| 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 | 1400 | | 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) | |
| 53.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1400 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "warmth" | | 1 | "pulsed" | | 2 | "pulse" | | 3 | "familiar" | | 4 | "flickered" | | 5 | "whisper" | | 6 | "vibrated" | | 7 | "could feel" |
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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 | 164 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 172 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1400 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1373 | | uniqueNames | 13 | | maxNameDensity | 1.09 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 2 | | Park | 1 | | Carter | 2 | | Golden | 1 | | Empress | 1 | | Eva | 5 | | Aurora | 15 | | Rory | 1 | | Laila | 1 | | Hel | 1 | | Dymas | 2 | | London | 2 | | Evan | 2 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Aurora" | | 3 | "Rory" | | 4 | "Laila" | | 5 | "Dymas" | | 6 | "Evan" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Hel" | | 3 | "London" |
| | globalScore | 0.954 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like someone waiting for a friend" | | 1 | "sounded like knuckles against a door" |
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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 | 1400 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 172 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 22.58 | | std | 21.18 | | cv | 0.938 | | sampleLengths | | 0 | 25 | | 1 | 32 | | 2 | 88 | | 3 | 51 | | 4 | 59 | | 5 | 93 | | 6 | 3 | | 7 | 50 | | 8 | 12 | | 9 | 15 | | 10 | 16 | | 11 | 10 | | 12 | 23 | | 13 | 6 | | 14 | 1 | | 15 | 37 | | 16 | 1 | | 17 | 23 | | 18 | 17 | | 19 | 2 | | 20 | 22 | | 21 | 3 | | 22 | 52 | | 23 | 48 | | 24 | 44 | | 25 | 13 | | 26 | 36 | | 27 | 8 | | 28 | 24 | | 29 | 54 | | 30 | 13 | | 31 | 6 | | 32 | 3 | | 33 | 7 | | 34 | 3 | | 35 | 39 | | 36 | 3 | | 37 | 15 | | 38 | 6 | | 39 | 31 | | 40 | 7 | | 41 | 39 | | 42 | 39 | | 43 | 33 | | 44 | 10 | | 45 | 3 | | 46 | 13 | | 47 | 2 | | 48 | 40 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 215 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 172 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 348 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.022988505747126436 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 172 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 172 | | mean | 8.14 | | std | 5.51 | | cv | 0.677 | | sampleLengths | | 0 | 21 | | 1 | 4 | | 2 | 14 | | 3 | 11 | | 4 | 7 | | 5 | 4 | | 6 | 23 | | 7 | 19 | | 8 | 9 | | 9 | 19 | | 10 | 14 | | 11 | 23 | | 12 | 13 | | 13 | 15 | | 14 | 4 | | 15 | 10 | | 16 | 8 | | 17 | 22 | | 18 | 4 | | 19 | 11 | | 20 | 9 | | 21 | 21 | | 22 | 23 | | 23 | 12 | | 24 | 4 | | 25 | 8 | | 26 | 6 | | 27 | 10 | | 28 | 3 | | 29 | 17 | | 30 | 5 | | 31 | 7 | | 32 | 15 | | 33 | 6 | | 34 | 5 | | 35 | 7 | | 36 | 3 | | 37 | 2 | | 38 | 10 | | 39 | 4 | | 40 | 10 | | 41 | 2 | | 42 | 10 | | 43 | 2 | | 44 | 8 | | 45 | 12 | | 46 | 1 | | 47 | 6 | | 48 | 1 | | 49 | 5 |
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| 40.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2441860465116279 | | totalSentences | 172 | | uniqueOpeners | 42 | |
| 67.57% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 148 | | matches | | 0 | "Then warmth bloomed under the" | | 1 | "Pale pink, cream, blue, colours" | | 2 | "Bright blue eyes, black hair" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 148 | | matches | | 0 | "It stayed cold until she" | | 1 | "She checked the time." | | 2 | "She counted those sounds, the" | | 3 | "She stepped around them." | | 4 | "She looked like someone waiting" | | 5 | "She moved deeper." | | 6 | "It sounded like knuckles against" | | 7 | "She looked up." | | 8 | "It had shape." | | 9 | "It had a person in" | | 10 | "She stepped backward until her" | | 11 | "Her phone screen showed 10:58." | | 12 | "She had entered the park" | | 13 | "Her voice came out lower" | | 14 | "She used to know how" | | 15 | "Their stems did not bend" | | 16 | "They opened on a schedule" | | 17 | "She stepped away, but another" | | 18 | "She had thought she could" | | 19 | "It wore branches and mist," |
| | ratio | 0.257 | |
| 24.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 129 | | totalSentences | 148 | | matches | | 0 | "The gate to Richmond Park" | | 1 | "Aurora Carter slipped through." | | 2 | "The pendant was cold against" | | 3 | "It stayed cold until she" | | 4 | "She checked the time." | | 5 | "The note from the courier" | | 6 | "Eva’s writing slanted left at" | | 7 | "The envelope smelled of star" | | 8 | "Someone had left it with" | | 9 | "Aurora had eaten, waited an" | | 10 | "She counted those sounds, the" | | 11 | "The air smelled wet." | | 12 | "Leaf mould held a sweetness" | | 13 | "Wildflowers grew in the grass" | | 14 | "She stepped around them." | | 15 | "The flowers leaned toward the" | | 16 | "Aurora caught her own reflection" | | 17 | "She looked like someone waiting" | | 18 | "That was the plan." | | 19 | "She moved deeper." |
| | ratio | 0.872 | |
| 33.78% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 148 | | matches | | 0 | "By the time the barking" |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 1 | | matches | | 0 | "Pale pink, cream, blue, colours that belonged in a window box or a painting, not a frosty park at half past ten." |
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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 | |