| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 66 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1664 | | totalAiIsmAdverbs | 2 | | 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) | |
| 97.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1664 | | 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | 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 | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1657 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1024 | | uniqueNames | 20 | | maxNameDensity | 1.86 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 19 | | Golden | 1 | | Empress | 1 | | Nest | 1 | | Silas | 8 | | Greek | 1 | | Street | 1 | | Vienna | 1 | | Cathays | 1 | | Contract | 1 | | Law | 1 | | Bute | 1 | | Park | 1 | | Lambrini | 1 | | Pritchard | 1 | | Cerys | 19 | | Walkabout | 1 | | Duffy | 1 | | Salisbury | 1 | | Road | 1 |
| | persons | | 0 | "Rory" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Law" | | 4 | "Cerys" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Vienna" | | 3 | "Cathays" | | 4 | "Bute" | | 5 | "Park" | | 6 | "Walkabout" | | 7 | "Salisbury" | | 8 | "Road" |
| | globalScore | 0.572 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | 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 | 1657 | | 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 | 81 | | mean | 20.46 | | std | 18.75 | | cv | 0.917 | | sampleLengths | | 0 | 54 | | 1 | 39 | | 2 | 3 | | 3 | 25 | | 4 | 24 | | 5 | 4 | | 6 | 4 | | 7 | 69 | | 8 | 32 | | 9 | 26 | | 10 | 54 | | 11 | 41 | | 12 | 6 | | 13 | 11 | | 14 | 5 | | 15 | 27 | | 16 | 31 | | 17 | 1 | | 18 | 2 | | 19 | 57 | | 20 | 8 | | 21 | 4 | | 22 | 31 | | 23 | 38 | | 24 | 4 | | 25 | 23 | | 26 | 9 | | 27 | 3 | | 28 | 21 | | 29 | 2 | | 30 | 14 | | 31 | 24 | | 32 | 32 | | 33 | 32 | | 34 | 21 | | 35 | 3 | | 36 | 9 | | 37 | 2 | | 38 | 40 | | 39 | 9 | | 40 | 8 | | 41 | 23 | | 42 | 50 | | 43 | 10 | | 44 | 3 | | 45 | 38 | | 46 | 4 | | 47 | 26 | | 48 | 9 | | 49 | 1 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 165 | | matches | (empty) | |
| 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 | 1026 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.018518518518518517 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009746588693957114 | |
| 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 | 12.37 | | std | 11.04 | | cv | 0.893 | | sampleLengths | | 0 | 10 | | 1 | 44 | | 2 | 5 | | 3 | 9 | | 4 | 25 | | 5 | 3 | | 6 | 23 | | 7 | 2 | | 8 | 24 | | 9 | 4 | | 10 | 4 | | 11 | 6 | | 12 | 7 | | 13 | 24 | | 14 | 2 | | 15 | 12 | | 16 | 18 | | 17 | 32 | | 18 | 4 | | 19 | 22 | | 20 | 4 | | 21 | 50 | | 22 | 2 | | 23 | 21 | | 24 | 18 | | 25 | 6 | | 26 | 8 | | 27 | 3 | | 28 | 5 | | 29 | 20 | | 30 | 7 | | 31 | 16 | | 32 | 15 | | 33 | 1 | | 34 | 2 | | 35 | 6 | | 36 | 27 | | 37 | 4 | | 38 | 20 | | 39 | 8 | | 40 | 2 | | 41 | 2 | | 42 | 6 | | 43 | 25 | | 44 | 37 | | 45 | 1 | | 46 | 4 | | 47 | 7 | | 48 | 16 | | 49 | 9 |
| |
| 61.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.40298507462686567 | | totalSentences | 134 | | uniqueOpeners | 54 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | | ratio | 0.013 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 78 | | matches | | 0 | "She pushed through the door." | | 1 | "He set the glass down" | | 2 | "He slid the glass across" | | 3 | "It went up high and" | | 4 | "She knew that laugh." | | 5 | "She had heard it through" | | 6 | "She had her head thrown" | | 7 | "He set the pint glass" | | 8 | "She thought about the bookshelf," | | 9 | "She thought about it long" | | 10 | "It took Cerys another ten" | | 11 | "She mimed something vanishing into" | | 12 | "It came out flatter than" | | 13 | "She jabbed a thumb at" | | 14 | "It landed harder than a" | | 15 | "She spun the stem of" | | 16 | "She didn't pick up the" | | 17 | "Her voice dropped" | | 18 | "She let go of the" | | 19 | "She didn't move." |
| | ratio | 0.333 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 78 | | matches | | 0 | "The green neon above the" | | 1 | "Every eleven seconds it dropped" | | 2 | "She pushed through the door." | | 3 | "The Nest smelled of lemon" | | 4 | "Silas stood behind the bar" | | 5 | "He set the glass down" | | 6 | "The corner of his beard" | | 7 | "That counted as a laugh" | | 8 | "He slid the glass across" | | 9 | "A woman alone under the" | | 10 | "It went up high and" | | 11 | "She knew that laugh." | | 12 | "She had heard it through" | | 13 | "Hair cut to a sharp" | | 14 | "She had her head thrown" | | 15 | "Rory turned back to the" | | 16 | "Silas didn't lift his eyes" | | 17 | "He set the pint glass" | | 18 | "She thought about the bookshelf," | | 19 | "She thought about it long" |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas stood behind the bar with a tea towel over his shoulder, turning a pint glass against the light as if it owed him money." | | 1 | "And at the far end, by the bookshelf, a table of four in office clothes, loud in the way of people who'd started drinking at five and had no plans to stop." | | 2 | "Hair cut to a sharp bob now, a navy suit that fit her properly, a lanyard tucked into the breast pocket." | | 3 | "Green washed over the maps on the walls, over the photographs of men in hats who'd been dead for fifty years." | | 4 | "At the girl who'd once held her hair back outside Walkabout and then walked her home in the rain, singing Duffy off-key the whole way down Salisbury Road." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 66 | | tagDensity | 0.015 | | leniency | 0.03 | | rawRatio | 1 | | effectiveRatio | 0.03 | |