| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1681 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "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) | |
| 82.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1681 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "warmth" | | 1 | "flickered" | | 2 | "lilt" | | 3 | "pulsed" | | 4 | "vibrated" | | 5 | "footsteps" |
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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 | 230 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 230 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 237 | | 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 | 1675 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1650 | | uniqueNames | 12 | | maxNameDensity | 1.7 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 3 | | Park | 3 | | Heartstone | 5 | | Hel | 2 | | Yu-Fei | 2 | | Aurora | 28 | | Richmond | 4 | | Evan | 1 | | Cardiff | 1 | | Jennifer | 3 | | One | 4 | | Click | 3 |
| | persons | | 0 | "Carter" | | 1 | "Park" | | 2 | "Heartstone" | | 3 | "Yu-Fei" | | 4 | "Aurora" | | 5 | "Evan" | | 6 | "Jennifer" | | 7 | "Click" |
| | places | | 0 | "Hel" | | 1 | "Richmond" | | 2 | "Cardiff" | | 3 | "One" |
| | globalScore | 0.652 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 128 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.597 | | wordCount | 1675 | | matches | | 0 | "not like a lamp but like blood beneath skin" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 237 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 148 | | mean | 11.32 | | std | 12.36 | | cv | 1.092 | | sampleLengths | | 0 | 11 | | 1 | 50 | | 2 | 5 | | 3 | 50 | | 4 | 7 | | 5 | 48 | | 6 | 8 | | 7 | 29 | | 8 | 4 | | 9 | 4 | | 10 | 40 | | 11 | 6 | | 12 | 7 | | 13 | 20 | | 14 | 16 | | 15 | 2 | | 16 | 4 | | 17 | 6 | | 18 | 12 | | 19 | 5 | | 20 | 3 | | 21 | 42 | | 22 | 3 | | 23 | 19 | | 24 | 6 | | 25 | 2 | | 26 | 16 | | 27 | 11 | | 28 | 9 | | 29 | 2 | | 30 | 5 | | 31 | 3 | | 32 | 61 | | 33 | 7 | | 34 | 11 | | 35 | 29 | | 36 | 3 | | 37 | 2 | | 38 | 15 | | 39 | 3 | | 40 | 12 | | 41 | 7 | | 42 | 3 | | 43 | 23 | | 44 | 3 | | 45 | 26 | | 46 | 4 | | 47 | 4 | | 48 | 24 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 230 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 291 | | matches | (empty) | |
| 94.64% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 237 | | ratio | 0.017 | | matches | | 0 | "Wildflowers crowded the gaps between them—bluebells, foxgloves, small white blooms she could not name—though winter had stripped every other part of the park bare." | | 1 | "It came again—three notes, thin and rising." | | 2 | "Their colours shifted when she looked at them—violet became blue, blue became white, white became the yellow of old teeth." | | 3 | "She counted stones as she passed them—one, two, three—though she had never seen stones there before." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1659 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.02531645569620253 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0054249547920434 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 237 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 237 | | mean | 7.07 | | std | 5.05 | | cv | 0.715 | | sampleLengths | | 0 | 11 | | 1 | 13 | | 2 | 7 | | 3 | 7 | | 4 | 5 | | 5 | 18 | | 6 | 5 | | 7 | 21 | | 8 | 5 | | 9 | 24 | | 10 | 7 | | 11 | 11 | | 12 | 10 | | 13 | 27 | | 14 | 8 | | 15 | 12 | | 16 | 17 | | 17 | 4 | | 18 | 4 | | 19 | 20 | | 20 | 20 | | 21 | 6 | | 22 | 7 | | 23 | 5 | | 24 | 7 | | 25 | 3 | | 26 | 5 | | 27 | 7 | | 28 | 1 | | 29 | 1 | | 30 | 7 | | 31 | 2 | | 32 | 4 | | 33 | 3 | | 34 | 3 | | 35 | 12 | | 36 | 5 | | 37 | 3 | | 38 | 14 | | 39 | 3 | | 40 | 25 | | 41 | 3 | | 42 | 7 | | 43 | 3 | | 44 | 3 | | 45 | 6 | | 46 | 6 | | 47 | 2 | | 48 | 6 | | 49 | 10 |
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| 39.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.23628691983122363 | | totalSentences | 237 | | uniqueOpeners | 56 | |
| 81.70% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 204 | | matches | | 0 | "Even the wind had dropped," | | 1 | "Then a twig snapped behind" | | 2 | "Only the flowers marked its" | | 3 | "Then another answered." | | 4 | "Then the smell of wet" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 204 | | matches | | 0 | "She stopped at the boundary." | | 1 | "Its deep crimson surface gave" | | 2 | "She had followed it here" | | 3 | "She had left her bicycle" | | 4 | "It came again—three notes, thin" | | 5 | "She lowered the torch." | | 6 | "Her voice travelled only a" | | 7 | "She looked back." | | 8 | "It had not moved with" | | 9 | "Her hand closed around the" | | 10 | "Its warmth strengthened." | | 11 | "She kept walking." | | 12 | "She placed each foot carefully," | | 13 | "She knew it from Evan’s" | | 14 | "She passed a crooked oak" | | 15 | "She noticed it only after" | | 16 | "It curled around the branch," | | 17 | "She tapped it against her" | | 18 | "It began at the edge" | | 19 | "Her mouth dried." |
| | ratio | 0.265 | |
| 28.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 176 | | totalSentences | 204 | | matches | | 0 | "Aurora Carter reached the standing" | | 1 | "Richmond Park lay behind her," | | 2 | "The city had vanished beyond" | | 3 | "She stopped at the boundary." | | 4 | "The stones rose from the" | | 5 | "Oak roots gripped their bases." | | 6 | "Wildflowers crowded the gaps between" | | 7 | "Aurora touched the pendant beneath" | | 8 | "The Heartstone rested against her" | | 9 | "Its deep crimson surface gave" | | 10 | "She had followed it here" | | 11 | "Aurora had not asked how" | | 12 | "The woman had only gripped" | | 13 | "Aurora had gone alone." | | 14 | "She had left her bicycle" | | 15 | "The beam now shone between" | | 16 | "The path led into the" | | 17 | "A bird called somewhere beyond" | | 18 | "Aurora turned towards the sound." | | 19 | "It came again—three notes, thin" |
| | ratio | 0.863 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 204 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "it repeated (repeat)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.6 | | effectiveRatio | 0.5 | |