| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 78 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 92.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 2 | | 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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 98.45% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 1 | | maxSentenceWordsSeen | 72 | | ratio | 0.007 | | matches | | 0 | "\"You didn't answer. You didn't answer me, you didn't answer Sarah, you didn't answer anyone. We thought, Christ, Rory, we thought the worst.\"" |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1268 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 36.21% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 747 | | uniqueNames | 12 | | maxNameDensity | 2.28 | | worstName | "Meredith" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Meredith" | | discoveredNames | | Aurora | 2 | | Silas | 6 | | Cardiff | 1 | | Llewellyn | 1 | | Raven | 1 | | Nest | 1 | | Camel | 1 | | Rory | 15 | | Meredith | 17 | | Cathays | 1 | | Terrace | 1 | | Quiet | 1 |
| | persons | | 0 | "Silas" | | 1 | "Llewellyn" | | 2 | "Raven" | | 3 | "Rory" | | 4 | "Meredith" |
| | places | | 0 | "Cardiff" | | 1 | "Cathays" | | 2 | "Terrace" |
| | globalScore | 0.362 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 1268 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 14.25 | | std | 16.51 | | cv | 1.159 | | sampleLengths | | 0 | 1 | | 1 | 50 | | 2 | 10 | | 3 | 1 | | 4 | 59 | | 5 | 4 | | 6 | 48 | | 7 | 4 | | 8 | 4 | | 9 | 10 | | 10 | 1 | | 11 | 33 | | 12 | 5 | | 13 | 17 | | 14 | 6 | | 15 | 11 | | 16 | 5 | | 17 | 16 | | 18 | 50 | | 19 | 15 | | 20 | 14 | | 21 | 2 | | 22 | 5 | | 23 | 3 | | 24 | 9 | | 25 | 7 | | 26 | 2 | | 27 | 25 | | 28 | 24 | | 29 | 22 | | 30 | 2 | | 31 | 8 | | 32 | 2 | | 33 | 43 | | 34 | 11 | | 35 | 5 | | 36 | 23 | | 37 | 5 | | 38 | 2 | | 39 | 3 | | 40 | 1 | | 41 | 27 | | 42 | 8 | | 43 | 12 | | 44 | 5 | | 45 | 2 | | 46 | 2 | | 47 | 11 | | 48 | 5 | | 49 | 2 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 72 | | matches | | 0 | "being clipped" | | 1 | "been rearranged" | | 2 | "being taken" | | 3 | "been asked" |
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| 86.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 117 | | matches | | 0 | "was losing" | | 1 | "was tasting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 749 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.025367156208277702 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004005340453938585 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 9.19 | | std | 10.5 | | cv | 1.143 | | sampleLengths | | 0 | 1 | | 1 | 12 | | 2 | 7 | | 3 | 2 | | 4 | 9 | | 5 | 20 | | 6 | 10 | | 7 | 1 | | 8 | 23 | | 9 | 2 | | 10 | 10 | | 11 | 18 | | 12 | 6 | | 13 | 4 | | 14 | 10 | | 15 | 3 | | 16 | 15 | | 17 | 2 | | 18 | 6 | | 19 | 7 | | 20 | 5 | | 21 | 4 | | 22 | 4 | | 23 | 10 | | 24 | 1 | | 25 | 33 | | 26 | 5 | | 27 | 17 | | 28 | 6 | | 29 | 11 | | 30 | 5 | | 31 | 12 | | 32 | 4 | | 33 | 29 | | 34 | 8 | | 35 | 2 | | 36 | 5 | | 37 | 6 | | 38 | 15 | | 39 | 14 | | 40 | 2 | | 41 | 5 | | 42 | 3 | | 43 | 9 | | 44 | 7 | | 45 | 2 | | 46 | 12 | | 47 | 3 | | 48 | 10 | | 49 | 24 |
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| 58.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4057971014492754 | | totalSentences | 138 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 58 | | matches | | 0 | "Then the laugh stopped and" | | 1 | "Somewhere above them a pipe" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 58 | | matches | | 0 | "She turned on the stool," | | 1 | "Her hair had gone darker," | | 2 | "He returned to the glass." | | 3 | "She'd been doing it, she" | | 4 | "He wouldn't say anything later." | | 5 | "He never did." | | 6 | "She said it like she" | | 7 | "She hadn't been asked a" | | 8 | "She laid her palm flat" |
| | ratio | 0.155 | |
| 54.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 58 | | matches | | 0 | "The name landed wrong, like" | | 1 | "Nobody in this bar called" | | 2 | "Carter if Silas was being" | | 3 | "She turned on the stool," | | 4 | "Meredith Llewellyn stood in the" | | 5 | "A leather briefcase that probably" | | 6 | "Her hair had gone darker," | | 7 | "Meredith came over, pulling the" | | 8 | "The legs scraped." | | 9 | "Silas, behind the bar polishing" | | 10 | "The smallest tilt of his" | | 11 | "He returned to the glass." | | 12 | "Meredith said, and then, to" | | 13 | "Silas pulled the pints without" | | 14 | "Rory lifted her pint" | | 15 | "Meredith laughed, and there it" | | 16 | "The highlighter Meredith always left" | | 17 | "Rory ran her thumb along" | | 18 | "An old habit." | | 19 | "She'd been doing it, she" |
| | ratio | 0.81 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 4 | | matches | | 0 | "But Aurora, full and formal, with that Cardiff lilt still riding under it, that was a voice from another country." | | 1 | "She laid her palm flat on top of it, the way she used to lay her palm flat on a textbook before an exam, as if she could press the knowledge up through her skin…" | | 2 | "Two men came in, shaking rain off, loud with the particular loudness of men who'd been loud somewhere else first." | | 3 | "From the booth, one of the men laughed, a big loose sound, and under it, closer, the bell above the door jangled as someone new pushed it open, and Rory felt th…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Meredith said, not moving" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 78 | | tagDensity | 0.103 | | leniency | 0.205 | | rawRatio | 0 | | effectiveRatio | 0 | |