| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 36 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 169 | | tagDensity | 0.213 | | leniency | 0.426 | | rawRatio | 0.028 | | effectiveRatio | 0.012 | |
| 98.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2864 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 86.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2864 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "weight" | | 1 | "eyebrow" | | 2 | "framework" | | 3 | "echo" | | 4 | "mosaic" | | 5 | "swept away" | | 6 | "chill" |
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| 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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 264 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2861 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 56 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1539 | | uniqueNames | 6 | | maxNameDensity | 0.65 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Dylan" | | discoveredNames | | Cathays | 1 | | Bristol | 1 | | Channel | 1 | | Silas | 10 | | Taff | 1 | | Dylan | 10 |
| | persons | | 0 | "Channel" | | 1 | "Silas" | | 2 | "Dylan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 32.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 4 | | matches | | 0 | "sounded like shoes on wet pavements" | | 1 | "looked like a country on a map you couldn" | | 2 | "smelled like a fairground in winter" | | 3 | "felt like something I chose" |
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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 | 2861 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 264 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 191 | | mean | 14.98 | | std | 16.66 | | cv | 1.112 | | sampleLengths | | 0 | 60 | | 1 | 39 | | 2 | 8 | | 3 | 6 | | 4 | 9 | | 5 | 67 | | 6 | 6 | | 7 | 5 | | 8 | 85 | | 9 | 72 | | 10 | 1 | | 11 | 34 | | 12 | 10 | | 13 | 16 | | 14 | 3 | | 15 | 11 | | 16 | 50 | | 17 | 10 | | 18 | 17 | | 19 | 5 | | 20 | 5 | | 21 | 8 | | 22 | 4 | | 23 | 6 | | 24 | 42 | | 25 | 6 | | 26 | 2 | | 27 | 31 | | 28 | 6 | | 29 | 8 | | 30 | 3 | | 31 | 10 | | 32 | 3 | | 33 | 10 | | 34 | 18 | | 35 | 38 | | 36 | 17 | | 37 | 26 | | 38 | 4 | | 39 | 6 | | 40 | 7 | | 41 | 4 | | 42 | 6 | | 43 | 22 | | 44 | 23 | | 45 | 7 | | 46 | 10 | | 47 | 3 | | 48 | 33 | | 49 | 17 |
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| 99.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 131 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 273 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 264 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1541 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.031148604802076575 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0025957170668397143 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 264 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 264 | | mean | 10.84 | | std | 7.69 | | cv | 0.709 | | sampleLengths | | 0 | 24 | | 1 | 11 | | 2 | 25 | | 3 | 14 | | 4 | 17 | | 5 | 8 | | 6 | 8 | | 7 | 6 | | 8 | 9 | | 9 | 16 | | 10 | 30 | | 11 | 12 | | 12 | 9 | | 13 | 6 | | 14 | 5 | | 15 | 35 | | 16 | 20 | | 17 | 18 | | 18 | 12 | | 19 | 4 | | 20 | 33 | | 21 | 5 | | 22 | 5 | | 23 | 13 | | 24 | 12 | | 25 | 1 | | 26 | 7 | | 27 | 11 | | 28 | 16 | | 29 | 4 | | 30 | 6 | | 31 | 11 | | 32 | 5 | | 33 | 3 | | 34 | 5 | | 35 | 6 | | 36 | 28 | | 37 | 22 | | 38 | 10 | | 39 | 17 | | 40 | 5 | | 41 | 5 | | 42 | 8 | | 43 | 4 | | 44 | 6 | | 45 | 20 | | 46 | 17 | | 47 | 5 | | 48 | 6 | | 49 | 2 |
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| 37.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.23484848484848486 | | totalSentences | 264 | | uniqueOpeners | 62 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 64 | | totalSentences | 105 | | matches | | 0 | "I came down the steps" | | 1 | "My wrist brushed the banister" | | 2 | "He slid the bottle across" | | 3 | "His limp showed when he" | | 4 | "He never asked with words," | | 5 | "I tipped the bottle and" | | 6 | "He drifted down to the" | | 7 | "He had the posture of" | | 8 | "It hit in pieces." | | 9 | "His hair had gone short" | | 10 | "His shirt collar was black" | | 11 | "He’d kept the lift at" | | 12 | "I held his gaze and" | | 13 | "He smiled without teeth and" | | 14 | "He tilted the glass" | | 15 | "I let a breath out" | | 16 | "He watched my face like" | | 17 | "I pictured the Cathays kitchen," | | 18 | "I tasted cheap salt in" | | 19 | "He leaned in a little." |
| | ratio | 0.61 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 105 | | matches | | 0 | "Silas worked a wedge of" | | 1 | "The maps on the wall" | | 2 | "Someone laughed at the back" | | 3 | "I came down the steps" | | 4 | "My wrist brushed the banister" | | 5 | "The room smelled of citrus" | | 6 | "Silas glanced up as I" | | 7 | "He slid the bottle across" | | 8 | "His limp showed when he" | | 9 | "He never asked with words," | | 10 | "I tipped the bottle and" | | 11 | "He drifted down to the" | | 12 | "He had the posture of" | | 13 | "It hit in pieces." | | 14 | "The line of the jaw" | | 15 | "Time had worked him over." | | 16 | "The safety pin was gone." | | 17 | "His hair had gone short" | | 18 | "His shirt collar was black" | | 19 | "The word went strange in" |
| | ratio | 0.981 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 2 | | matches | | 0 | "He had become the kind of person who wore a collar and took his hands from his pockets when he spoke to people." | | 1 | "Rhys who chased us out of his room with a slipper and then shared his crisps when he cooled." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 36 | | uselessAdditionCount | 1 | | matches | | 0 | "Dylan said, half turning," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 29 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 169 | | tagDensity | 0.172 | | leniency | 0.343 | | rawRatio | 0 | | effectiveRatio | 0 | |