| 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 | 1664 | | 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) | |
| 66.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1664 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echo" | | 1 | "warmth" | | 2 | "flickered" | | 3 | "vibrated" | | 4 | "measured" | | 5 | "silence" | | 6 | "throbbed" | | 7 | "pulse" | | 8 | "footfall" | | 9 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 237 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 237 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 263 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1662 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.34% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1597 | | uniqueNames | 15 | | maxNameDensity | 1.75 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 2 | | Park | 1 | | Thames | 1 | | Eva | 6 | | Cardiff | 1 | | London | 1 | | Heartstone | 5 | | June | 1 | | Aurora | 28 | | Met | 1 | | Carter | 1 | | Dad | 2 | | Click | 3 | | Three | 3 | | Knock | 3 |
| | persons | | 0 | "Eva" | | 1 | "Heartstone" | | 2 | "Aurora" | | 3 | "Carter" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Thames" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 0.623 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 126 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared beneath a blank notification, without passing through any app she recognised" | | 1 | "appeared washed in grey" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.602 | | wordCount | 1662 | | matches | | 0 | "not enough to burn, but close to skin temperature" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 263 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 183 | | mean | 9.08 | | std | 9.39 | | cv | 1.034 | | sampleLengths | | 0 | 12 | | 1 | 42 | | 2 | 5 | | 3 | 9 | | 4 | 24 | | 5 | 36 | | 6 | 23 | | 7 | 44 | | 8 | 8 | | 9 | 8 | | 10 | 5 | | 11 | 6 | | 12 | 3 | | 13 | 27 | | 14 | 13 | | 15 | 3 | | 16 | 46 | | 17 | 12 | | 18 | 8 | | 19 | 5 | | 20 | 12 | | 21 | 1 | | 22 | 3 | | 23 | 42 | | 24 | 6 | | 25 | 8 | | 26 | 5 | | 27 | 48 | | 28 | 2 | | 29 | 23 | | 30 | 5 | | 31 | 5 | | 32 | 12 | | 33 | 5 | | 34 | 4 | | 35 | 1 | | 36 | 5 | | 37 | 5 | | 38 | 6 | | 39 | 5 | | 40 | 5 | | 41 | 4 | | 42 | 8 | | 43 | 8 | | 44 | 3 | | 45 | 10 | | 46 | 10 | | 47 | 2 | | 48 | 5 | | 49 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 237 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 271 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 263 | | ratio | 0.004 | | matches | | 0 | "Even London’s distant pressure—the aircraft, tyres, engines and lives grinding beyond the park—had vanished." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1604 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 41 | | adverbRatio | 0.025561097256857856 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018703241895261845 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 263 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 263 | | mean | 6.32 | | std | 4.89 | | cv | 0.774 | | sampleLengths | | 0 | 12 | | 1 | 16 | | 2 | 26 | | 3 | 5 | | 4 | 9 | | 5 | 2 | | 6 | 4 | | 7 | 3 | | 8 | 15 | | 9 | 2 | | 10 | 3 | | 11 | 15 | | 12 | 16 | | 13 | 23 | | 14 | 10 | | 15 | 14 | | 16 | 3 | | 17 | 7 | | 18 | 10 | | 19 | 8 | | 20 | 8 | | 21 | 5 | | 22 | 6 | | 23 | 2 | | 24 | 1 | | 25 | 7 | | 26 | 6 | | 27 | 14 | | 28 | 13 | | 29 | 3 | | 30 | 6 | | 31 | 15 | | 32 | 17 | | 33 | 8 | | 34 | 12 | | 35 | 8 | | 36 | 2 | | 37 | 2 | | 38 | 1 | | 39 | 12 | | 40 | 1 | | 41 | 3 | | 42 | 4 | | 43 | 3 | | 44 | 9 | | 45 | 26 | | 46 | 6 | | 47 | 5 | | 48 | 3 | | 49 | 5 |
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| 53.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3524904214559387 | | totalSentences | 261 | | uniqueOpeners | 92 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 193 | | matches | | 0 | "Even London’s distant pressure—the aircraft," | | 1 | "Instead, she stepped between the" | | 2 | "Then Aurora heard herself speak." | | 3 | "Then another, farther off." | | 4 | "Then, from farther across the" | | 5 | "Instead, a slow knocking came" |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 193 | | matches | | 0 | "Her phone read 11:47 p.m." | | 1 | "She had taken screenshots, sent" | | 2 | "Their trunks stood thick and" | | 3 | "Their branches meshed overhead like" | | 4 | "Her voice died at the" | | 5 | "She rubbed the small crescent" | | 6 | "She should have left." | | 7 | "She knew that." | | 8 | "She could walk back to" | | 9 | "Her ears popped." | | 10 | "It was June, yet foxgloves" | | 11 | "Her phone vibrated." | | 12 | "She lowered the phone." | | 13 | "Her own voice came again," | | 14 | "She opened the message thread" | | 15 | "Her screenshots remained there, followed" | | 16 | "She swung the phone torch" | | 17 | "They passed around the grove" | | 18 | "Its glow had strengthened, painting" | | 19 | "She watched the darkness between" |
| | ratio | 0.233 | |
| 71.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 150 | | totalSentences | 193 | | matches | | 0 | "The gap between the oaks" | | 1 | "Aurora checked again, pinching the" | | 2 | "The blue dot marking her" | | 3 | "Her phone read 11:47 p.m." | | 4 | "The message had told her" | | 5 | "The message had appeared beneath" | | 6 | "She had taken screenshots, sent" | | 7 | "That promise now sat somewhere" | | 8 | "A row of ancient oaks" | | 9 | "Their trunks stood thick and" | | 10 | "None bore leaves despite the" | | 11 | "Their branches meshed overhead like" | | 12 | "Aurora slipped her phone into" | | 13 | "Her voice died at the" | | 14 | "She rubbed the small crescent" | | 15 | "The Heartstone rested beneath her" | | 16 | "Aurora took out the pendant" | | 17 | "A dull red glow moved" | | 18 | "Something dark turned beneath the" | | 19 | "The glow settled." |
| | ratio | 0.777 | |
| 77.72% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 193 | | matches | | 0 | "Now heat spread from it" | | 1 | "If you stop replying, I’m" | | 2 | "Before she could touch it," |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |