| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said nervously [nervously]" |
| | dialogueSentences | 19 | | tagDensity | 0.632 | | leniency | 1 | | rawRatio | 0.083 | | effectiveRatio | 0.083 | |
| 82.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 579 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 39.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 579 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "scanning" | | 1 | "gloom" | | 2 | "eyebrow" | | 3 | "etched" | | 4 | "echoing" | | 5 | "intrigue" | | 6 | "firmly" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 38 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 582 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 474 | | uniqueNames | 11 | | maxNameDensity | 1.69 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Barnes" | | discoveredNames | | Harlow | 2 | | Quinn | 8 | | Camden | 1 | | Town | 1 | | Tube | 1 | | Barnes | 7 | | Greek | 1 | | Recognition | 1 | | Veil | 1 | | Market | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Barnes" | | 3 | "Recognition" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Tube" | | 3 | "Veil" |
| | globalScore | 0.656 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | 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 | 582 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 75.13% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 30.63 | | std | 12.65 | | cv | 0.413 | | sampleLengths | | 0 | 51 | | 1 | 31 | | 2 | 12 | | 3 | 10 | | 4 | 24 | | 5 | 47 | | 6 | 19 | | 7 | 39 | | 8 | 17 | | 9 | 33 | | 10 | 29 | | 11 | 31 | | 12 | 20 | | 13 | 32 | | 14 | 53 | | 15 | 46 | | 16 | 33 | | 17 | 39 | | 18 | 16 |
| |
| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 38 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 78 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 45 | | ratio | 0.067 | | matches | | 0 | "She could make out a few words here and there - Greek letters, maybe?" | | 1 | "This was no ordinary murder - this was something deeper, darker." | | 2 | "It was her ticket into the Veil Market - a secret haven for all manner of supernatural trade and intrigue." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 471 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.01910828025477707 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.010615711252653927 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 95.72% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 12.93 | | std | 5.03 | | cv | 0.389 | | sampleLengths | | 0 | 22 | | 1 | 21 | | 2 | 8 | | 3 | 16 | | 4 | 15 | | 5 | 5 | | 6 | 7 | | 7 | 10 | | 8 | 15 | | 9 | 9 | | 10 | 4 | | 11 | 17 | | 12 | 17 | | 13 | 9 | | 14 | 5 | | 15 | 14 | | 16 | 18 | | 17 | 14 | | 18 | 7 | | 19 | 17 | | 20 | 6 | | 21 | 20 | | 22 | 7 | | 23 | 5 | | 24 | 13 | | 25 | 11 | | 26 | 17 | | 27 | 14 | | 28 | 10 | | 29 | 10 | | 30 | 15 | | 31 | 17 | | 32 | 11 | | 33 | 16 | | 34 | 15 | | 35 | 11 | | 36 | 12 | | 37 | 20 | | 38 | 14 | | 39 | 19 | | 40 | 14 | | 41 | 24 | | 42 | 8 | | 43 | 7 | | 44 | 16 |
| |
| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.7333333333333333 | | totalSentences | 45 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 93.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 38 | | matches | | 0 | "She narrowed her eyes, taking" | | 1 | "he said nervously" | | 2 | "She stepped closer to the" | | 3 | "She could make out a" | | 4 | "He held up a clear" | | 5 | "She'd seen similar tokens before," | | 6 | "she ordered, snatching the token" | | 7 | "She turned on her heel," | | 8 | "She descended the stairs rapidly," | | 9 | "It was her ticket into" | | 10 | "She glanced back at Barnes," | | 11 | "she told him firmly" |
| | ratio | 0.316 | |
| 65.26% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 30 | | totalSentences | 38 | | matches | | 0 | "Detective Harlow Quinn stood on" | | 1 | "The dim flickering of the" | | 2 | "She narrowed her eyes, taking" | | 3 | "he said nervously" | | 4 | "Quinn asked, her voice curt" | | 5 | "Barnes replied, pointing to a" | | 6 | "Quinn raised an eyebrow." | | 7 | "She stepped closer to the" | | 8 | "The victim's body was arranged" | | 9 | "Barnes nodded, swallowing hard" | | 10 | "The detective's eyes narrowed further" | | 11 | "She could make out a" | | 12 | "Quinn asked, pulling out a" | | 13 | "Barnes shook his head" | | 14 | "He held up a clear" | | 15 | "Recognition flashed in Quinn's eyes." | | 16 | "She'd seen similar tokens before," | | 17 | "This was no ordinary murder" | | 18 | "she ordered, snatching the token" | | 19 | "She turned on her heel," |
| | ratio | 0.789 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 38 | | matches | | 0 | "If there were answers to" |
| | ratio | 0.026 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "She turned on her heel, striding towards the staircase leading down to track level." |
| |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "Barnes called, hurrying to catch up" | | 1 | "Quinn called back, not slowing her pace" |
| |
| 44.74% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "she ordered (order)" | | 1 | "Quinn called back (call back)" |
| | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0.25 | | effectiveRatio | 0.211 | |