| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.159 | | leniency | 0.317 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 96.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | 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 | 75 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1343 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 832 | | uniqueNames | 9 | | maxNameDensity | 2.04 | | worstName | "Wren" | | maxWindowNameDensity | 4 | | worstWindowName | "Wren" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Wren | 17 | | Silas | 16 | | Rioja | 2 | | Bridge | 1 | | Golden | 1 | | Empress | 1 | | Aurora | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Wren" | | 3 | "Silas" | | 4 | "Rioja" | | 5 | "Aurora" |
| | places | | | globalScore | 0.478 | | windowScore | 0.333 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 2 | | matches | | 0 | "quite laughter, and for a moment the bar held something other than rain" | | 1 | "looked like a man who'd been handed the b" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1343 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 82 | | mean | 16.38 | | std | 13.77 | | cv | 0.841 | | sampleLengths | | 0 | 51 | | 1 | 43 | | 2 | 3 | | 3 | 2 | | 4 | 6 | | 5 | 44 | | 6 | 5 | | 7 | 57 | | 8 | 14 | | 9 | 59 | | 10 | 19 | | 11 | 14 | | 12 | 11 | | 13 | 6 | | 14 | 10 | | 15 | 16 | | 16 | 8 | | 17 | 7 | | 18 | 31 | | 19 | 37 | | 20 | 32 | | 21 | 3 | | 22 | 3 | | 23 | 33 | | 24 | 4 | | 25 | 5 | | 26 | 3 | | 27 | 12 | | 28 | 3 | | 29 | 14 | | 30 | 2 | | 31 | 45 | | 32 | 6 | | 33 | 9 | | 34 | 25 | | 35 | 3 | | 36 | 9 | | 37 | 4 | | 38 | 4 | | 39 | 22 | | 40 | 16 | | 41 | 20 | | 42 | 19 | | 43 | 14 | | 44 | 7 | | 45 | 40 | | 46 | 5 | | 47 | 21 | | 48 | 10 | | 49 | 18 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 75 | | matches | | 0 | "been fed" | | 1 | "been handed" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 128 | | ratio | 0.008 | | matches | | 0 | "The door swung; the neon guttered and caught." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 836 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.025119617224880382 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0035885167464114833 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 10.49 | | std | 7.96 | | cv | 0.759 | | sampleLengths | | 0 | 13 | | 1 | 38 | | 2 | 21 | | 3 | 5 | | 4 | 17 | | 5 | 3 | | 6 | 2 | | 7 | 6 | | 8 | 3 | | 9 | 16 | | 10 | 25 | | 11 | 5 | | 12 | 25 | | 13 | 3 | | 14 | 2 | | 15 | 2 | | 16 | 5 | | 17 | 7 | | 18 | 13 | | 19 | 2 | | 20 | 12 | | 21 | 18 | | 22 | 17 | | 23 | 8 | | 24 | 16 | | 25 | 8 | | 26 | 11 | | 27 | 11 | | 28 | 3 | | 29 | 6 | | 30 | 5 | | 31 | 6 | | 32 | 10 | | 33 | 11 | | 34 | 5 | | 35 | 8 | | 36 | 7 | | 37 | 23 | | 38 | 8 | | 39 | 6 | | 40 | 31 | | 41 | 27 | | 42 | 5 | | 43 | 3 | | 44 | 3 | | 45 | 11 | | 46 | 10 | | 47 | 12 | | 48 | 4 | | 49 | 5 |
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| 63.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4296875 | | totalSentences | 128 | | uniqueOpeners | 55 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Somewhere in the walls a" | | 1 | "Then he was gone, and" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 67 | | matches | | 0 | "Her tea had gone cold." | | 1 | "She ate anyway." | | 2 | "Her sleeve had ridden up," | | 3 | "His hair had gone a" | | 4 | "She gave the stranger a" | | 5 | "He looked along the wall" | | 6 | "He turned the glass again" | | 7 | "His ring clicked on the" | | 8 | "It came out of both" | | 9 | "He corked the bottle" | | 10 | "He laid a fifty on" | | 11 | "She nodded at the change" | | 12 | "She looked at him a" | | 13 | "He heard her on the" |
| | ratio | 0.209 | |
| 27.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 67 | | matches | | 0 | "The rain set in at" | | 1 | "Aurora sat at the far" | | 2 | "Her tea had gone cold." | | 3 | "Silas took the mug away," | | 4 | "She ate anyway." | | 5 | "Her sleeve had ridden up," | | 6 | "The door opened at six." | | 7 | "The man who came in" | | 8 | "The far wall." | | 9 | "Aurora's hands on the mug." | | 10 | "An old sweep, worn smooth" | | 11 | "Silas knew him by the" | | 12 | "The Wren he remembered was" | | 13 | "This one carried weight at" | | 14 | "His hair had gone a" | | 15 | "The hunger had been fed" | | 16 | "Wren shook the rain off" | | 17 | "Silas turned a glass upright" | | 18 | "Wren's eyes moved along the" | | 19 | "Aurora checked the time, drained" |
| | ratio | 0.866 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "The Wren he remembered was all elbows and appetite, a man who wore cheap suits like a dare." | | 1 | "It came out of both of them at once, a short, rusty sound that wasn't quite laughter, and for a moment the bar held something other than rain." | | 2 | "At the door Wren stopped, his reflection hanging in the glass with the green light bleeding through it." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Wren pulled, and the street came in with the rain" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.063 | | leniency | 0.127 | | rawRatio | 0 | | effectiveRatio | 0 | |