| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 93 | | tagDensity | 0.258 | | leniency | 0.516 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1504 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "lightly" | | 1 | "slowly" | | 2 | "very" |
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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) | |
| 96.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1504 | | totalAiIsms | 1 | | 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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1504 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 842 | | uniqueNames | 10 | | maxNameDensity | 0.83 | | worstName | "Dec" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Rory | 3 | | Moss | 1 | | Silas | 6 | | Fanta | 2 | | Cardiff | 1 | | Central | 1 | | Dec | 7 | | Dutch | 2 | | Northern | 1 | | Declan | 2 |
| | persons | | 0 | "Rory" | | 1 | "Moss" | | 2 | "Silas" | | 3 | "Fanta" | | 4 | "Dec" | | 5 | "Declan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a technique, and she couldn't" |
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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 | 1504 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 134 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 94 | | mean | 16 | | std | 18.64 | | cv | 1.165 | | sampleLengths | | 0 | 41 | | 1 | 5 | | 2 | 29 | | 3 | 72 | | 4 | 1 | | 5 | 1 | | 6 | 53 | | 7 | 4 | | 8 | 4 | | 9 | 1 | | 10 | 30 | | 11 | 4 | | 12 | 1 | | 13 | 16 | | 14 | 10 | | 15 | 40 | | 16 | 4 | | 17 | 21 | | 18 | 2 | | 19 | 1 | | 20 | 3 | | 21 | 30 | | 22 | 38 | | 23 | 6 | | 24 | 12 | | 25 | 2 | | 26 | 35 | | 27 | 6 | | 28 | 1 | | 29 | 4 | | 30 | 5 | | 31 | 43 | | 32 | 7 | | 33 | 1 | | 34 | 14 | | 35 | 13 | | 36 | 7 | | 37 | 8 | | 38 | 45 | | 39 | 5 | | 40 | 2 | | 41 | 2 | | 42 | 23 | | 43 | 5 | | 44 | 9 | | 45 | 1 | | 46 | 47 | | 47 | 6 | | 48 | 11 | | 49 | 24 |
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| 94.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 64 | | matches | | 0 | "was left" | | 1 | "being asked" |
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| 16.09% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 145 | | matches | | 0 | "were arguing" | | 1 | "was playing" | | 2 | "was standing" | | 3 | "were reading" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 860 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.030232558139534883 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004651162790697674 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 11.22 | | std | 11.69 | | cv | 1.041 | | sampleLengths | | 0 | 41 | | 1 | 5 | | 2 | 9 | | 3 | 12 | | 4 | 8 | | 5 | 7 | | 6 | 22 | | 7 | 8 | | 8 | 7 | | 9 | 28 | | 10 | 1 | | 11 | 1 | | 12 | 15 | | 13 | 38 | | 14 | 4 | | 15 | 4 | | 16 | 1 | | 17 | 30 | | 18 | 4 | | 19 | 1 | | 20 | 14 | | 21 | 2 | | 22 | 10 | | 23 | 6 | | 24 | 4 | | 25 | 30 | | 26 | 4 | | 27 | 16 | | 28 | 5 | | 29 | 2 | | 30 | 1 | | 31 | 3 | | 32 | 23 | | 33 | 7 | | 34 | 34 | | 35 | 4 | | 36 | 6 | | 37 | 7 | | 38 | 5 | | 39 | 2 | | 40 | 27 | | 41 | 8 | | 42 | 6 | | 43 | 1 | | 44 | 4 | | 45 | 5 | | 46 | 5 | | 47 | 38 | | 48 | 7 | | 49 | 1 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.44029850746268656 | | totalSentences | 134 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 51 | | matches | | 0 | "Then she looked, and the" | | 1 | "Somewhere behind her a table" |
| | ratio | 0.039 | |
| 8.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 51 | | matches | | 0 | "She knew the voice before" | | 1 | "She set the pint down" | | 2 | "He laughed again, and there" | | 3 | "He turned the glass on" | | 4 | "She didn't take it." | | 5 | "She stood at the bar" | | 6 | "He tipped his head towards" | | 7 | "She reached over the bar" | | 8 | "She put it down again." | | 9 | "He said it lightly, and" | | 10 | "She smiled with her mouth." | | 11 | "He'd always been a waiter," | | 12 | "It used to comfort her." | | 13 | "His thumb went to the" | | 14 | "She kept her voice level," | | 15 | "He turned the watch back" | | 16 | "He laughed, and this time" | | 17 | "He drained what was left" | | 18 | "He said it without heat," | | 19 | "He nodded, slowly, and she" |
| | ratio | 0.529 | |
| 38.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 51 | | matches | | 0 | "The pint glass slipped through" | | 1 | "She knew the voice before" | | 2 | "Declan Moss had been a" | | 3 | "Round-faced, freckled, forever hunched inside" | | 4 | "The man on the stool" | | 5 | "A charcoal wool coat folded" | | 6 | "She set the pint down" | | 7 | "He laughed again, and there" | | 8 | "He turned the glass on" | | 9 | "The stool beside him was" | | 10 | "She didn't take it." | | 11 | "She stood at the bar" | | 12 | "He tipped his head towards" | | 13 | "Dec's eyes moved over the" | | 14 | "She reached over the bar" | | 15 | "The half-glass stopped halfway to" | | 16 | "She put it down again." | | 17 | "He said it lightly, and" | | 18 | "She smiled with her mouth." | | 19 | "He'd always been a waiter," |
| | ratio | 0.843 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 51 | | matches | | | ratio | 0.02 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 3 | | matches | | 0 | "Round-faced, freckled, forever hunched inside a hoodie two sizes too big, the sort of lad who apologised to furniture he bumped into." | | 1 | "A charcoal wool coat folded over his knee, and a watch that caught the green wash from the neon outside the window every time he moved his wrist." | | 2 | "He laughed again, and there it was, the old wheeze underneath the new confidence, the boy who used to snort Fanta out his nose in the Cardiff Central bus statio…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | 0 | "He said, and the lightness was new too, a coat he'd learned to wear" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 93 | | tagDensity | 0.151 | | leniency | 0.301 | | rawRatio | 0.071 | | effectiveRatio | 0.022 | |