| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.519 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1223 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 2 | "gently" |
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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) | |
| 67.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1223 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "footfall" | | 1 | "echo" | | 2 | "absolutely" | | 3 | "pulse" | | 4 | "pulsed" | | 5 | "weight" | | 6 | "silence" |
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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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 31 | | totalWords | 1223 | | ratio | 0.025 | | matches | | 0 | "Grove. Now. Please don't tell anyone. Bring the stone." | | 1 | "Hiya, it's Eva, leave it and I'll get back to you—" | | 2 | "Roro" | | 3 | "Don't answer it. Whatever it says. Don't say your name." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 1 | | matches | | 0 | "A deer, she told herself, or a fox, or a bit of park doing park things." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1117 | | uniqueNames | 15 | | maxNameDensity | 1.16 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 13 | | Pembroke | 1 | | Lodge | 1 | | Golden | 1 | | Empress | 1 | | Kingston | 1 | | Heartstone | 1 | | November | 1 | | Eva | 9 | | Aurora | 1 | | Cardiff | 2 | | Ror | 1 | | Silence | 1 | | Evan | 1 | | London | 1 |
| | persons | | | places | | 0 | "Pembroke" | | 1 | "Golden" | | 2 | "Kingston" | | 3 | "November" | | 4 | "Cardiff" | | 5 | "London" |
| | globalScore | 0.918 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like holding a rolled newspaper ag" |
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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 | 1223 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 22.24 | | std | 20.54 | | cv | 0.924 | | sampleLengths | | 0 | 54 | | 1 | 6 | | 2 | 9 | | 3 | 16 | | 4 | 71 | | 5 | 2 | | 6 | 7 | | 7 | 41 | | 8 | 58 | | 9 | 29 | | 10 | 10 | | 11 | 60 | | 12 | 16 | | 13 | 1 | | 14 | 30 | | 15 | 2 | | 16 | 51 | | 17 | 11 | | 18 | 40 | | 19 | 7 | | 20 | 31 | | 21 | 58 | | 22 | 5 | | 23 | 7 | | 24 | 53 | | 25 | 14 | | 26 | 21 | | 27 | 60 | | 28 | 41 | | 29 | 1 | | 30 | 2 | | 31 | 33 | | 32 | 11 | | 33 | 5 | | 34 | 9 | | 35 | 44 | | 36 | 5 | | 37 | 14 | | 38 | 9 | | 39 | 4 | | 40 | 9 | | 41 | 30 | | 42 | 4 | | 43 | 43 | | 44 | 4 | | 45 | 4 | | 46 | 73 | | 47 | 11 | | 48 | 6 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 134 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 177 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 145 | | ratio | 0.007 | | matches | | 0 | "*Hiya, it's Eva, leave it and I'll get back to you—*" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1125 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.03111111111111111 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0026666666666666666 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 8.43 | | std | 7.27 | | cv | 0.862 | | sampleLengths | | 0 | 28 | | 1 | 10 | | 2 | 16 | | 3 | 6 | | 4 | 1 | | 5 | 1 | | 6 | 4 | | 7 | 3 | | 8 | 16 | | 9 | 11 | | 10 | 22 | | 11 | 9 | | 12 | 1 | | 13 | 1 | | 14 | 11 | | 15 | 16 | | 16 | 2 | | 17 | 7 | | 18 | 3 | | 19 | 10 | | 20 | 2 | | 21 | 2 | | 22 | 16 | | 23 | 8 | | 24 | 3 | | 25 | 27 | | 26 | 2 | | 27 | 17 | | 28 | 9 | | 29 | 8 | | 30 | 10 | | 31 | 2 | | 32 | 9 | | 33 | 10 | | 34 | 10 | | 35 | 16 | | 36 | 20 | | 37 | 6 | | 38 | 1 | | 39 | 1 | | 40 | 4 | | 41 | 2 | | 42 | 16 | | 43 | 1 | | 44 | 9 | | 45 | 5 | | 46 | 4 | | 47 | 2 | | 48 | 10 | | 49 | 2 |
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| 61.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4 | | totalSentences | 145 | | uniqueOpeners | 58 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 101 | | matches | | 0 | "Somewhere past ninety, she noticed" | | 1 | "Then something white slid across" | | 2 | "Then it repeated, closer." | | 3 | "Then eleven forty-one." | | 4 | "Then a string of numbers" |
| | ratio | 0.05 | |
| 77.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 101 | | matches | | 0 | "She had clocked off from" | | 1 | "She counted her steps to" | | 2 | "Her voice came out flat" | | 3 | "She walked on and did" | | 4 | "They stood close enough that" | | 5 | "She reached for the pendant" | | 6 | "she muttered, and pulled her" | | 7 | "They formed an arch she" | | 8 | "She lifted the phone." | | 9 | "She stepped through the stones" | | 10 | "She shook the phone as" | | 11 | "She thumbed Eva's contact and" | | 12 | "She lowered the phone." | | 13 | "She kept the beam on" | | 14 | "She snapped the torch around." | | 15 | "Her pulse thudded in her" | | 16 | "She bent and picked up" | | 17 | "It felt like nothing." | | 18 | "It felt like holding a" | | 19 | "She held on to it" |
| | ratio | 0.356 | |
| 78.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 101 | | matches | | 0 | "The park gates had closed" | | 1 | "The bag still held the" | | 2 | "She had clocked off from" | | 3 | "Eva's text glowed on her" | | 4 | "Rory pushed through bracken that" | | 5 | "The path gave out after" | | 6 | "She counted her steps to" | | 7 | "Each footfall landed, and a" | | 8 | "The second step stopped a" | | 9 | "Her voice came out flat" | | 10 | "A deer, she told herself," | | 11 | "She walked on and did" | | 12 | "The oaks thickened." | | 13 | "They stood close enough that" | | 14 | "The quiet had a texture," | | 15 | "She reached for the pendant" | | 16 | "The Heartstone lay against her" | | 17 | "she muttered, and pulled her" | | 18 | "The standing stones rose out" | | 19 | "They formed an arch she" |
| | ratio | 0.762 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 101 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "A ring of flowers lay pressed flat in the grass, a wide circle, as if something large had knelt there and stayed a long time." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Rory whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.222 | | effectiveRatio | 0.148 | |