| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1119 | | 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) | |
| 59.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1119 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "pulsed" | | 1 | "pulse" | | 2 | "footsteps" | | 3 | "silence" | | 4 | "echo" | | 5 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | 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 | 1122 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "Birch trunks, she told herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 1018 | | uniqueNames | 9 | | maxNameDensity | 0.29 | | worstName | "Richmond" | | maxWindowNameDensity | 1 | | worstWindowName | "Richmond" | | discoveredNames | | Richmond | 3 | | Park | 1 | | Yu-Fei | 3 | | Golden | 1 | | Empress | 1 | | Heathrow | 1 | | December | 1 | | Cheung | 1 | | Rory | 3 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Cheung" | | 2 | "Rory" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "December" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1122 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 26.09 | | std | 23.32 | | cv | 0.894 | | sampleLengths | | 0 | 26 | | 1 | 18 | | 2 | 66 | | 3 | 32 | | 4 | 44 | | 5 | 57 | | 6 | 9 | | 7 | 65 | | 8 | 7 | | 9 | 44 | | 10 | 36 | | 11 | 14 | | 12 | 31 | | 13 | 4 | | 14 | 18 | | 15 | 17 | | 16 | 20 | | 17 | 13 | | 18 | 8 | | 19 | 29 | | 20 | 13 | | 21 | 23 | | 22 | 41 | | 23 | 6 | | 24 | 7 | | 25 | 41 | | 26 | 114 | | 27 | 38 | | 28 | 2 | | 29 | 18 | | 30 | 6 | | 31 | 1 | | 32 | 45 | | 33 | 25 | | 34 | 3 | | 35 | 68 | | 36 | 8 | | 37 | 3 | | 38 | 11 | | 39 | 59 | | 40 | 9 | | 41 | 13 | | 42 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 105 | | matches | | 0 | "being acknowledged" | | 1 | "been expected" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 159 | | matches | | |
| 93.17% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 115 | | ratio | 0.017 | | matches | | 0 | "The second set of footsteps stopped — late, catching up to its own silence." | | 1 | "Richmond oaks — squat, split, familiar as bus stops — gave way to pillars." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1024 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.02734375 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001953125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.76 | | std | 6.77 | | cv | 0.694 | | sampleLengths | | 0 | 26 | | 1 | 18 | | 2 | 8 | | 3 | 15 | | 4 | 2 | | 5 | 2 | | 6 | 9 | | 7 | 4 | | 8 | 26 | | 9 | 13 | | 10 | 19 | | 11 | 5 | | 12 | 26 | | 13 | 5 | | 14 | 3 | | 15 | 5 | | 16 | 8 | | 17 | 20 | | 18 | 10 | | 19 | 19 | | 20 | 6 | | 21 | 3 | | 22 | 24 | | 23 | 17 | | 24 | 3 | | 25 | 21 | | 26 | 7 | | 27 | 10 | | 28 | 10 | | 29 | 2 | | 30 | 14 | | 31 | 4 | | 32 | 4 | | 33 | 24 | | 34 | 12 | | 35 | 7 | | 36 | 7 | | 37 | 6 | | 38 | 4 | | 39 | 7 | | 40 | 14 | | 41 | 4 | | 42 | 3 | | 43 | 15 | | 44 | 12 | | 45 | 5 | | 46 | 6 | | 47 | 14 | | 48 | 5 | | 49 | 8 |
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| 73.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4824561403508772 | | totalSentences | 114 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 90 | | matches | | 0 | "Then flowers she had no" | | 1 | "Somewhere off to the left" | | 2 | "Then a voice, agreeable as" | | 3 | "Once, bright, obscene in all" |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 90 | | matches | | 0 | "She wheeled her bike through" | | 1 | "Her pocket had been warm" | | 2 | "She fished out the pendant," | | 3 | "she told it" | | 4 | "She locked the bike to" | | 5 | "She made herself watch the" | | 6 | "It broke off mid-phrase, clipped," | | 7 | "She dialled the number on" | | 8 | "It connected before the first" | | 9 | "Her voice came out smaller" | | 10 | "Her mobile buzzed in her" | | 11 | "She zipped her jacket to" | | 12 | "She held the phone over" | | 13 | "Her breath fogged white and" | | 14 | "She had not brought it." | | 15 | "She had not touched it." | | 16 | "Her arrival was being acknowledged," | | 17 | "She drew it out and" | | 18 | "She looked up." | | 19 | "They were birch trunks." |
| | ratio | 0.256 | |
| 82.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 90 | | matches | | 0 | "The padlock on the Richmond" | | 1 | "Rory gave the words to" | | 2 | "The order had come in" | | 3 | "The note in the app" | | 4 | "Rent was due, Yu-Fei had" | | 5 | "She wheeled her bike through" | | 6 | "Frost stiffened every blade, crackling" | | 7 | "Richmond lay under the stack" | | 8 | "Tonight, nothing moved up there." | | 9 | "The sky looked painted on." | | 10 | "Her pocket had been warm" | | 11 | "She fished out the pendant," | | 12 | "Here it ticked against her" | | 13 | "she told it" | | 14 | "She locked the bike to" | | 15 | "Crocuses stood open in the" | | 16 | "She made herself watch the" | | 17 | "The second set of footsteps" | | 18 | "The echo took four." | | 19 | "It broke off mid-phrase, clipped," |
| | ratio | 0.756 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 3 | | matches | | 0 | "She fished out the pendant, and the little crimson stone pulsed through her glove, dimming and brightening, dimming and brightening." | | 1 | "Wildflowers ran to the edges of a hidden clearing, every head turned toward its centre, waiting the way an audience waits." | | 2 | "She drew it out and its glow threw red up the crescent scar on her wrist, pulsing, keeping time." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Rory gave, and the road gave nothing back" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "her phone agreed (agree)" |
| | dialogueSentences | 17 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |