| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "Rory said again [again]" |
| | dialogueSentences | 68 | | tagDensity | 0.309 | | leniency | 0.618 | | rawRatio | 0.048 | | effectiveRatio | 0.029 | |
| 84.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1319 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 96.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1319 | | 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 | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 19 | | totalWords | 1326 | | ratio | 0.014 | | matches | | 0 | "Rory" | | 1 | "no, I don't think so" | | 2 | "this is a debt I can't carry" | | 3 | "avoiding" | | 4 | "Gone to France, don't die." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 70.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 691 | | uniqueNames | 12 | | maxNameDensity | 1.59 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 11 | | Moreau | 1 | | Downstairs | 1 | | Ptolemy | 2 | | Lucien | 5 | | Savile | 1 | | Row | 1 | | Eva | 2 | | Started | 1 | | Brick | 1 | | Lane | 1 | | Amber | 1 |
| | persons | | 0 | "Rory" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Eva" | | 5 | "Started" | | 6 | "Amber" |
| | places | | | globalScore | 0.704 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 1326 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 113 | | matches | | 0 | "let that sit" | | 1 | "shaved that morning" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 18.16 | | std | 22.34 | | cv | 1.23 | | sampleLengths | | 0 | 21 | | 1 | 11 | | 2 | 70 | | 3 | 6 | | 4 | 12 | | 5 | 6 | | 6 | 9 | | 7 | 34 | | 8 | 63 | | 9 | 5 | | 10 | 3 | | 11 | 3 | | 12 | 25 | | 13 | 1 | | 14 | 1 | | 15 | 78 | | 16 | 30 | | 17 | 3 | | 18 | 43 | | 19 | 3 | | 20 | 8 | | 21 | 3 | | 22 | 11 | | 23 | 6 | | 24 | 64 | | 25 | 15 | | 26 | 9 | | 27 | 2 | | 28 | 3 | | 29 | 35 | | 30 | 3 | | 31 | 44 | | 32 | 3 | | 33 | 3 | | 34 | 1 | | 35 | 19 | | 36 | 39 | | 37 | 3 | | 38 | 37 | | 39 | 9 | | 40 | 4 | | 41 | 1 | | 42 | 4 | | 43 | 24 | | 44 | 19 | | 45 | 4 | | 46 | 3 | | 47 | 1 | | 48 | 71 | | 49 | 5 |
| |
| 94.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 65 | | matches | | 0 | "being startled" | | 1 | "got picked" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 121 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 113 | | ratio | 0.053 | | matches | | 0 | "Not much of it — a fine beading across the platinum blond, the kind that came from twelve steps between a car door and a doorway." | | 1 | "Six inches of gap, her bare foot braced behind the door, and she was aware — furious about it — of exactly how she looked." | | 2 | "He knew nobody called her that; that was the entire architecture of it." | | 3 | "He came in the way he came into everywhere, which was as if he'd already been there and was returning — three steps, a pause, a slow read of the room." | | 4 | "Leaned against the counter with her arms folded and her left wrist tucked under her right elbow — the crescent scar, an old habit, hiding a thing that had never needed hiding." | | 5 | "She had not decided to; her feet went, and the rest followed, and she stopped close enough to see the rain still sitting in his hair and the fact that he hadn't shaved that morning, which she'd never once seen." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 670 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.026865671641791045 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008955223880597015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 11.73 | | std | 12.65 | | cv | 1.078 | | sampleLengths | | 0 | 21 | | 1 | 11 | | 2 | 26 | | 3 | 4 | | 4 | 4 | | 5 | 36 | | 6 | 6 | | 7 | 12 | | 8 | 6 | | 9 | 6 | | 10 | 3 | | 11 | 17 | | 12 | 17 | | 13 | 7 | | 14 | 25 | | 15 | 4 | | 16 | 4 | | 17 | 23 | | 18 | 5 | | 19 | 3 | | 20 | 3 | | 21 | 12 | | 22 | 13 | | 23 | 1 | | 24 | 1 | | 25 | 4 | | 26 | 13 | | 27 | 61 | | 28 | 30 | | 29 | 3 | | 30 | 9 | | 31 | 30 | | 32 | 4 | | 33 | 3 | | 34 | 8 | | 35 | 3 | | 36 | 5 | | 37 | 6 | | 38 | 6 | | 39 | 31 | | 40 | 4 | | 41 | 4 | | 42 | 13 | | 43 | 12 | | 44 | 11 | | 45 | 4 | | 46 | 5 | | 47 | 4 | | 48 | 2 | | 49 | 3 |
| |
| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4424778761061947 | | totalSentences | 113 | | uniqueOpeners | 50 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Somewhere below, the tray got" |
| | ratio | 0.019 | |
| 49.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 54 | | matches | | 0 | "His suit was dry." | | 1 | "His shoes were not." | | 2 | "He had the ivory cane" | | 3 | "His mouth moved, one corner" | | 4 | "He looked past her shoulder," | | 5 | "He knew nobody called her" | | 6 | "He'd said it the first" | | 7 | "He looked at the cat," | | 8 | "He came in the way" | | 9 | "He put his cane down" | | 10 | "He did not sit." | | 11 | "She carried the mug to" | | 12 | "Her hands wanted something to" | | 13 | "She put the mug down." | | 14 | "He said it quietly" | | 15 | "He looked at her." | | 16 | "Her arms came unfolded" | | 17 | "He looked out at Brick" | | 18 | "he said, He turned his" | | 19 | "He let that sit exactly" |
| | ratio | 0.426 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 54 | | matches | | 0 | "The third deadbolt stuck, like" | | 1 | "Lucien Moreau stood on the" | | 2 | "His suit was dry." | | 3 | "His shoes were not." | | 4 | "He had the ivory cane" | | 5 | "His mouth moved, one corner" | | 6 | "Downstairs, someone in the curry" | | 7 | "The stairwell held the smell" | | 8 | "Rory didn't move out of" | | 9 | "Eva's oversized university jumper." | | 10 | "Yesterday's plait coming apart." | | 11 | "A biro mark on the" | | 12 | "He looked past her shoulder," | | 13 | "Nobody called her that." | | 14 | "He knew nobody called her" | | 15 | "He'd said it the first" | | 16 | "Ptolemy shoved past her ankle" | | 17 | "Lucien didn't bend to stroke" | | 18 | "He looked at the cat," | | 19 | "Rory let go of the" |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "He came in the way he came into everywhere, which was as if he'd already been there and was returning — three steps, a pause, a slow read of the room." | | 1 | "Leaned against the counter with her arms folded and her left wrist tucked under her right elbow — the crescent scar, an old habit, hiding a thing that had never…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "he agreed (agree)" | | 1 | "Rory reached (reach)" |
| | dialogueSentences | 68 | | tagDensity | 0.191 | | leniency | 0.382 | | rawRatio | 0.154 | | effectiveRatio | 0.059 | |