| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2876 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 82.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2876 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "encounter" | | 2 | "flicked" | | 3 | "etched" | | 4 | "familiar" | | 5 | "traced" | | 6 | "whisper" | | 7 | "stomach" | | 8 | "flickered" |
| |
| 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 | 355 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 355 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 357 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2851 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 1 | | matches | | 0 | "On the house, he murmured with his eyes, not his mouth." |
| |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 134 | | wordCount | 2798 | | uniqueNames | 33 | | maxNameDensity | 1.5 | | worstName | "You" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Rory | 20 | | Si | 1 | | Robin | 1 | | Hood | 1 | | You | 42 | | London | 2 | | Year | 1 | | Eleven | 1 | | Silas | 7 | | Red | 1 | | Dragon | 1 | | Eva | 28 | | Gareth | 1 | | Cathays | 1 | | Cardiff | 2 | | Bay | 1 | | Paddington | 1 | | Reading | 1 | | Boots | 1 | | Chinatown | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Raven | 1 | | Nest | 1 | | KitKats | 1 | | Spar | 1 | | Saint | 1 | | Sundays | 1 | | Friday | 2 | | Aurora | 1 | | Soho | 1 | | Do | 6 |
| | persons | | 0 | "Rory" | | 1 | "Robin" | | 2 | "Hood" | | 3 | "You" | | 4 | "Silas" | | 5 | "Dragon" | | 6 | "Eva" | | 7 | "Gareth" | | 8 | "Raven" | | 9 | "Saint" |
| | places | | 0 | "London" | | 1 | "Year" | | 2 | "Cathays" | | 3 | "Cardiff" | | 4 | "Bay" | | 5 | "Paddington" | | 6 | "Reading" | | 7 | "Boots" | | 8 | "Chinatown" | | 9 | "Tottenham" | | 10 | "Court" | | 11 | "Road" | | 12 | "Spar" | | 13 | "Soho" |
| | globalScore | 0.749 | | windowScore | 0.667 | |
| 55.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 185 | | glossingSentenceCount | 7 | | matches | | 0 | "seemed unsure where to go" | | 1 | "looked like the picture on the postcard y" | | 2 | "looked like something out of a film where" | | 3 | "smelled like fried onions and stale fear a" | | 4 | "looked like when you were late" | | 5 | "looked like when it’s three weeks old" | | 6 | "not quite even on the boards" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2851 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 357 | | matches | | 0 | "loved that thing" | | 1 | "using that word" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 181 | | mean | 15.75 | | std | 18.4 | | cv | 1.168 | | sampleLengths | | 0 | 79 | | 1 | 47 | | 2 | 6 | | 3 | 27 | | 4 | 15 | | 5 | 3 | | 6 | 3 | | 7 | 53 | | 8 | 40 | | 9 | 1 | | 10 | 89 | | 11 | 1 | | 12 | 58 | | 13 | 4 | | 14 | 6 | | 15 | 14 | | 16 | 3 | | 17 | 41 | | 18 | 30 | | 19 | 19 | | 20 | 4 | | 21 | 18 | | 22 | 4 | | 23 | 3 | | 24 | 30 | | 25 | 18 | | 26 | 2 | | 27 | 10 | | 28 | 2 | | 29 | 23 | | 30 | 6 | | 31 | 24 | | 32 | 7 | | 33 | 8 | | 34 | 8 | | 35 | 44 | | 36 | 18 | | 37 | 20 | | 38 | 24 | | 39 | 2 | | 40 | 11 | | 41 | 5 | | 42 | 13 | | 43 | 30 | | 44 | 10 | | 45 | 8 | | 46 | 14 | | 47 | 67 | | 48 | 4 | | 49 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 355 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 570 | | matches | | |
| 62.83% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 15 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 357 | | ratio | 0.028 | | matches | | 0 | "I mean—her fingers worried her gold chain—after." | | 1 | "I thought—Eva swallowed—you were outside my flat by three." | | 2 | "I must have—maybe someone— Her nails scratched her knuckles like she wanted to tear a date out of them." | | 3 | "He still—Eva stopped." | | 4 | "I mean I would have—she faltered—I don’t know what I would have done." | | 5 | "All their faces had that look—brave or stupid, who could say." | | 6 | "Rory looked into Eva’s face and searched for the girl in the train window—nose pressed to the cold, mascara smudged, hope like a rash—and found a shape that felt familiar only when it tilted and caught light." | | 7 | "I’m not good at— She stopped and looked at the gold flashing under the bar’s light." | | 8 | "I wanted—I wanted you to see it." | | 9 | "Do you think we would have been like that, if— She stopped." