| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 83.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2168 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "sharply" | | 1 | "quickly" | | 2 | "very" | | 3 | "completely" | | 4 | "slowly" |
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| 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) | |
| 51.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2168 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "pulsed" | | 1 | "shattered" | | 2 | "throbbed" | | 3 | "pulse" | | 4 | "flickered" | | 5 | "silence" | | 6 | "warmth" | | 7 | "footsteps" | | 8 | "perfect" | | 9 | "resolved" | | 10 | "stomach" | | 11 | "lurched" | | 12 | "lilt" | | 13 | "stark" |
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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 | 242 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 242 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 248 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 16 | | totalWords | 2168 | | ratio | 0.007 | | matches | | 0 | "Bring it to the grove at midnight on the solstice. The path opens only then." | | 1 | "Ror-y." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 2116 | | uniqueNames | 15 | | maxNameDensity | 0.71 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 2 | | Park | 2 | | Carter | 1 | | November | 1 | | Heartstone | 2 | | Pendant | 1 | | Cardiff | 2 | | Eva | 3 | | Celtic | 1 | | Evan | 2 | | London | 1 | | Cardiff-bred | 1 | | Welsh | 1 | | Aurora | 15 | | One | 3 |
| | persons | | 0 | "Carter" | | 1 | "Heartstone" | | 2 | "Pendant" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Aurora" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "November" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 166 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like her own name stretched across" | | 1 | "felt like minutes" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.923 | | wordCount | 2168 | | matches | | 0 | "not like sand but like skin flinching from contact" | | 1 | "Not bleeding, but yawning wider, revealing darkness beneath the skin rather th" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 248 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 28.53 | | std | 17.42 | | cv | 0.611 | | sampleLengths | | 0 | 65 | | 1 | 58 | | 2 | 53 | | 3 | 59 | | 4 | 47 | | 5 | 56 | | 6 | 46 | | 7 | 23 | | 8 | 33 | | 9 | 47 | | 10 | 34 | | 11 | 5 | | 12 | 23 | | 13 | 8 | | 14 | 50 | | 15 | 18 | | 16 | 37 | | 17 | 9 | | 18 | 44 | | 19 | 6 | | 20 | 33 | | 21 | 42 | | 22 | 43 | | 23 | 3 | | 24 | 6 | | 25 | 44 | | 26 | 22 | | 27 | 36 | | 28 | 18 | | 29 | 9 | | 30 | 51 | | 31 | 3 | | 32 | 44 | | 33 | 19 | | 34 | 3 | | 35 | 20 | | 36 | 38 | | 37 | 53 | | 38 | 4 | | 39 | 8 | | 40 | 38 | | 41 | 3 | | 42 | 5 | | 43 | 44 | | 44 | 2 | | 45 | 27 | | 46 | 16 | | 47 | 65 | | 48 | 31 | | 49 | 35 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 242 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 362 | | matches | | 0 | "were studying" | | 1 | "were using" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 248 | | ratio | 0 | | matches | (empty) | |
| 78.23% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1534 | | adjectiveStacks | 1 | | stackExamples | | 0 | "stubborn Cardiff-bred refusal" |
| | adverbCount | 89 | | adverbRatio | 0.058018252933507174 | | lyAdverbCount | 23 | | lyAdverbRatio | 0.01499348109517601 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 248 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 248 | | mean | 8.74 | | std | 5.11 | | cv | 0.584 | | sampleLengths | | 0 | 15 | | 1 | 16 | | 2 | 8 | | 3 | 26 | | 4 | 9 | | 5 | 12 | | 6 | 17 | | 7 | 12 | | 8 | 8 | | 9 | 10 | | 10 | 22 | | 11 | 21 | | 12 | 10 | | 13 | 13 | | 14 | 10 | | 15 | 13 | | 16 | 3 | | 17 | 10 | | 18 | 7 | | 19 | 9 | | 20 | 21 | | 21 | 10 | | 22 | 9 | | 23 | 24 | | 24 | 15 | | 25 | 8 | | 26 | 12 | | 27 | 8 | | 28 | 11 | | 29 | 2 | | 30 | 13 | | 31 | 6 | | 32 | 9 | | 33 | 3 | | 34 | 5 | | 35 | 5 | | 36 | 2 | | 37 | 11 | | 38 | 15 | | 39 | 8 | | 40 | 12 | | 41 | 7 | | 42 | 11 | | 43 | 3 | | 44 | 6 | | 45 | 9 | | 46 | 4 | | 47 | 10 | | 48 | 11 | | 49 | 2 |
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| 42.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.2862903225806452 | | totalSentences | 248 | | uniqueOpeners | 71 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 14 | | totalSentences | 218 | | matches | | 0 | "Only the wind moved through" | | 1 | "All blooming in defiant, mad" | | 2 | "Only a foxglove swaying though" | | 3 | "Just cold stone pretending to" | | 4 | "More like fabric dragging across" | | 5 | "Then the wildflowers began to" | | 6 | "Then her right." | | 7 | "Then directly behind her." | | 8 | "Only the slow crunch of" | | 9 | "Even the wind died." | | 10 | "Too many points of contact." | | 11 | "Only normal oaks stood in" | | 12 | "Then deleted it again from" | | 13 | "Then powered the phone off" |
| | ratio | 0.064 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 65 | | totalSentences | 218 | | matches | | 0 | "She slipped the Heartstone Pendant" | | 1 | "Her boots crunched over frost" | | 2 | "She rubbed it absently and" | | 3 | "She had come here seeking" | | 4 | "She stopped at the center" | | 5 | "She pulled out her phone." | | 6 | "She lifted it by the" | | 7 | "She whipped toward it." | | 8 | "Its purple bells hung heavy," | | 9 | "Her voice sounded muffled, as" | | 10 | "She tried again, louder." | | 11 | "She crouched and touched the" | | 12 | "It felt warm, almost feverish." | | 13 | "She jerked her hand back." | | 14 | "She stood too quickly." | | 15 | "She placed her hand on" | | 16 | "It yielded like flesh, then" | | 17 | "Her heart slammed against her" | | 18 | "She forced her breathing to" | | 19 | "She studied the monoliths again." |
| | ratio | 0.298 | |
| 60.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 174 | | totalSentences | 218 | | matches | | 0 | "The moon hung low over" | | 1 | "Aurora Carter parked her scooter" | | 2 | "The city’s distant hum had" | | 3 | "She slipped the Heartstone Pendant" | | 4 | "The crimson gem pulsed once" | | 5 | "The unknown benefactor’s note had" | | 6 | "The path opens only then.*" | | 7 | "Desperation had a way of" | | 8 | "Her boots crunched over frost" | | 9 | "The small crescent scar on" | | 10 | "She rubbed it absently and" | | 11 | "The air here carried the" | | 12 | "Bluebells, foxgloves, meadowsweet." | | 13 | "Aurora stepped between the first" | | 14 | "The temperature dropped so sharply" | | 15 | "The pendant flared once, bright" | | 16 | "The wrongness settled over her" | | 17 | "She had come here seeking" | | 18 | "The ones that showed her" | | 19 | "The dreams always ended with" |
| | ratio | 0.798 | |
| 22.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 218 | | matches | | | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 96 | | technicalSentenceCount | 3 | | matches | | 0 | "She rubbed it absently and kept walking, following the faint pull that tugged at her sternum whenever the pendant grew hotter." | | 1 | "Her voice sounded muffled, as though the grove had stuffed cotton in her ears." | | 2 | "A single wildflower where her palm had been now lay crushed, its stem leaking sap that smelled of iron." |
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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 | |