| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.169 | | leniency | 0.337 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1504 | | 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) | |
| 93.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1504 | | 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 | 66 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 66 | | 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 | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1507 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 772 | | uniqueNames | 12 | | maxNameDensity | 2.33 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Old | 2 | | Compton | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Silas | 5 | | Belfast | 1 | | Prague | 2 | | Rory | 16 | | Cardiff | 1 | | Morgan | 1 | | Eva | 18 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Morgan" | | 5 | "Eva" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Belfast" | | 4 | "Prague" | | 5 | "Cardiff" |
| | globalScore | 0.334 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1507 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 88 | | mean | 17.13 | | std | 17.81 | | cv | 1.04 | | sampleLengths | | 0 | 27 | | 1 | 90 | | 2 | 26 | | 3 | 7 | | 4 | 21 | | 5 | 13 | | 6 | 16 | | 7 | 22 | | 8 | 3 | | 9 | 32 | | 10 | 4 | | 11 | 17 | | 12 | 5 | | 13 | 38 | | 14 | 7 | | 15 | 7 | | 16 | 45 | | 17 | 2 | | 18 | 1 | | 19 | 24 | | 20 | 6 | | 21 | 7 | | 22 | 4 | | 23 | 2 | | 24 | 4 | | 25 | 43 | | 26 | 27 | | 27 | 9 | | 28 | 10 | | 29 | 1 | | 30 | 11 | | 31 | 8 | | 32 | 4 | | 33 | 84 | | 34 | 9 | | 35 | 17 | | 36 | 21 | | 37 | 17 | | 38 | 3 | | 39 | 2 | | 40 | 20 | | 41 | 4 | | 42 | 5 | | 43 | 4 | | 44 | 46 | | 45 | 6 | | 46 | 31 | | 47 | 3 | | 48 | 30 | | 49 | 3 |
| |
| 84.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 66 | | matches | | 0 | "being asked" | | 1 | "being told" | | 2 | "been handed" | | 3 | "been itemised" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 136 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 134 | | ratio | 0.007 | | matches | | 0 | "\"Everything's a phone shop now.\" The gin went down and the vowels went down with it — flat, warm, Cardiff all over." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 778 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.021850899742930592 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012853470437017994 | |
| 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 | 11.25 | | std | 8.88 | | cv | 0.789 | | sampleLengths | | 0 | 27 | | 1 | 7 | | 2 | 33 | | 3 | 24 | | 4 | 17 | | 5 | 9 | | 6 | 10 | | 7 | 16 | | 8 | 7 | | 9 | 16 | | 10 | 5 | | 11 | 7 | | 12 | 6 | | 13 | 7 | | 14 | 9 | | 15 | 22 | | 16 | 3 | | 17 | 20 | | 18 | 12 | | 19 | 4 | | 20 | 17 | | 21 | 5 | | 22 | 18 | | 23 | 20 | | 24 | 7 | | 25 | 7 | | 26 | 45 | | 27 | 2 | | 28 | 1 | | 29 | 18 | | 30 | 6 | | 31 | 6 | | 32 | 7 | | 33 | 4 | | 34 | 2 | | 35 | 4 | | 36 | 12 | | 37 | 25 | | 38 | 6 | | 39 | 5 | | 40 | 9 | | 41 | 13 | | 42 | 6 | | 43 | 3 | | 44 | 3 | | 45 | 7 | | 46 | 1 | | 47 | 11 | | 48 | 8 | | 49 | 4 |
| |
| 60.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.41044776119402987 | | totalSentences | 134 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 63 | | matches | | 0 | "Then her old trick of" | | 1 | "Then he limped back into" | | 2 | "Then she lifted them, squared" |
| | ratio | 0.048 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 63 | | matches | | 0 | "She took a stool three" | | 1 | "He slid the glass across" | | 2 | "He went back to his" | | 3 | "She checked her phone" | | 4 | "She raised the glass a" | | 5 | "Her sleeve rode up, and" | | 6 | "Her thumb moved over the" | | 7 | "She reached up instead and" | | 8 | "They were already square." | | 9 | "His eyes rested on Rory" | | 10 | "she said, turning the glass" |
| | ratio | 0.175 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 63 | | matches | | 0 | "Rain came at Old Compton" | | 1 | "Rory had the end stool" | | 2 | "The radio muttered something with" | | 3 | "The door heaved open on" | | 4 | "A woman came in shoulder-first," | | 5 | "Silas didn't look up" | | 6 | "She took a stool three" | | 7 | "He slid the glass across" | | 8 | "The gin went down and" | | 9 | "Rory's pencil stopped." | | 10 | "The woman glanced at the" | | 11 | "Rory turned on her stool." | | 12 | "Eva Morgan had a city" | | 13 | "Eva laughed the way she" | | 14 | "Eva dragged her stool close" | | 15 | "Silas came down the bar" | | 16 | "He went back to his" | | 17 | "The side door banged open." | | 18 | "A kid in a crash" | | 19 | "Rory tossed them across the" |
| | ratio | 0.857 | |
| 79.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 63 | | matches | | 0 | "While he poured, she studied" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.024 | | leniency | 0.048 | | rawRatio | 0 | | effectiveRatio | 0 | |