| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1079 | | 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) | |
| 72.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1079 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "throb" | | 1 | "pulse" | | 2 | "flicker" | | 3 | "warmth" | | 4 | "perfect" |
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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 | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 139 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "started to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1079 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1034 | | uniqueNames | 11 | | maxNameDensity | 0.77 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Click" | | discoveredNames | | Park | 1 | | Heartstone | 2 | | Pendant | 1 | | November | 2 | | Priory | 1 | | Eva | 5 | | Rory | 8 | | Yu-Fei | 1 | | Cheung | 1 | | Silas | 1 | | Click | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Pendant" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Yu-Fei" | | 5 | "Cheung" | | 6 | "Silas" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.927 | | wordCount | 1079 | | matches | | 0 | "Not a shape appearing but the absence of shape rearranging" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 144 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 19.62 | | std | 15.68 | | cv | 0.799 | | sampleLengths | | 0 | 10 | | 1 | 21 | | 2 | 38 | | 3 | 31 | | 4 | 2 | | 5 | 25 | | 6 | 13 | | 7 | 14 | | 8 | 15 | | 9 | 54 | | 10 | 3 | | 11 | 34 | | 12 | 39 | | 13 | 6 | | 14 | 48 | | 15 | 24 | | 16 | 4 | | 17 | 7 | | 18 | 74 | | 19 | 29 | | 20 | 5 | | 21 | 27 | | 22 | 10 | | 23 | 9 | | 24 | 31 | | 25 | 15 | | 26 | 6 | | 27 | 28 | | 28 | 21 | | 29 | 3 | | 30 | 2 | | 31 | 28 | | 32 | 48 | | 33 | 30 | | 34 | 3 | | 35 | 7 | | 36 | 44 | | 37 | 19 | | 38 | 4 | | 39 | 8 | | 40 | 31 | | 41 | 12 | | 42 | 10 | | 43 | 30 | | 44 | 14 | | 45 | 4 | | 46 | 24 | | 47 | 3 | | 48 | 16 | | 49 | 2 |
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| 97.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 139 | | matches | | 0 | "being asked" | | 1 | "was dented" | | 2 | "were spaced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 144 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 413 | | adjectiveStacks | 2 | | stackExamples | | 0 | "Thumbnail-small, deep crimson," | | 1 | "grown over deep cuts." |
| | adverbCount | 9 | | adverbRatio | 0.021791767554479417 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 7.49 | | std | 5.49 | | cv | 0.733 | | sampleLengths | | 0 | 10 | | 1 | 4 | | 2 | 6 | | 3 | 11 | | 4 | 9 | | 5 | 5 | | 6 | 13 | | 7 | 11 | | 8 | 10 | | 9 | 7 | | 10 | 14 | | 11 | 2 | | 12 | 2 | | 13 | 5 | | 14 | 18 | | 15 | 13 | | 16 | 8 | | 17 | 2 | | 18 | 4 | | 19 | 5 | | 20 | 10 | | 21 | 7 | | 22 | 2 | | 23 | 22 | | 24 | 4 | | 25 | 2 | | 26 | 8 | | 27 | 9 | | 28 | 3 | | 29 | 13 | | 30 | 1 | | 31 | 2 | | 32 | 18 | | 33 | 3 | | 34 | 15 | | 35 | 4 | | 36 | 6 | | 37 | 2 | | 38 | 9 | | 39 | 6 | | 40 | 4 | | 41 | 19 | | 42 | 12 | | 43 | 13 | | 44 | 3 | | 45 | 5 | | 46 | 7 | | 47 | 9 | | 48 | 4 | | 49 | 2 |
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| 43.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.3541666666666667 | | totalSentences | 144 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 117 | | matches | | 0 | "Somewhere far behind her a" | | 1 | "Just a dropped pin in" | | 2 | "Just a note in plain" | | 3 | "Too warm for November." | | 4 | "Then again, further to her" | | 5 | "Bright blue eyes wide, not" | | 6 | "Too long an inhale." | | 7 | "Too long a pause before" | | 8 | "Just an inch." | | 9 | "Then the breathing started again," |
| | ratio | 0.085 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 117 | | matches | | 0 | "Her torch threw a thin" | | 1 | "She tugged the silver chain" | | 2 | "Her voice sounded wrong in" | | 3 | "It did not carry." | | 4 | "She let the pendant drop." | | 5 | "It knocked against her jacket" | | 6 | "She flinched and her left" | | 7 | "She had come because Eva" | | 8 | "She had come because the" | | 9 | "She had come because the" | | 10 | "She had not stayed away." | | 11 | "She snapped the beam toward" | | 12 | "Her breath came out white" | | 13 | "She pushed it back with" | | 14 | "Hers was short, through her" | | 15 | "She yanked the chain over" | | 16 | "Her shadow had no head." | | 17 | "She looked down." | | 18 | "Her shadow on the flower" | | 19 | "It hit the soft ground" |
| | ratio | 0.197 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 117 | | matches | | 0 | "Richmond Park after dark bore" | | 1 | "The gate stood open." | | 2 | "Rory pushed through and the" | | 3 | "Her torch threw a thin" | | 4 | "Mud sucked at her trainers." | | 5 | "The trees crowded close on" | | 6 | "She tugged the silver chain" | | 7 | "The Heartstone Pendant sat in" | | 8 | "A steady, low throb of" | | 9 | "Her voice sounded wrong in" | | 10 | "It did not carry." | | 11 | "She let the pendant drop." | | 12 | "It knocked against her jacket" | | 13 | "Eva had sent the location" | | 14 | "Rory knew that oak." | | 15 | "The place the runners avoided" | | 16 | "The place where the city" | | 17 | "The path ended." | | 18 | "The air changed." | | 19 | "The wind that had tugged" |
| | ratio | 0.718 | |
| 85.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 117 | | matches | | 0 | "As if something large had" | | 1 | "As if whatever lay there" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 1 | | matches | | 0 | "A steady, low throb of heat against her skin, in rhythm with something that was not her pulse." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |