| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1046 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "precisely" | | 1 | "slightly" | | 2 | "slowly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1046 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1051 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 18.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 723 | | uniqueNames | 14 | | maxNameDensity | 2.63 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 19 | | Raven | 1 | | Nest | 1 | | Silas | 4 | | Thursday | 1 | | October | 1 | | Golden | 1 | | Empress | 1 | | Aurora | 1 | | Christmas | 1 | | Maeve | 11 | | Cardiff | 1 | | Italy | 1 | | Evan | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "October" | | 5 | "Maeve" | | 6 | "Evan" |
| | places | | 0 | "Golden" | | 1 | "Christmas" | | 2 | "Cardiff" | | 3 | "Italy" |
| | globalScore | 0.186 | | windowScore | 0.333 | |
| 14.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 2 | | matches | | 0 | "something close to ash" | | 1 | "somewhere Rory apparently lived now" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.951 | | wordCount | 1051 | | matches | | 0 | "not fine, but it's done" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 19.11 | | std | 25.88 | | cv | 1.354 | | sampleLengths | | 0 | 64 | | 1 | 70 | | 2 | 6 | | 3 | 2 | | 4 | 3 | | 5 | 76 | | 6 | 1 | | 7 | 56 | | 8 | 3 | | 9 | 1 | | 10 | 102 | | 11 | 5 | | 12 | 3 | | 13 | 2 | | 14 | 35 | | 15 | 7 | | 16 | 3 | | 17 | 5 | | 18 | 1 | | 19 | 18 | | 20 | 5 | | 21 | 53 | | 22 | 9 | | 23 | 4 | | 24 | 1 | | 25 | 62 | | 26 | 2 | | 27 | 6 | | 28 | 39 | | 29 | 6 | | 30 | 1 | | 31 | 3 | | 32 | 1 | | 33 | 7 | | 34 | 8 | | 35 | 1 | | 36 | 28 | | 37 | 4 | | 38 | 36 | | 39 | 2 | | 40 | 1 | | 41 | 1 | | 42 | 56 | | 43 | 3 | | 44 | 9 | | 45 | 97 | | 46 | 10 | | 47 | 20 | | 48 | 4 | | 49 | 6 |
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| 84.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 50 | | matches | | 0 | "being looked" | | 1 | "being asked" | | 2 | "been addressed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 114 | | matches | | 0 | "was polishing" | | 1 | "was turning" | | 2 | "was waiting" | | 3 | "was still staring" |
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| 15.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 90 | | ratio | 0.044 | | matches | | 0 | "A Thursday in late October did that to the place — too far from weekend, too close to payday, the regulars still deciding whether to commit." | | 1 | "\"Oh thank God.\" The woman laughed, and it was the laugh Rory remembered — short, startled, more an exhale than a sound." | | 2 | "Maeve had been soft at university — round-cheeked, always slightly flushed, as if she'd just come in from a run." | | 3 | "She thought about the last time she'd seen her — graduation, a photograph taken on the lawn, both of them squinting into the sun, Maeve's arm around her shoulders, Evan just out of frame." |
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| 97.43% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 722 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.04293628808864266 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.015235457063711912 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 11.68 | | std | 11.28 | | cv | 0.966 | | sampleLengths | | 0 | 33 | | 1 | 5 | | 2 | 26 | | 3 | 12 | | 4 | 22 | | 5 | 36 | | 6 | 6 | | 7 | 2 | | 8 | 3 | | 9 | 8 | | 10 | 5 | | 11 | 9 | | 12 | 32 | | 13 | 22 | | 14 | 1 | | 15 | 22 | | 16 | 34 | | 17 | 3 | | 18 | 1 | | 19 | 6 | | 20 | 11 | | 21 | 20 | | 22 | 6 | | 23 | 38 | | 24 | 9 | | 25 | 5 | | 26 | 7 | | 27 | 5 | | 28 | 3 | | 29 | 2 | | 30 | 31 | | 31 | 4 | | 32 | 7 | | 33 | 3 | | 34 | 5 | | 35 | 1 | | 36 | 14 | | 37 | 4 | | 38 | 5 | | 39 | 32 | | 40 | 21 | | 41 | 3 | | 42 | 6 | | 43 | 4 | | 44 | 1 | | 45 | 14 | | 46 | 48 | | 47 | 2 | | 48 | 6 | | 49 | 3 |
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| 74.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4777777777777778 | | totalSentences | 90 | | uniqueOpeners | 43 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 44 | | matches | | | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 44 | | matches | | 0 | "She meant to go straight" | | 1 | "She had an essay to" | | 2 | "he said, low" | | 3 | "She looked again, properly this" | | 4 | "She wore a gold band" | | 5 | "She hadn't known about any" | | 6 | "Her father, who had opinions" | | 7 | "It was almost, she sometimes" | | 8 | "It took a moment for" | | 9 | "She looked up." | | 10 | "She thought about the last" | | 11 | "She hadn't taken it off" | | 12 | "She looked up and around" |
| | ratio | 0.295 | |
| 39.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 44 | | matches | | 0 | "The rain had been falling" | | 1 | "The bar was nearly empty." | | 2 | "A Thursday in late October" | | 3 | "She meant to go straight" | | 4 | "She had an essay to" | | 5 | "he said, low" | | 6 | "Rory set her bag down" | | 7 | "Nobody called her Aurora anymore." | | 8 | "She looked again, properly this" | | 9 | "The woman laughed, and it" | | 10 | "Rory slid onto the stool" | | 11 | "Maeve had been soft at" | | 12 | "She wore a gold band" | | 13 | "Rory hadn't known about that." | | 14 | "She hadn't known about any" | | 15 | "Maeve reached across and touched" | | 16 | "Rory turned her wrist out" | | 17 | "Silas had already poured a" | | 18 | "Rory took the wine he" | | 19 | "Maeve watched her own fingers" |
| | ratio | 0.841 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 44 | | matches | | | ratio | 0.023 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain had been falling for three hours when Rory pushed through the door of the Raven's Nest, shaking water from her jacket onto the mat Silas kept there for…" | | 1 | "Maeve had been soft at university — round-cheeked, always slightly flushed, as if she'd just come in from a run." | | 2 | "There was a hollow beneath her cheekbone that Rory had never seen before, and her hair, which used to be a riot of unbrushed copper, was cut blunt to her jaw an…" | | 3 | "Silas had already poured a second glass without being asked and was polishing a tumbler at the other end of the bar with the studied indifference of a man who h…" | | 4 | "Her father, who had opinions about everything, had learned one particular silence for the subject of his daughter's last year in Cardiff." | | 5 | "She looked up and around the bar, at the green neon bleeding through the front window, at Silas with his polishing cloth, at the stairs that led up to somewhere…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, low" | | 1 | "Rory turned, not unkindly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "The woman laughed (laugh)" |
| | dialogueSentences | 54 | | tagDensity | 0.148 | | leniency | 0.296 | | rawRatio | 0.125 | | effectiveRatio | 0.037 | |