| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "a woman said very [very]" |
| | dialogueSentences | 5 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 51.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 924 | | totalAiIsmAdverbs | 9 | | found | | | highlights | | 0 | "utterly" | | 1 | "very" | | 2 | "slowly" | | 3 | "suddenly" | | 4 | "softly" |
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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) | |
| 45.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 924 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "could feel" | | 1 | "warmth" | | 2 | "measured" | | 3 | "echo" | | 4 | "pulsed" | | 5 | "tracing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 80 | | matches | (empty) | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 25 | | totalWords | 924 | | ratio | 0.027 | | matches | | 0 | "Come alone. Come before the turn of the night." | | 1 | "11:52" | | 2 | "Fine" | | 3 | "Fine. Just find the stone, see who's waiting, leave." | | 4 | "Don't give it the satisfaction." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 905 | | uniqueNames | 7 | | maxNameDensity | 0.55 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 5 | | Richmond | 1 | | London | 1 | | Heathrow | 1 | | Heartstone | 2 | | February | 1 | | Eva | 1 |
| | persons | | 0 | "Rory" | | 1 | "Heartstone" | | 2 | "Eva" |
| | places | | 0 | "Richmond" | | 1 | "London" | | 2 | "Heathrow" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "something close to bone, but it was warm, the wa" |
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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 | 924 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 30.8 | | std | 20.07 | | cv | 0.652 | | sampleLengths | | 0 | 60 | | 1 | 45 | | 2 | 67 | | 3 | 32 | | 4 | 65 | | 5 | 36 | | 6 | 12 | | 7 | 52 | | 8 | 2 | | 9 | 35 | | 10 | 5 | | 11 | 52 | | 12 | 12 | | 13 | 39 | | 14 | 22 | | 15 | 39 | | 16 | 36 | | 17 | 23 | | 18 | 59 | | 19 | 56 | | 20 | 8 | | 21 | 5 | | 22 | 15 | | 23 | 9 | | 24 | 44 | | 25 | 19 | | 26 | 4 | | 27 | 23 | | 28 | 7 | | 29 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 33.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 160 | | matches | | 0 | "was standing" | | 1 | "was telling" | | 2 | "was tracing" | | 3 | "was beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 81 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 133 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.03007518796992481 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.007518796992481203 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 11.41 | | std | 8.77 | | cv | 0.769 | | sampleLengths | | 0 | 33 | | 1 | 5 | | 2 | 11 | | 3 | 11 | | 4 | 13 | | 5 | 21 | | 6 | 11 | | 7 | 2 | | 8 | 35 | | 9 | 8 | | 10 | 22 | | 11 | 10 | | 12 | 22 | | 13 | 10 | | 14 | 24 | | 15 | 3 | | 16 | 10 | | 17 | 18 | | 18 | 5 | | 19 | 31 | | 20 | 3 | | 21 | 1 | | 22 | 8 | | 23 | 11 | | 24 | 18 | | 25 | 23 | | 26 | 2 | | 27 | 6 | | 28 | 12 | | 29 | 17 | | 30 | 5 | | 31 | 4 | | 32 | 24 | | 33 | 5 | | 34 | 19 | | 35 | 6 | | 36 | 6 | | 37 | 7 | | 38 | 15 | | 39 | 17 | | 40 | 3 | | 41 | 1 | | 42 | 18 | | 43 | 13 | | 44 | 1 | | 45 | 1 | | 46 | 24 | | 47 | 7 | | 48 | 24 | | 49 | 5 |
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| 65.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4444444444444444 | | totalSentences | 81 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 70 | | matches | | 0 | "Only the wind moving through" | | 1 | "Then she had lifted the" | | 2 | "Instead the air smelled of" | | 3 | "Just find the stone, see" | | 4 | "Then it turned, unhurried, and" | | 5 | "Somewhere to her left, a" | | 6 | "Once, low and clear, the" | | 7 | "Only oaks, and the bracken," | | 8 | "Then, at the very edge" | | 9 | "Too loud on the damp" |
| | ratio | 0.143 | |
| 94.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 70 | | matches | | 0 | "She stopped at the first" | | 1 | "She took her hand back" | | 2 | "She had told herself it" | | 3 | "She could feel it through" | | 4 | "It was February." | | 5 | "She took out her phone." | | 6 | "She had walked perhaps twenty" | | 7 | "It was a deer, she" | | 8 | "It had no face." | | 9 | "It watched her without moving." | | 10 | "She started again, shallow and" | | 11 | "She turned slowly." | | 12 | "She did not look straight" | | 13 | "She kept her eyes on" | | 14 | "She had brought a thing" | | 15 | "She started to walk back" | | 16 | "Her steps sounded wrong." | | 17 | "She did not turn around." | | 18 | "Her voice came out thin" | | 19 | "It did not carry." |
| | ratio | 0.314 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 70 | | matches | | 0 | "The gate Rory had climbed" | | 1 | "The city's hum had gone." | | 2 | "She stopped at the first" | | 3 | "The bark had been weathered" | | 4 | "She took her hand back" | | 5 | "She had told herself it" | | 6 | "The pendant sat against her" | | 7 | "She could feel it through" | | 8 | "The clearing opened ahead of" | | 9 | "Wildflowers covered the ground in" | | 10 | "It was February." | | 11 | "There should have been frost" | | 12 | "She took out her phone." | | 13 | "The grove was larger than" | | 14 | "She had walked perhaps twenty" | | 15 | "It was a deer, she" | | 16 | "A roe, maybe, a young" | | 17 | "Rory let out a breath" | | 18 | "The deer lifted its head." | | 19 | "It had no face." |
| | ratio | 0.714 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 46.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 5 | | matches | | 0 | "Where its eyes should have been, the skin simply continued, smooth and grey, stretched over bone that was too long and too finely shaped." | | 1 | "She kept her eyes on the middle distance, the way her mother had taught her to watch a dog that wasn't sure of you." | | 2 | "She had brought a thing that answered to something under the grass, and now it was telling her it was close." | | 3 | "Her left wrist burned, the old crescent scar pulling tight as though something was tracing it with a fingernail." | | 4 | "Rory's hand closed around the pendant, and it was hot now, almost painful, and the grass at her feet was beginning, very slowly, to bend away from her, as if so…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |