| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 1 | | adverbTags | | 0 | "Nia said finally [finally]" |
| | dialogueSentences | 66 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.05 | | effectiveRatio | 0.03 | |
| 90.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1546 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "really" |
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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) | |
| 93.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1546 | | 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 | 75 | | matches | (empty) | |
| 66.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1546 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 931 | | uniqueNames | 30 | | maxNameDensity | 2.15 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Nia" | | discoveredNames | | Frith | 1 | | Street | 1 | | Golden | 1 | | Empress | 1 | | Fitzrovia | 1 | | Nest | 1 | | Thursday | 1 | | Thames | 2 | | Law | 1 | | Society | 1 | | Rory | 20 | | Nia | 20 | | Pritchard | 1 | | Doc | 1 | | Martens | 1 | | Welsh | 1 | | Club | 1 | | Guinness | 4 | | Nina | 1 | | Simone | 1 | | Cardiff | 1 | | Cathays | 1 | | Terrace | 1 | | Gareth | 1 | | Round | 1 | | Evan | 1 | | Silas | 4 | | Sunday | 1 | | Wednesday | 1 | | Wound | 1 |
| | persons | | 0 | "Rory" | | 1 | "Nia" | | 2 | "Pritchard" | | 3 | "Guinness" | | 4 | "Nina" | | 5 | "Simone" | | 6 | "Gareth" | | 7 | "Evan" | | 8 | "Silas" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "Fitzrovia" | | 3 | "Thames" | | 4 | "Welsh" | | 5 | "Cardiff" | | 6 | "Cathays" | | 7 | "Terrace" |
| | globalScore | 0.426 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 1546 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 23.07 | | std | 22.89 | | cv | 0.992 | | sampleLengths | | 0 | 94 | | 1 | 56 | | 2 | 4 | | 3 | 56 | | 4 | 13 | | 5 | 12 | | 6 | 1 | | 7 | 94 | | 8 | 4 | | 9 | 3 | | 10 | 13 | | 11 | 23 | | 12 | 5 | | 13 | 30 | | 14 | 45 | | 15 | 24 | | 16 | 52 | | 17 | 1 | | 18 | 29 | | 19 | 7 | | 20 | 2 | | 21 | 4 | | 22 | 4 | | 23 | 34 | | 24 | 3 | | 25 | 21 | | 26 | 10 | | 27 | 30 | | 28 | 74 | | 29 | 52 | | 30 | 5 | | 31 | 5 | | 32 | 4 | | 33 | 68 | | 34 | 6 | | 35 | 4 | | 36 | 6 | | 37 | 30 | | 38 | 23 | | 39 | 51 | | 40 | 3 | | 41 | 45 | | 42 | 6 | | 43 | 17 | | 44 | 30 | | 45 | 2 | | 46 | 43 | | 47 | 50 | | 48 | 10 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 74.21% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 159 | | matches | | 0 | "wasn't looking" | | 1 | "was looking" | | 2 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 120 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 934 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.028907922912205567 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0053533190578158455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 12.88 | | std | 10.16 | | cv | 0.788 | | sampleLengths | | 0 | 10 | | 1 | 22 | | 2 | 33 | | 3 | 2 | | 4 | 27 | | 5 | 8 | | 6 | 26 | | 7 | 22 | | 8 | 4 | | 9 | 28 | | 10 | 2 | | 11 | 10 | | 12 | 16 | | 13 | 8 | | 14 | 5 | | 15 | 10 | | 16 | 2 | | 17 | 1 | | 18 | 22 | | 19 | 22 | | 20 | 23 | | 21 | 5 | | 22 | 22 | | 23 | 4 | | 24 | 3 | | 25 | 10 | | 26 | 3 | | 27 | 15 | | 28 | 8 | | 29 | 5 | | 30 | 3 | | 31 | 27 | | 32 | 21 | | 33 | 24 | | 34 | 13 | | 35 | 11 | | 36 | 24 | | 37 | 28 | | 38 | 1 | | 39 | 18 | | 40 | 11 | | 41 | 7 | | 42 | 2 | | 43 | 4 | | 44 | 4 | | 45 | 14 | | 46 | 20 | | 47 | 3 | | 48 | 21 | | 49 | 10 |
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| 71.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4583333333333333 | | totalSentences | 120 | | uniqueOpeners | 55 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Then she heard herself and" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 67 | | matches | | 0 | "It buzzed, died and caught" | | 1 | "He clocked her over the" | | 2 | "It took Rory three seconds" | | 3 | "It took the woman one." | | 4 | "She had once been sick" | | 5 | "She had a wedding band," | | 6 | "It wasn't great." | | 7 | "He didn't linger, only moved" | | 8 | "She was looking at the" | | 9 | "She'd had it since she" | | 10 | "She picked up the gin," | | 11 | "Her mouth twisted" | | 12 | "He had set down the" | | 13 | "They both almost smiled." | | 14 | "She pulled the lanyard out" |
| | ratio | 0.224 | |
| 27.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 67 | | matches | | 0 | "The green neon above the" | | 1 | "It buzzed, died and caught" | | 2 | "Rory stood under it for" | | 3 | "Silas stood behind the bar" | | 4 | "He clocked her over the" | | 5 | "Rory followed the tilt." | | 6 | "A woman sat beneath the" | | 7 | "Hair scraped into a knot" | | 8 | "A lanyard poked out of" | | 9 | "It took Rory three seconds" | | 10 | "It took the woman one." | | 11 | "The name came out flat," | | 12 | "Nia Pritchard slid off the" | | 13 | "The girl Rory remembered had" | | 14 | "She had once been sick" | | 15 | "This woman wore court shoes." | | 16 | "She had a wedding band," | | 17 | "Nia's gaze snagged on the" | | 18 | "Rory unbuckled the straps and" | | 19 | "It wasn't great." |
| | ratio | 0.866 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "One man in Fitzrovia who'd opened the door in a towel and tipped her a single fifty-pence coin, warm from somewhere she didn't want to think about." | | 1 | "A woman sat beneath the framed photograph of the Thames frozen in 1963, a gin and tonic in front of her that hadn't lost any of its ice." | | 2 | "He had set down the cloth and was standing with both hands flat on the counter, the silver signet ring catching the lamplight, his face turned toward the window…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 66 | | tagDensity | 0.121 | | leniency | 0.242 | | rawRatio | 0 | | effectiveRatio | 0 | |