| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 997 | | 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) | |
| 24.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 997 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "weight" | | 1 | "potential" | | 2 | "navigated" | | 3 | "calculated" | | 4 | "echoing" | | 5 | "anticipating" | | 6 | "fleeting" | | 7 | "echo" | | 8 | "sense of" | | 9 | "encounter" | | 10 | "loomed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 20 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 20 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 22 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 103 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 997 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 941 | | uniqueNames | 15 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 12 | | London | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Camden | 3 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" |
| | places | | | globalScore | 0.862 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 19 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared partially open" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 997 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 22 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 71.21 | | std | 40.09 | | cv | 0.563 | | sampleLengths | | 0 | 113 | | 1 | 110 | | 2 | 100 | | 3 | 96 | | 4 | 8 | | 5 | 3 | | 6 | 12 | | 7 | 15 | | 8 | 98 | | 9 | 103 | | 10 | 86 | | 11 | 84 | | 12 | 88 | | 13 | 81 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 20 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 144 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 189 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.005291005291005291 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.005291005291005291 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 22 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 22 | | mean | 45.32 | | std | 36.45 | | cv | 0.804 | | sampleLengths | | 0 | 17 | | 1 | 23 | | 2 | 18 | | 3 | 21 | | 4 | 34 | | 5 | 19 | | 6 | 19 | | 7 | 20 | | 8 | 9 | | 9 | 43 | | 10 | 100 | | 11 | 96 | | 12 | 8 | | 13 | 3 | | 14 | 12 | | 15 | 15 | | 16 | 98 | | 17 | 103 | | 18 | 86 | | 19 | 84 | | 20 | 88 | | 21 | 81 |
| |
| 68.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.4090909090909091 | | totalSentences | 22 | | uniqueOpeners | 9 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 20 | | matches | | 0 | "Further into the pursuit, the" |
| | ratio | 0.05 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 20 | | matches | | 0 | "Her target, a man who" | | 1 | "She had begun the night" | | 2 | "She maintained her focus on" | | 3 | "Her brown eyes narrowed against" | | 4 | "She took a tentative step" |
| | ratio | 0.25 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 17 | | totalSentences | 20 | | matches | | 0 | "The rain lashed against the" | | 1 | "Her target, a man who" | | 2 | "Quinn's military precision kept her" | | 3 | "The bar's distinctive green neon" | | 4 | "She had begun the night" | | 5 | "Quinn's closely cropped salt-and-pepper hair" | | 6 | "The leather watch on her" | | 7 | "She maintained her focus on" | | 8 | "Her brown eyes narrowed against" | | 9 | "The suspect cut across traffic" | | 10 | "Quinn pushed her voice out" | | 11 | "The suspect responded from ahead" | | 12 | "The suspect did not comply" | | 13 | "Tomás Herrera came to mind" | | 14 | "The suspect's choice of the" | | 15 | "Quinn paused at the top" | | 16 | "She took a tentative step" |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 20 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 6 | | matches | | 0 | "The suspect cut across traffic lanes, causing cars to brake with screeching tires that sent up additional sprays of water. Quinn followed, her boots gripping th…" | | 1 | "Further into the pursuit, the path led towards Camden, where the suspect seemed to have an ultimate destination. Quinn closed some of the gap by anticipating hi…" | | 2 | "The suspect did not comply further, instead accelerating towards a series of steps leading underground. These stairs descended to an abandoned Tube station, the…" | | 3 | "At the mouth of the station, an additional layer revealed itself as he applied a bone token to a concealed lock, opening the way to the Veil Market hidden below…" | | 4 | "The suspect's choice of the market indicated a deeper involvement than she initially thought. Perhaps the clique used this venue for exchanging information on b…" | | 5 | "Quinn paused at the top of the stairs, the rain beating down on her unchanged. Water accumulated around her feet, pooling in the cracks of the concrete. Her wat…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |