| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 657 | | totalAiIsmAdverbs | 1 | | 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) | |
| 31.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 657 | | totalAiIsms | 9 | | found | | 0 | | | 1 | | word | "down her spine" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | |
| | highlights | | 0 | "etched" | | 1 | "down her spine" | | 2 | "weight" | | 3 | "pulse" | | 4 | "potential" | | 5 | "glinting" | | 6 | "crystallized" | | 7 | "glint" | | 8 | "reminder" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "shiver down spine" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | 0 | "A shiver ran down her spine" | | 1 | "eyes narrowed" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 43 | | matches | | |
| 9.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 43 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 660 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.74% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 606 | | uniqueNames | 13 | | maxNameDensity | 2.15 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Tomás | 2 | | Herrera | 8 | | Raven | 1 | | Nest | 2 | | Veil | 3 | | Market | 3 | | Tube | 1 | | Camden | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Market" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | | globalScore | 0.427 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | 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 | 660 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 76.48% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 38.82 | | std | 16.22 | | cv | 0.418 | | sampleLengths | | 0 | 68 | | 1 | 12 | | 2 | 63 | | 3 | 35 | | 4 | 55 | | 5 | 56 | | 6 | 26 | | 7 | 20 | | 8 | 46 | | 9 | 42 | | 10 | 43 | | 11 | 17 | | 12 | 31 | | 13 | 38 | | 14 | 52 | | 15 | 18 | | 16 | 38 |
| |
| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 43 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 93 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 46 | | ratio | 0.13 | | matches | | 0 | "This wasn't the entrance to the Nest's secret room; it was something else entirely." | | 1 | "She holstered her pistol and fished out her phone, but the screen remained blank - no signal." | | 2 | "This was the break she needed - a chance to infiltrate the market and gather concrete evidence against the clique." | | 3 | "At the bottom, she found herself in a dimly lit, cavernous space - the abandoned Tube station beneath Camden." | | 4 | "Her presence here was already a risk; without the token, she was a trespasser." | | 5 | "Quinn's decision crystallized - follow Herrera, and risk everything, or retreat while she still could." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 604 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.028145695364238412 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.019867549668874173 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 14.35 | | std | 6.88 | | cv | 0.479 | | sampleLengths | | 0 | 22 | | 1 | 25 | | 2 | 21 | | 3 | 12 | | 4 | 11 | | 5 | 25 | | 6 | 8 | | 7 | 19 | | 8 | 17 | | 9 | 8 | | 10 | 10 | | 11 | 17 | | 12 | 8 | | 13 | 13 | | 14 | 3 | | 15 | 14 | | 16 | 18 | | 17 | 10 | | 18 | 6 | | 19 | 5 | | 20 | 17 | | 21 | 10 | | 22 | 16 | | 23 | 20 | | 24 | 3 | | 25 | 3 | | 26 | 4 | | 27 | 16 | | 28 | 20 | | 29 | 14 | | 30 | 19 | | 31 | 9 | | 32 | 13 | | 33 | 11 | | 34 | 19 | | 35 | 11 | | 36 | 6 | | 37 | 17 | | 38 | 14 | | 39 | 23 | | 40 | 15 | | 41 | 14 | | 42 | 17 | | 43 | 21 | | 44 | 18 | | 45 | 38 |
| |
| 68.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.41304347826086957 | | totalSentences | 46 | | uniqueOpeners | 19 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 43 | | matches | | 0 | "Her worn leather watch glinted" | | 1 | "He vanished into the alley" | | 2 | "She had been tracking Herrera" | | 3 | "Her heart sank." | | 4 | "She holstered her pistol and" | | 5 | "It had to be." | | 6 | "She'd heard whispers of the" | | 7 | "Her presence here was already" | | 8 | "She spotted Herrera across the" | | 9 | "Her hand instinctively went to" | | 10 | "she muttered, the words lost" |
| | ratio | 0.256 | |
| 29.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 43 | | matches | | 0 | "Rain lashed down on the" | | 1 | "Detective Harlow Quinn sprinted down" | | 2 | "Her worn leather watch glinted" | | 3 | "Quinn bellowed, but her voice" | | 4 | "Herrera darted between two pedestrians," | | 5 | "He vanished into the alley" | | 6 | "Quinn followed, her sharp jaw" | | 7 | "She had been tracking Herrera" | | 8 | "Quinn drew her pistol, thumbing" | | 9 | "The only response was the" | | 10 | "Quinn approached cautiously, pistol trained" | | 11 | "A faded sign on the" | | 12 | "Her heart sank." | | 13 | "This wasn't the entrance to" | | 14 | "The air wafting up reeked" | | 15 | "A shiver ran down her" | | 16 | "This was no ordinary entrance." | | 17 | "She holstered her pistol and" | | 18 | "Quinn called down into the" | | 19 | "The only response was the" |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn bellowed, but her voice was swallowed by the downpour" | | 1 | "she muttered, the words lost in the din of the Veil Market" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn bellowed (bellow)" | | 1 | "the figure murmured (murmur)" | | 2 | "she muttered (mutter)" |
| | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 1 | | effectiveRatio | 0.75 | |