| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 1 | | adverbTags | | 0 | "Nora spoke softly [softly]" |
| | dialogueSentences | 149 | | tagDensity | 0.168 | | leniency | 0.336 | | rawRatio | 0.04 | | effectiveRatio | 0.013 | |
| 97.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2219 | | 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) | |
| 93.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2219 | | totalAiIsms | 3 | | 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 | 129 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 253 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2219 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 43 | | unquotedAttributions | 1 | | matches | | 0 | "Halfway there, Nora spoke again." |
| |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 107 | | wordCount | 1259 | | uniqueNames | 6 | | maxNameDensity | 3.02 | | worstName | "Nora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Silas" | | discoveredNames | | Silas | 37 | | Raven | 1 | | Nest | 1 | | Aurora | 29 | | Nora | 38 | | Magda | 1 |
| | persons | | 0 | "Silas" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Aurora" | | 4 | "Nora" | | 5 | "Magda" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 2219 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 253 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 164 | | mean | 13.53 | | std | 14.74 | | cv | 1.089 | | sampleLengths | | 0 | 32 | | 1 | 81 | | 2 | 62 | | 3 | 12 | | 4 | 8 | | 5 | 1 | | 6 | 6 | | 7 | 50 | | 8 | 14 | | 9 | 4 | | 10 | 5 | | 11 | 9 | | 12 | 18 | | 13 | 11 | | 14 | 8 | | 15 | 8 | | 16 | 19 | | 17 | 5 | | 18 | 24 | | 19 | 23 | | 20 | 53 | | 21 | 7 | | 22 | 17 | | 23 | 4 | | 24 | 9 | | 25 | 14 | | 26 | 4 | | 27 | 3 | | 28 | 5 | | 29 | 54 | | 30 | 5 | | 31 | 5 | | 32 | 10 | | 33 | 5 | | 34 | 6 | | 35 | 5 | | 36 | 11 | | 37 | 25 | | 38 | 6 | | 39 | 4 | | 40 | 5 | | 41 | 64 | | 42 | 5 | | 43 | 13 | | 44 | 4 | | 45 | 6 | | 46 | 5 | | 47 | 10 | | 48 | 11 | | 49 | 33 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 129 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 226 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 253 | | ratio | 0.004 | | matches | | 0 | "He offered his hand; she caught his forearm instead, and for a moment they stood close enough to embrace without doing it." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1260 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar." |
| | adverbCount | 32 | | adverbRatio | 0.025396825396825397 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007936507936507937 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 253 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 253 | | mean | 8.77 | | std | 6.54 | | cv | 0.745 | | sampleLengths | | 0 | 32 | | 1 | 11 | | 2 | 32 | | 3 | 7 | | 4 | 5 | | 5 | 26 | | 6 | 6 | | 7 | 28 | | 8 | 8 | | 9 | 20 | | 10 | 6 | | 11 | 6 | | 12 | 8 | | 13 | 1 | | 14 | 6 | | 15 | 8 | | 16 | 10 | | 17 | 10 | | 18 | 22 | | 19 | 9 | | 20 | 5 | | 21 | 4 | | 22 | 5 | | 23 | 9 | | 24 | 8 | | 25 | 10 | | 26 | 7 | | 27 | 4 | | 28 | 8 | | 29 | 8 | | 30 | 16 | | 31 | 3 | | 32 | 5 | | 33 | 5 | | 34 | 19 | | 35 | 4 | | 36 | 17 | | 37 | 2 | | 38 | 7 | | 39 | 25 | | 40 | 21 | | 41 | 7 | | 42 | 8 | | 43 | 9 | | 44 | 4 | | 45 | 9 | | 46 | 5 | | 47 | 9 | | 48 | 4 | | 49 | 3 |
| |
| 45.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.20553359683794467 | | totalSentences | 253 | | uniqueOpeners | 52 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 116 | | matches | | 0 | "Then she drank and winced." | | 1 | "Then she stopped, one hand" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 116 | | matches | | 0 | "He wore his reading glasses" | | 1 | "Her hair was cropped close" | | 2 | "She moved from photograph to" | | 3 | "she spoke to Silas" | | 4 | "His left foot caught for" | | 5 | "He offered his hand; she" | | 6 | "He took a bottle of" | | 7 | "She looked past Silas at" | | 8 | "She had never seen him" | | 9 | "He dragged a stool close" | | 10 | "She put her glass down" | | 11 | "It showed three people outside" | | 12 | "She brought her glass to" | | 13 | "Her eyes travelled to Aurora’s" | | 14 | "She rinsed it though it" | | 15 | "He stared at the bar." | | 16 | "His knee resisted him." | | 17 | "He took his glass behind" | | 18 | "She crossed towards the narrow" | | 19 | "He held it in his" |
| | ratio | 0.233 | |
| 3.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 116 | | matches | | 0 | "Aurora had come downstairs for" | | 1 | "The Raven’s Nest had emptied" | | 2 | "Someone had left a scarf" | | 3 | "Aurora set the glass beside" | | 4 | "Silas had not noticed her." | | 5 | "He wore his reading glasses" | | 6 | "The woman took off her" | | 7 | "Her hair was cropped close" | | 8 | "She moved from photograph to" | | 9 | "she spoke to Silas" | | 10 | "Aurora saw his hand lift" | | 11 | "Silas came around the end" | | 12 | "His left foot caught for" | | 13 | "Nora watched it and looked" | | 14 | "He offered his hand; she" | | 15 | "Nora told him" | | 16 | "Aurora picked up the glass" | | 17 | "Silas kept his eyes on" | | 18 | "Nora gave Aurora a quick," | | 19 | "Aurora held up her glass" |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "Someone had left a scarf hooked over the back of a chair, and the green neon sign above the door washed the windows with a colour that made the street look subm…" | | 1 | "Beneath it she wore a grey suit with one cuff folded back, as though she had washed her hands somewhere and forgotten to put herself in order again." | | 2 | "She looked past Silas at the shelves, the old maps with their edges curling inside mismatched frames, the row of photographs that gave customers something to po…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 1 | | matches | | 0 | "He touched, as though the folded paper might still be there" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 5 | | fancyTags | | 0 | "she spoke (speak)" | | 1 | "Silas muttered (mutter)" | | 2 | "Nora spoke softly (speak)" | | 3 | "Silas added (add)" | | 4 | "Nora continued (continue)" |
| | dialogueSentences | 149 | | tagDensity | 0.107 | | leniency | 0.215 | | rawRatio | 0.313 | | effectiveRatio | 0.067 | |