| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1119 | | 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) | |
| 37.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1119 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | word | "skipped a beat" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | |
| | highlights | | 0 | "scanning" | | 1 | "skipped a beat" | | 2 | "flickered" | | 3 | "familiar" | | 4 | "unreadable" | | 5 | "silence" | | 6 | "unspoken" | | 7 | "racing" | | 8 | "echoing" | | 9 | "depths" | | 10 | "reminder" | | 11 | "weight" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
| | 3 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "clenched her jaw" | | 2 | "The air was thick with" | | 3 | "a flash of anger" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 65 | | matches | | |
| 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 | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 747 | | uniqueNames | 8 | | maxNameDensity | 2.68 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Rory | 20 | | Silas | 9 | | Eva | 20 | | London | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Eva" |
| | places | | | globalScore | 0.161 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.897 | | wordCount | 1115 | | matches | | 0 | "not much, but it's honest work" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 25.93 | | std | 17.25 | | cv | 0.665 | | sampleLengths | | 0 | 59 | | 1 | 80 | | 2 | 66 | | 3 | 67 | | 4 | 5 | | 5 | 25 | | 6 | 19 | | 7 | 34 | | 8 | 10 | | 9 | 32 | | 10 | 21 | | 11 | 12 | | 12 | 22 | | 13 | 28 | | 14 | 12 | | 15 | 18 | | 16 | 4 | | 17 | 17 | | 18 | 8 | | 19 | 33 | | 20 | 14 | | 21 | 24 | | 22 | 17 | | 23 | 22 | | 24 | 12 | | 25 | 45 | | 26 | 12 | | 27 | 19 | | 28 | 20 | | 29 | 27 | | 30 | 26 | | 31 | 25 | | 32 | 7 | | 33 | 39 | | 34 | 29 | | 35 | 22 | | 36 | 49 | | 37 | 15 | | 38 | 11 | | 39 | 24 | | 40 | 9 | | 41 | 46 | | 42 | 29 |
| |
| 94.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 65 | | matches | | 0 | "were adorned" | | 1 | "were guarded" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 126 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 94 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 749 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.036048064085447265 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.010680907877169559 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 11.86 | | std | 5.88 | | cv | 0.496 | | sampleLengths | | 0 | 23 | | 1 | 19 | | 2 | 17 | | 3 | 23 | | 4 | 17 | | 5 | 16 | | 6 | 24 | | 7 | 13 | | 8 | 14 | | 9 | 9 | | 10 | 13 | | 11 | 17 | | 12 | 13 | | 13 | 5 | | 14 | 16 | | 15 | 20 | | 16 | 13 | | 17 | 5 | | 18 | 7 | | 19 | 18 | | 20 | 12 | | 21 | 7 | | 22 | 9 | | 23 | 25 | | 24 | 10 | | 25 | 17 | | 26 | 15 | | 27 | 2 | | 28 | 19 | | 29 | 12 | | 30 | 11 | | 31 | 11 | | 32 | 15 | | 33 | 13 | | 34 | 7 | | 35 | 5 | | 36 | 10 | | 37 | 4 | | 38 | 4 | | 39 | 4 | | 40 | 7 | | 41 | 10 | | 42 | 8 | | 43 | 33 | | 44 | 3 | | 45 | 11 | | 46 | 8 | | 47 | 16 | | 48 | 5 | | 49 | 12 |
| |
| 46.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.2765957446808511 | | totalSentences | 94 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 64 | | matches | | 0 | "Maybe it was the rain" | | 1 | "Maybe, just maybe, they could" |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 64 | | matches | | 0 | "She adjusted her delivery bag" | | 1 | "He had always been a" | | 2 | "She reached for her glass" | | 3 | "It was Eva, her childhood" | | 4 | "Her eyes, once full of" | | 5 | "she said, her voice a" | | 6 | "she added, and there was" | | 7 | "He turned to Rory." | | 8 | "She took a sip, the" | | 9 | "He gave a nod and" | | 10 | "She remembered the days when" | | 11 | "She pushed back her chair" | | 12 | "She grabbed her delivery bag" | | 13 | "She took another sip of" | | 14 | "He gave her a small" |
| | ratio | 0.234 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 64 | | matches | | 0 | "The Raven's Nest was as" | | 1 | "The air was thick with" | | 2 | "Aurora Carter, known to her" | | 3 | "She adjusted her delivery bag" | | 4 | "The bar was nearly empty," | | 5 | "Silas, the bar owner, was" | | 6 | "Rory's thoughts wandered to the" | | 7 | "He had always been a" | | 8 | "She reached for her glass" | | 9 | "The door jingled, and a" | | 10 | "Rory's heart skipped a beat." | | 11 | "It was Eva, her childhood" | | 12 | "Eva's hair, once a wild" | | 13 | "Her eyes, once full of" | | 14 | "Rory whispered, standing up" | | 15 | "Eva's gaze flickered with surprise," | | 16 | "she said, her voice a" | | 17 | "Rory replied, gesturing to the" | | 18 | "Eva nodded, sliding into the" | | 19 | "she added, and there was" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, her voice a blend of familiarity and distance" | | 1 | "Silas said, his voice steady and warm" | | 2 | "Eva asked, her voice low" |
| |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Rory whispered (whisper)" | | 1 | "she added (add)" |
| | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.286 | | effectiveRatio | 0.111 | |