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 192 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 2 | | adverbRatio | 0.010416666666666666 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.010416666666666666 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 357 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 357 | | mean | 7.99 | | std | 6.35 | | cv | 0.795 | | sampleLengths | | 0 | 20 | | 1 | 9 | | 2 | 11 | | 3 | 19 | | 4 | 20 | | 5 | 22 | | 6 | 14 | | 7 | 11 | | 8 | 2 | | 9 | 4 | | 10 | 17 | | 11 | 10 | | 12 | 15 | | 13 | 3 | | 14 | 3 | | 15 | 29 | | 16 | 9 | | 17 | 15 | | 18 | 12 | | 19 | 15 | | 20 | 13 | | 21 | 1 | | 22 | 11 | | 23 | 25 | | 24 | 29 | | 25 | 24 | | 26 | 1 | | 27 | 18 | | 28 | 25 | | 29 | 10 | | 30 | 5 | | 31 | 4 | | 32 | 6 | | 33 | 7 | | 34 | 7 | | 35 | 3 | | 36 | 8 | | 37 | 12 | | 38 | 21 | | 39 | 17 | | 40 | 13 | | 41 | 5 | | 42 | 14 | | 43 | 4 | | 44 | 18 | | 45 | 4 | | 46 | 3 | | 47 | 8 | | 48 | 7 | | 49 | 15 |
| |
| 37.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 46 | | diversityRatio | 0.2184873949579832 | | totalSentences | 357 | | uniqueOpeners | 78 | |
| 21.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 308 | | matches | | 0 | "Only on Sundays." | | 1 | "Somewhere between the second move" |
| | ratio | 0.006 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 179 | | totalSentences | 308 | | matches | | 0 | "He took the bag, that" | | 1 | "You hate strangers." | | 2 | "I hate tall." | | 3 | "Her mouth flashed a quick" | | 4 | "Her bright blue eyes swung" | | 5 | "She tucked the bag on" | | 6 | "She had the same eyes" | | 7 | "His eyes flicked once to" | | 8 | "He did not limp when" | | 9 | "He did not make noise." | | 10 | "You cut it, Rory." | | 11 | "You swore off black in" | | 12 | "She looked down, thumb hovering," | | 13 | "Her gaze flicked to Silas," | | 14 | "We used to haunt the" | | 15 | "We got chased by a" | | 16 | "He had a key for" | | 17 | "You memorised his name?" | | 18 | "It’s in there with my" | | 19 | "You were a nerd." |
| | ratio | 0.581 | |
| 36.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 261 | | totalSentences | 308 | | matches | | 0 | "The green neon over the" | | 1 | "London curled there in yellowed" | | 2 | "The jukebox in the corner" | | 3 | "Silas glanced up when the" | | 4 | "Shoulders angled, hair tucked behind" | | 5 | "The smell of fried garlic" | | 6 | "Yu-Fei overdid an order." | | 7 | "He took the bag, that" | | 8 | "You hate strangers." | | 9 | "I hate tall." | | 10 | "Her mouth flashed a quick" | | 11 | "Laughter, the scratch of wet" | | 12 | "A woman’s voice threaded through," | | 13 | "Rory’s hands froze on the" | | 14 | "Her bright blue eyes swung" | | 15 | "She tucked the bag on" | | 16 | "The name hung from a" | | 17 | "The woman stepped into the" | | 18 | "She had the same eyes" | | 19 | "The rest of her belonged" |
| | ratio | 0.847 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 308 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 103 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas placed two tumblers on the bar and poured out whisky that caught the map-light and the sticky green of the sign." | | 1 | "I used kitchen scissors and a bread knife and a mirror that lies." | | 2 | "He limped to the other end and fixed a drink for a man who spoke in exclamation points to no one in particular." | | 3 | "She scribbled with a free pen that had a pharmaceutical logo." | | 4 | "She slipped it into a pocket with the practiced fingers of someone who had carried too many small, important rectangles." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |