| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 29 | | adverbTagCount | 1 | | adverbTags | | 0 | "he asked quietly [quietly]" |
| | dialogueSentences | 67 | | tagDensity | 0.433 | | leniency | 0.866 | | rawRatio | 0.034 | | effectiveRatio | 0.03 | |
| 82.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 855 | | totalAiIsmAdverbs | 3 | | found | | 0 | | adverb | "barely above a whisper" | | count | 1 |
| | 1 | | | 2 | |
| | highlights | | 0 | "barely above a whisper" | | 1 | "really" | | 2 | "slightly" |
| |
| 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) | |
| 0.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 855 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | word | "moth to a flame" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "familiar" | | 1 | "echoing" | | 2 | "moth to a flame" | | 3 | "beacon" | | 4 | "scanned" | | 5 | "whisper" | | 6 | "tinged" | | 7 | "tension" | | 8 | "weight" | | 9 | "eyebrow" | | 10 | "silence" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
| | 1 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "the weight of her words" | | 1 | "a glimmer of hope" |
| |
| 97.22% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 60 | | matches | | 0 | "d in surprise" | | 1 | "d with disbelief" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 851 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 29 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 530 | | uniqueNames | 8 | | maxNameDensity | 1.13 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Blackwood | 1 | | Silas | 4 | | Evan | 4 | | Aurora | 6 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Blackwood" | | 3 | "Silas" | | 4 | "Evan" | | 5 | "Aurora" |
| | places | | | globalScore | 0.934 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 851 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 17.02 | | std | 11.83 | | cv | 0.695 | | sampleLengths | | 0 | 72 | | 1 | 40 | | 2 | 12 | | 3 | 12 | | 4 | 21 | | 5 | 14 | | 6 | 11 | | 7 | 8 | | 8 | 50 | | 9 | 27 | | 10 | 15 | | 11 | 15 | | 12 | 15 | | 13 | 16 | | 14 | 16 | | 15 | 10 | | 16 | 17 | | 17 | 8 | | 18 | 7 | | 19 | 9 | | 20 | 17 | | 21 | 16 | | 22 | 22 | | 23 | 22 | | 24 | 15 | | 25 | 9 | | 26 | 34 | | 27 | 15 | | 28 | 12 | | 29 | 13 | | 30 | 7 | | 31 | 16 | | 32 | 12 | | 33 | 21 | | 34 | 10 | | 35 | 22 | | 36 | 7 | | 37 | 10 | | 38 | 24 | | 39 | 14 | | 40 | 10 | | 41 | 11 | | 42 | 12 | | 43 | 8 | | 44 | 20 | | 45 | 9 | | 46 | 14 | | 47 | 9 | | 48 | 7 | | 49 | 38 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 114 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 98 | | ratio | 0.01 | | matches | | 0 | "He looked different—older, more worn—but there was no mistaking him." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 534 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.0299625468164794 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.011235955056179775 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 8.68 | | std | 5.34 | | cv | 0.615 | | sampleLengths | | 0 | 21 | | 1 | 22 | | 2 | 13 | | 3 | 16 | | 4 | 14 | | 5 | 12 | | 6 | 14 | | 7 | 8 | | 8 | 4 | | 9 | 9 | | 10 | 3 | | 11 | 16 | | 12 | 5 | | 13 | 10 | | 14 | 4 | | 15 | 8 | | 16 | 3 | | 17 | 7 | | 18 | 1 | | 19 | 19 | | 20 | 16 | | 21 | 5 | | 22 | 10 | | 23 | 15 | | 24 | 12 | | 25 | 8 | | 26 | 7 | | 27 | 8 | | 28 | 7 | | 29 | 12 | | 30 | 3 | | 31 | 16 | | 32 | 13 | | 33 | 3 | | 34 | 7 | | 35 | 3 | | 36 | 13 | | 37 | 4 | | 38 | 3 | | 39 | 5 | | 40 | 7 | | 41 | 8 | | 42 | 1 | | 43 | 9 | | 44 | 8 | | 45 | 10 | | 46 | 6 | | 47 | 9 | | 48 | 13 | | 49 | 7 |
| |
| 71.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45918367346938777 | | totalSentences | 98 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 55 | | matches | | 0 | "She shook off the drizzle" | | 1 | "She made her way to" | | 2 | "His hazel eyes met hers," | | 3 | "he greeted, his voice a" | | 4 | "she replied, sliding onto a" | | 5 | "he said, tapping his leg" | | 6 | "she said, glancing around" | | 7 | "She took a sip, savoring" | | 8 | "It was Evan, her ex." | | 9 | "He looked different—older, more worn—but" | | 10 | "He hesitated for a moment" | | 11 | "he said, his voice tinged" | | 12 | "she replied, her tone cool" | | 13 | "he said, sliding onto the" | | 14 | "she said, not meeting his" | | 15 | "he said with a bitter" | | 16 | "he asked, his voice softening" | | 17 | "she said, finally looking at" | | 18 | "he said, running a hand" | | 19 | "she said, her voice hardening" |
| | ratio | 0.709 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 55 | | matches | | 0 | "Aurora Carter pushed open the" | | 1 | "The green neon sign outside" | | 2 | "She shook off the drizzle" | | 3 | "The walls, adorned with old" | | 4 | "She made her way to" | | 5 | "Silas Blackwood, the bar's owner," | | 6 | "His hazel eyes met hers," | | 7 | "he greeted, his voice a" | | 8 | "she replied, sliding onto a" | | 9 | "he said, tapping his leg" | | 10 | "she said, glancing around" | | 11 | "Silas nodded and poured her" | | 12 | "She took a sip, savoring" | | 13 | "Aurora's breath caught in her" | | 14 | "It was Evan, her ex." | | 15 | "He looked different—older, more worn—but" | | 16 | "Evan's eyes scanned the room," | | 17 | "He hesitated for a moment" | | 18 | "he said, his voice tinged" | | 19 | "she replied, her tone cool" |
| | ratio | 0.964 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 10 | | matches | | 0 | "he greeted, his voice a gravelly rumble" | | 1 | "she replied, her tone cool" | | 2 | "she said, not meeting his gaze" | | 3 | "he asked, his voice softening" | | 4 | "she said, her voice hardening" | | 5 | "she said, her voice barely above a whisper" | | 6 | "he asked, his eyes searching hers" | | 7 | "she asked, her tone challenging" | | 8 | "he said, his voice firm" | | 9 | "he said, a small smile tugging at his lips" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 28 | | fancyCount | 3 | | fancyTags | | 0 | "he insisted (insist)" | | 1 | "he admitted (admit)" | | 2 | "he repeated (repeat)" |
| | dialogueSentences | 67 | | tagDensity | 0.418 | | leniency | 0.836 | | rawRatio | 0.107 | | effectiveRatio | 0.09 | |