| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 895 | | 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) | |
| 60.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 895 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "weight" | | 3 | "pulse" | | 4 | "reminder" |
| |
| 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 | 54 | | matches | (empty) | |
| 63.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 54 | | filterMatches | (empty) | | hedgeMatches | | 0 | "tended to" | | 1 | "seemed to" | | 2 | "tried to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 887 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 544 | | uniqueNames | 16 | | maxNameDensity | 0.74 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | London | 1 | | Eva | 1 | | Evan | 1 | | Pre-Law | 1 | | Cardiff | 2 | | University | 1 | | Silas | 4 | | Rory | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Yu-Fei" | | 3 | "Cheung" | | 4 | "Blackwood" | | 5 | "Eva" | | 6 | "Evan" | | 7 | "Silas" | | 8 | "Rory" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like stones in her mouth" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 887 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 24.64 | | std | 17.54 | | cv | 0.712 | | sampleLengths | | 0 | 61 | | 1 | 25 | | 2 | 64 | | 3 | 14 | | 4 | 6 | | 5 | 59 | | 6 | 9 | | 7 | 7 | | 8 | 55 | | 9 | 20 | | 10 | 30 | | 11 | 35 | | 12 | 9 | | 13 | 19 | | 14 | 37 | | 15 | 10 | | 16 | 45 | | 17 | 5 | | 18 | 48 | | 19 | 14 | | 20 | 15 | | 21 | 42 | | 22 | 15 | | 23 | 14 | | 24 | 43 | | 25 | 13 | | 26 | 8 | | 27 | 9 | | 28 | 31 | | 29 | 21 | | 30 | 29 | | 31 | 12 | | 32 | 25 | | 33 | 4 | | 34 | 2 | | 35 | 32 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 54 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 90 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 72 | | ratio | 0.069 | | matches | | 0 | "The bar smelled of old wood, tobacco ghosts, and something sharper—clean steel hidden beneath polish." | | 1 | "That limp—left leg, old injury—echoed in the floorboards." | | 2 | "Silas had changed—retired MI6 field agent turned bar owner, a spymaster playing landlord in a bar with a secret bookshelf room." | | 3 | "\"Time asks regardless.\" He slid a photograph from beneath the bar—a black-and-white image of two young people outside a Cardiff lecture hall." | | 4 | "Inside, the old maps watched from every wall—places they had both dreamed of, places neither had reached." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 340 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.023529411764705882 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 12.32 | | std | 7.82 | | cv | 0.635 | | sampleLengths | | 0 | 22 | | 1 | 24 | | 2 | 15 | | 3 | 6 | | 4 | 10 | | 5 | 9 | | 6 | 10 | | 7 | 27 | | 8 | 9 | | 9 | 18 | | 10 | 6 | | 11 | 8 | | 12 | 6 | | 13 | 2 | | 14 | 23 | | 15 | 8 | | 16 | 26 | | 17 | 3 | | 18 | 6 | | 19 | 7 | | 20 | 5 | | 21 | 26 | | 22 | 3 | | 23 | 21 | | 24 | 10 | | 25 | 10 | | 26 | 19 | | 27 | 11 | | 28 | 2 | | 29 | 5 | | 30 | 28 | | 31 | 9 | | 32 | 8 | | 33 | 11 | | 34 | 15 | | 35 | 22 | | 36 | 10 | | 37 | 22 | | 38 | 23 | | 39 | 5 | | 40 | 10 | | 41 | 38 | | 42 | 8 | | 43 | 6 | | 44 | 15 | | 45 | 15 | | 46 | 27 | | 47 | 15 | | 48 | 7 | | 49 | 7 |
| |
| 58.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.375 | | totalSentences | 72 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 47.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 44 | | matches | | 0 | "She hadn't intended to stop" | | 1 | "Her straight shoulder-length black hair" | | 2 | "His hazel eyes held the" | | 3 | "She had fled to London" | | 4 | "She had changed." | | 5 | "she said, pushing the bag" | | 6 | "He circled the bar, limping" | | 7 | "She stared into her glass" | | 8 | "He slid a photograph from" | | 9 | "She recognized the bright blue" | | 10 | "His gaze softened but didn't" | | 11 | "She had worn them like" | | 12 | "He touched his left knee" | | 13 | "She looked at the bookshelves" | | 14 | "He pushed the second drink" | | 15 | "She picked up the glass." | | 16 | "She walked toward the door," | | 17 | "She had survived too." | | 18 | "She pushed into the rain," |
| | ratio | 0.432 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 44 | | matches | | 0 | "The green neon of The" | | 1 | "Rory pushed through the door" | | 2 | "The bar smelled of old" | | 3 | "She hadn't intended to stop" | | 4 | "The flat above" | | 5 | "The walls wore old maps" | | 6 | "Rory shrugged out of her" | | 7 | "Her straight shoulder-length black hair" | | 8 | "The crescent-shaped scar on her" | | 9 | "Footsteps approached behind her, deliberate," | | 10 | "That limp—left leg, old injury—echoed" | | 11 | "Silas Blackwood stood in the" | | 12 | "His hazel eyes held the" | | 13 | "The air between them tightened." | | 14 | "She had fled to London" | | 15 | "She had changed." | | 16 | "Silas had changed—retired MI6 field" | | 17 | "she said, pushing the bag" | | 18 | "He circled the bar, limping" | | 19 | "The burn matched her mood." |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory pushed through the door with her shoulder, carrying a damp takeout bag from Yu-Fei Cheung's Golden Empress that smelled of ginger and regret." | | 1 | "His hazel eyes held the same quiet authority she remembered, but time had carved deeper lines around them, channels of a river that had flowed hard." | | 2 | "She recognized the bright blue of her own younger eyes, the absence of the scar, the hope that hadn't yet learned to flinch." |
| |
| 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 | 9 | | fancyCount | 1 | | fancyTags | | 0 | "she corrected (correct)" |
| | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.111 | | effectiveRatio | 0.061 | |