| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Her voice cracked then [then]" |
| | dialogueSentences | 36 | | tagDensity | 0.306 | | leniency | 0.611 | | rawRatio | 0.091 | | effectiveRatio | 0.056 | |
| 54.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 880 | | totalAiIsmAdverbs | 8 | | found | | 0 | | adverb | "deliberately" | | count | 2 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "deliberately" | | 1 | "perfectly" | | 2 | "slightly" | | 3 | "slowly" | | 4 | "softly" | | 5 | "suddenly" |
| |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 880 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "calloused fingers" | | count | 1 |
| | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "chill" | | 1 | "pulse" | | 2 | "silence" | | 3 | "silk" | | 4 | "eyebrow" | | 5 | "echo" | | 6 | "calloused fingers" | | 7 | "flicked" | | 8 | "steeled" | | 9 | "echoed" | | 10 | "tension" | | 11 | "scanning" | | 12 | "affection" | | 13 | "flickered" | | 14 | "flicker" | | 15 | "dance" | | 16 | "glint" |
| |
| 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 | 51 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 51 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | 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 | 879 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 601 | | uniqueNames | 3 | | maxNameDensity | 1.83 | | worstName | "Lucien" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Lucien" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.585 | | windowScore | 0.5 | |
| 28.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared behind Lucien, arching his back then flicking his tail, eyes narrowing as he regarded the visitor with suspicion" | | 1 | "quite human" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 879 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 22.54 | | std | 12.43 | | cv | 0.552 | | sampleLengths | | 0 | 73 | | 1 | 39 | | 2 | 35 | | 3 | 26 | | 4 | 29 | | 5 | 20 | | 6 | 43 | | 7 | 25 | | 8 | 31 | | 9 | 11 | | 10 | 23 | | 11 | 35 | | 12 | 26 | | 13 | 21 | | 14 | 15 | | 15 | 21 | | 16 | 9 | | 17 | 17 | | 18 | 13 | | 19 | 30 | | 20 | 6 | | 21 | 24 | | 22 | 28 | | 23 | 23 | | 24 | 15 | | 25 | 8 | | 26 | 29 | | 27 | 24 | | 28 | 20 | | 29 | 18 | | 30 | 19 | | 31 | 14 | | 32 | 7 | | 33 | 9 | | 34 | 26 | | 35 | 9 | | 36 | 18 | | 37 | 6 | | 38 | 34 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 51 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 116 | | matches | (empty) | |
| 66.67% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 75 | | ratio | 0.027 | | matches | | 0 | "Standing there was Lucien, like a shadow molten into flesh — smooth charcoal suit perfectly pressed, one amber eye catching the dull light, the other black as midnight." | | 1 | "As he settled in, the echo of past arguments teased the edges of her mind — all the things they’d left burning between them." |
| |
| 84.13% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 602 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.05813953488372093 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.018272425249169437 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 11.72 | | std | 6.58 | | cv | 0.561 | | sampleLengths | | 0 | 6 | | 1 | 13 | | 2 | 13 | | 3 | 28 | | 4 | 13 | | 5 | 17 | | 6 | 22 | | 7 | 22 | | 8 | 3 | | 9 | 10 | | 10 | 26 | | 11 | 10 | | 12 | 19 | | 13 | 20 | | 14 | 19 | | 15 | 24 | | 16 | 16 | | 17 | 9 | | 18 | 22 | | 19 | 9 | | 20 | 5 | | 21 | 6 | | 22 | 10 | | 23 | 2 | | 24 | 5 | | 25 | 6 | | 26 | 24 | | 27 | 11 | | 28 | 13 | | 29 | 13 | | 30 | 13 | | 31 | 8 | | 32 | 8 | | 33 | 7 | | 34 | 7 | | 35 | 4 | | 36 | 10 | | 37 | 9 | | 38 | 12 | | 39 | 5 | | 40 | 5 | | 41 | 8 | | 42 | 14 | | 43 | 16 | | 44 | 6 | | 45 | 14 | | 46 | 10 | | 47 | 17 | | 48 | 11 | | 49 | 13 |
| |
| 68.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.44 | | totalSentences | 75 | | uniqueOpeners | 33 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | 0 | "Somehow, her fingers loosened around" |
| | ratio | 0.02 | |
| 68.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 50 | | matches | | 0 | "he said, tone polished but" | | 1 | "She raised an eyebrow but" | | 2 | "Her bright blue eyes fixed" | | 3 | "His half-smile was more question" | | 4 | "she murmured, folding her arms" | | 5 | "She laughed, sharp and brittle." | | 6 | "He turned slowly, eyes flickering" | | 7 | "She pointed to the scar" | | 8 | "Her voice cracked, then steeled" | | 9 | "He sighed, cane tapping softly" | | 10 | "She didn’t move, didn’t invite" | | 11 | "She quirked her lips, a" | | 12 | "He leaned on the cane," | | 13 | "she cut in, voice low" | | 14 | "she shot back, stepping forward" | | 15 | "His eyes locked with hers," | | 16 | "They breathed, a mutual storm" | | 17 | "he said, softer almost, the" | | 18 | "She didn’t smile." |
| | ratio | 0.38 | |
| 30.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 50 | | matches | | 0 | "Aurora’s hand froze on the" | | 1 | "The bone-deep chill wrapping around" | | 2 | "The door, three deadbolts and" | | 3 | "The ivory handle of his" | | 4 | "The silence stretched long enough" | | 5 | "Ptolemy, Eva’s tabby, appeared behind" | | 6 | "Aurora said, voice sharper than" | | 7 | "he said, tone polished but" | | 8 | "She raised an eyebrow but" | | 9 | "Her bright blue eyes fixed" | | 10 | "His half-smile was more question" | | 11 | "Aurora stepped aside, and he" | | 12 | "she murmured, folding her arms" | | 13 | "Lucien limped just slightly on" | | 14 | "She laughed, sharp and brittle." | | 15 | "He turned slowly, eyes flickering" | | 16 | "Something dangerously close to regret." | | 17 | "She pointed to the scar" | | 18 | "Lucien’s gaze dropped for a" | | 19 | "Her voice cracked, then steeled" |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Lucien’s amber eye flickered with a flicker of pain that wasn’t quite human." |
| |
| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "Aurora warned, shoulders blinking awake after the tension" | | 1 | "she cut in, voice low but fierce" |
| |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Aurora warned (warn)" |
| | dialogueSentences | 36 | | tagDensity | 0.139 | | leniency | 0.278 | | rawRatio | 0.4 | | effectiveRatio | 0.111 | |