matches | | 0 | "gesture that seemed to signal both caution and invitation" | | 1 | "glow that seemed to echo the demon’s presence" | | 2 | "growl that seemed to echo through the very fabric of reality" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 11317 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 4871 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 452.68 | | std | 1987.9 | | cv | 4.391 | | sampleLengths | | 0 | 78 | | 1 | 21 | | 2 | 65 | | 3 | 61 | | 4 | 41 | | 5 | 72 | | 6 | 53 | | 7 | 51 | | 8 | 62 | | 9 | 62 | | 10 | 60 | | 11 | 23 | | 12 | 27 | | 13 | 62 | | 14 | 35 | | 15 | 48 | | 16 | 34 | | 17 | 48 | | 18 | 14 | | 19 | 21 | | 20 | 61 | | 21 | 57 | | 22 | 49 | | 23 | 21 | | 24 | 10191 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 4857 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 4934 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 4871 | | ratio | 0 | | matches | | 0 | "“But this one is different. It’s not just a rogue entity; it’s a threat to the entire city. And I… I need someone who can see past the obvious, someone who can read between the lines.”" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 5675 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.0037004405286343613 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.000881057268722467 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 4871 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 4871 | | mean | 2.32 | | std | 77.06 | | cv | 33.166 | | sampleLengths | | 0 | 18 | | 1 | 35 | | 2 | 25 | | 3 | 15 | | 4 | 6 | | 5 | 20 | | 6 | 15 | | 7 | 22 | | 8 | 8 | | 9 | 18 | | 10 | 19 | | 11 | 24 | | 12 | 12 | | 13 | 23 | | 14 | 6 | | 15 | 17 | | 16 | 18 | | 17 | 8 | | 18 | 29 | | 19 | 11 | | 20 | 15 | | 21 | 14 | | 22 | 13 | | 23 | 11 | | 24 | 4 | | 25 | 36 | | 26 | 10 | | 27 | 36 | | 28 | 16 | | 29 | 14 | | 30 | 11 | | 31 | 7 | | 32 | 30 | | 33 | 8 | | 34 | 17 | | 35 | 17 | | 36 | 18 | | 37 | 6 | | 38 | 17 | | 39 | 13 | | 40 | 3 | | 41 | 11 | | 42 | 17 | | 43 | 11 | | 44 | 15 | | 45 | 19 | | 46 | 10 | | 47 | 10 | | 48 | 15 | | 49 | 17 |
| |
| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4798 | | diversityRatio | 0.0043112297269554505 | | totalSentences | 4871 | | uniqueOpeners | 21 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 56 | | matches | | 0 | "she called, voice steady but" | | 1 | "He paused at the threshold," | | 2 | "he said, his voice smooth," | | 3 | "She could almost hear the" | | 4 | "she asked, stepping forward, her" | | 5 | "She was used to being" | | 6 | "He tapped the cane against" | | 7 | "His words were sharp, but" | | 8 | "she countered, her voice low" | | 9 | "She remembered the night she" | | 10 | "she asked, her voice barely" | | 11 | "He placed a hand on" | | 12 | "He paused, letting the words" | | 13 | "She glanced at his heterochromatic" | | 14 | "she said, her voice firm" | | 15 | "He turned to leave, but" | | 16 | "She looked at Lucien, her" | | 17 | "He placed a hand on" | | 18 | "She turned to face Lucien," | | 19 | "she whispered, her voice trembling" |
| | ratio | 0.375 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 56 | | matches | | 0 | "Lucien’s cane clicked against the" | | 1 | "The thud echoed like a" | | 2 | "Aurora froze mid‑step, her hand" | | 3 | "she called, voice steady but" | | 4 | "He paused at the threshold," | | 5 | "A faint smile tugged at" | | 6 | "he said, his voice smooth," | | 7 | "The word hung in the" | | 8 | "Aurora’s mind raced, recalling the" | | 9 | "She could almost hear the" | | 10 | "she asked, stepping forward, her" | | 11 | "She was used to being" | | 12 | "Lucien lifted his cane, the" | | 13 | "He tapped the cane against" | | 14 | "His words were sharp, but" | | 15 | "Aurora’s eyes narrowed, the small" | | 16 | "she countered, her voice low" | | 17 | "Lucien’s amber eye softened, a" | | 18 | "The mention of the city’s" | | 19 | "She remembered the night she" |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 6 | | matches | | 0 | "Aurora froze mid‑step, her hand hovering over the small crescent‑shaped scar on her left wrist that had been a silent witness to a childhood accident." | | 1 | "He paused at the threshold, his amber eye flicking over her, then settling on the scar that marked her wrist." | | 2 | "Aurora’s mind raced, recalling the nights they had shared, the electric pull between them that had once felt inevitable." | | 3 | "Lucien’s smile widened, a flash of amusement that danced in his amber eye." | | 4 | "The shadows coalesced into a shape that was both terrifying and familiar, a demon that had been lurking in the shadows of London for centuries." | | 5 | "Aurora’s scar pulsed, a faint blue glow that seemed to echo the demon’s presence." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 6 | | matches | | 0 | "she countered, her voice low" | | 1 | "she asked, her voice barely a whisper" | | 2 | "she said, her voice firm" | | 3 | "she whispered, her voice trembling" | | 4 | "Aurora whispered, her voice barely audible above the roar of the demon" | | 5 | "he said, his voice steady" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 4 | | fancyTags | | 0 | "he agreed (agree)" | | 1 | "he murmured (murmur)" | | 2 | "she whispered (whisper)" | | 3 | "Aurora whispered (whisper)" |
| | dialogueSentences | 32 | | tagDensity | 0.469 | | leniency | 0.938 | | rawRatio | 0.267 | | effectiveRatio | 0.25 | |