| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "She gestured vaguely [vaguely]" | | 1 | "He glanced around [around]" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.222 | | effectiveRatio | 0.148 | |
| 89.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 1 |
|
| | highlights | | 0 | "suddenly" | | 1 | "really" | | 2 | "deliberately" |
| |
| 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) | |
| 16.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | word | "practiced ease" | | count | 1 |
| | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "stark" | | 1 | "comfortable" | | 2 | "flickered" | | 3 | "chill" | | 4 | "traced" | | 5 | "gloom" | | 6 | "flicker" | | 7 | "unreadable" | | 8 | "familiar" | | 9 | "stomach" | | 10 | "unspoken" | | 11 | "tension" | | 12 | "lilt" | | 13 | "practiced ease" | | 14 | "sanctuary" | | 15 | "footsteps" | | 16 | "churning" | | 17 | "facade" | | 18 | "potential" | | 19 | "profound" | | 20 | "echoing" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1426 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1114 | | uniqueNames | 11 | | maxNameDensity | 0.45 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "London" | | discoveredNames | | Tuesday | 1 | | Raven | 1 | | Nest | 1 | | London | 3 | | Silas | 3 | | Blackwood | 1 | | Rory | 5 | | Soho | 1 | | Cardiff | 1 | | Welsh | 1 | | Evan | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Rory" | | 5 | "Evan" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | 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 | 1426 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 103 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 33.16 | | std | 25.39 | | cv | 0.766 | | sampleLengths | | 0 | 92 | | 1 | 109 | | 2 | 19 | | 3 | 89 | | 4 | 58 | | 5 | 70 | | 6 | 12 | | 7 | 7 | | 8 | 26 | | 9 | 25 | | 10 | 31 | | 11 | 20 | | 12 | 62 | | 13 | 37 | | 14 | 12 | | 15 | 9 | | 16 | 25 | | 17 | 19 | | 18 | 17 | | 19 | 37 | | 20 | 45 | | 21 | 6 | | 22 | 23 | | 23 | 39 | | 24 | 7 | | 25 | 3 | | 26 | 47 | | 27 | 46 | | 28 | 51 | | 29 | 15 | | 30 | 21 | | 31 | 62 | | 32 | 7 | | 33 | 2 | | 34 | 64 | | 35 | 43 | | 36 | 38 | | 37 | 3 | | 38 | 15 | | 39 | 26 | | 40 | 49 | | 41 | 26 | | 42 | 12 |
| |
| 88.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 86 | | matches | | 0 | "been sucked" | | 1 | "was gone" | | 2 | "was buried" | | 3 | "were meant" |
| |
| 18.84% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 184 | | matches | | 0 | "was waiting" | | 1 | "was measuring" | | 2 | "was listening" | | 3 | "was rewriting" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 103 | | ratio | 0.01 | | matches | | 0 | "A flicker of something unreadable—surprise, maybe something else—crossed his features before settling into a mask of cool composure." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1118 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.03220035778175313 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008050089445438283 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 13.84 | | std | 7.31 | | cv | 0.528 | | sampleLengths | | 0 | 26 | | 1 | 14 | | 2 | 20 | | 3 | 11 | | 4 | 21 | | 5 | 20 | | 6 | 12 | | 7 | 14 | | 8 | 18 | | 9 | 31 | | 10 | 14 | | 11 | 19 | | 12 | 15 | | 13 | 14 | | 14 | 14 | | 15 | 18 | | 16 | 7 | | 17 | 21 | | 18 | 10 | | 19 | 7 | | 20 | 18 | | 21 | 1 | | 22 | 17 | | 23 | 5 | | 24 | 14 | | 25 | 17 | | 26 | 6 | | 27 | 19 | | 28 | 14 | | 29 | 5 | | 30 | 7 | | 31 | 7 | | 32 | 8 | | 33 | 18 | | 34 | 14 | | 35 | 4 | | 36 | 7 | | 37 | 11 | | 38 | 20 | | 39 | 6 | | 40 | 14 | | 41 | 13 | | 42 | 12 | | 43 | 8 | | 44 | 8 | | 45 | 21 | | 46 | 15 | | 47 | 8 | | 48 | 14 | | 49 | 12 |
| |
| 46.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.33980582524271846 | | totalSentences | 103 | | uniqueOpeners | 35 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 84 | | matches | | 0 | "Then the door opened, letting" | | 1 | "Then he started towards her." |
| | ratio | 0.024 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 84 | | matches | | 0 | "It was a scent she" | | 1 | "He wiped down a glass," | | 2 | "His gaze, a warm hazel," | | 3 | "She gave a faint nod," | | 4 | "Her shift had been long," | | 5 | "She traced the crescent scar" | | 6 | "He was a slash of" | | 7 | "His suit was sharp, tailored" | | 8 | "His hair, once a shaggy" | | 9 | "He paused for a moment," | | 10 | "It felt as if all" | | 11 | "His eyes swept the bar" | | 12 | "He straightened his tie, a" | | 13 | "She was suddenly back in" | | 14 | "He registered the man’s approach," | | 15 | "He placed the clean glass" | | 16 | "He smelled of expensive cologne" | | 17 | "His voice was deeper than" | | 18 | "She kept her own voice" | | 19 | "She did not stand." |
| | ratio | 0.643 | |
| 7.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 84 | | matches | | 0 | "The old clock above the" | | 1 | "Rory swirled the last of" | | 2 | "The Raven’s Nest smelled of" | | 3 | "It was a scent she" | | 4 | "He wiped down a glass," | | 5 | "His gaze, a warm hazel," | | 6 | "She gave a faint nod," | | 7 | "Her shift had been long," | | 8 | "She traced the crescent scar" | | 9 | "He was a slash of" | | 10 | "His suit was sharp, tailored" | | 11 | "His hair, once a shaggy" | | 12 | "He paused for a moment," | | 13 | "The glass grew slick in" | | 14 | "It felt as if all" | | 15 | "His eyes swept the bar" | | 16 | "Recognition dawned, slow at first," | | 17 | "A flicker of something unreadable—surprise," | | 18 | "He straightened his tie, a" | | 19 | "Each step he took on" |
| | ratio | 0.905 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 2 | | matches | | 0 | "He straightened his tie, a small, nervous gesture that was so familiar it made her stomach clench." | | 1 | "He had neatly edited out the shouting that curdled the air, the slammed doors, the cold, deliberate silences that lasted for days." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "She kept, a stone skipping across a frozen lake" | | 1 | "He reached, his hand moving to cover hers" | | 2 | "she said, the words deliberately neutral," |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.333 | | effectiveRatio | 0.074 | |