| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.34 | | leniency | 0.681 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1348 | | 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) | |
| 96.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1348 | | 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 | 53 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 53 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1348 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 704 | | uniqueNames | 10 | | maxNameDensity | 2.41 | | worstName | "Cerys" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Cerys" | | discoveredNames | | Silas | 5 | | Soho | 1 | | Golden | 1 | | Empress | 1 | | Fitzroy | 1 | | Street | 1 | | Rory | 15 | | Cerys | 17 | | Talybont | 1 | | Cream | 1 |
| | persons | | 0 | "Silas" | | 1 | "Empress" | | 2 | "Rory" | | 3 | "Cerys" |
| | places | | 0 | "Soho" | | 1 | "Fitzroy" | | 2 | "Street" | | 3 | "Talybont" |
| | globalScore | 0.293 | | windowScore | 0 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 2 | | matches | | 0 | "as though leaving remained an option she hadn't ruled out" | | 1 | "quite fit its wall" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1348 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 29.3 | | std | 23.37 | | cv | 0.797 | | sampleLengths | | 0 | 64 | | 1 | 50 | | 2 | 13 | | 3 | 46 | | 4 | 3 | | 5 | 13 | | 6 | 2 | | 7 | 36 | | 8 | 3 | | 9 | 43 | | 10 | 44 | | 11 | 7 | | 12 | 4 | | 13 | 20 | | 14 | 42 | | 15 | 59 | | 16 | 45 | | 17 | 3 | | 18 | 56 | | 19 | 4 | | 20 | 37 | | 21 | 9 | | 22 | 32 | | 23 | 21 | | 24 | 5 | | 25 | 4 | | 26 | 52 | | 27 | 33 | | 28 | 20 | | 29 | 19 | | 30 | 52 | | 31 | 1 | | 32 | 47 | | 33 | 5 | | 34 | 73 | | 35 | 21 | | 36 | 55 | | 37 | 6 | | 38 | 55 | | 39 | 16 | | 40 | 4 | | 41 | 94 | | 42 | 15 | | 43 | 26 | | 44 | 19 | | 45 | 70 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 74.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 83 | | ratio | 0.024 | | matches | | 0 | "\"I finished, obviously. Training contract, then Bishopsgate. I was going to prosecute war criminals; now I help energy companies argue about pipelines. The money's obscene. I've decided to let it be.\"" | | 1 | "\"He came to your twenty-first. He chose what you wore; you said it in the taxi like it was a joke, and I laughed. I thought it was romantic, Rory. I have thought about that night every few months for five years, and I thought it was romantic.\"" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 710 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.014084507042253521 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0028169014084507044 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 16.24 | | std | 12.86 | | cv | 0.792 | | sampleLengths | | 0 | 9 | | 1 | 37 | | 2 | 18 | | 3 | 9 | | 4 | 25 | | 5 | 16 | | 6 | 13 | | 7 | 2 | | 8 | 13 | | 9 | 31 | | 10 | 3 | | 11 | 7 | | 12 | 6 | | 13 | 2 | | 14 | 28 | | 15 | 8 | | 16 | 3 | | 17 | 19 | | 18 | 24 | | 19 | 33 | | 20 | 11 | | 21 | 7 | | 22 | 4 | | 23 | 12 | | 24 | 8 | | 25 | 35 | | 26 | 7 | | 27 | 17 | | 28 | 26 | | 29 | 4 | | 30 | 12 | | 31 | 8 | | 32 | 37 | | 33 | 3 | | 34 | 7 | | 35 | 49 | | 36 | 4 | | 37 | 6 | | 38 | 31 | | 39 | 9 | | 40 | 11 | | 41 | 21 | | 42 | 21 | | 43 | 5 | | 44 | 4 | | 45 | 34 | | 46 | 18 | | 47 | 11 | | 48 | 22 | | 49 | 8 |
| |
| 61.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.40963855421686746 | | totalSentences | 83 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 48 | | matches | | 0 | "Then she crossed the room" | | 1 | "Somewhere behind them the door" |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 48 | | matches | | 0 | "Her jacket steamed on the" | | 1 | "It smelled of sesame oil" | | 2 | "She cupped her whisky and" | | 3 | "She shook the rain from" | | 4 | "She turned on the stool," | | 5 | "His limp announced him before" | | 6 | "She glanced at the maps" | | 7 | "He set Rory's second whisky" | | 8 | "She belongs here, in a" | | 9 | "He kept his back to" | | 10 | "She watched the neon bleed" | | 11 | "She stopped, recalibrated" | | 12 | "She didn't pick it up." | | 13 | "She didn't push it back." |
| | ratio | 0.292 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 48 | | matches | | 0 | "The neon above the door" | | 1 | "Rory watched it run down" | | 2 | "Her jacket steamed on the" | | 3 | "It smelled of sesame oil" | | 4 | "She cupped her whisky and" | | 5 | "The door opened and let" | | 6 | "She shook the rain from" | | 7 | "Rory set the book face" | | 8 | "Cerys stood there with rain" | | 9 | "Cerys touched the blunt line" | | 10 | "She turned on the stool," | | 11 | "Silas appeared with a glass," | | 12 | "His limp announced him before" | | 13 | "She glanced at the maps" | | 14 | "Silas said, and poured, and" | | 15 | "He set Rory's second whisky" | | 16 | "This is her local." | | 17 | "She belongs here, in a" | | 18 | "Cerys wrapped both hands around" | | 19 | "Cerys let the words sit" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "Rory watched it run down the window in bright threads while Silas worked a cloth through a glass that was already clean, the way he did when the bar was quiet a…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.085 | | leniency | 0.17 | | rawRatio | 0 | | effectiveRatio | 0 | |