| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped aside [aside]" |
| | dialogueSentences | 22 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.167 | | effectiveRatio | 0.091 | |
| 88.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 906 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 22.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 906 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "echoed" | | 1 | "footfall" | | 2 | "pulse" | | 3 | "macabre" | | 4 | "dance" | | 5 | "stark" | | 6 | "fluttered" | | 7 | "traced" | | 8 | "flicked" | | 9 | "navigating" | | 10 | "furrowing" | | 11 | "scanning" | | 12 | "echoing" | | 13 | "palpable" |
| |
| 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 | 53 | | matches | (empty) | |
| 61.99% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "happened to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 906 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 40.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 686 | | uniqueNames | 13 | | maxNameDensity | 2.19 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Thompson" | | discoveredNames | | London | 2 | | Thames | 1 | | Detective | 2 | | Harlow | 2 | | Quinn | 15 | | Tube | 1 | | Camden | 1 | | Thompson | 12 | | Eva | 2 | | Morris | 2 | | Veil | 1 | | Compass | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Thompson" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.407 | | windowScore | 0.5 | |
| 97.92% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 1 | | matches | | 0 | "darkness that seemed to cling to the edges of the platform" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 906 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 69 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 37.75 | | std | 22.12 | | cv | 0.586 | | sampleLengths | | 0 | 70 | | 1 | 69 | | 2 | 85 | | 3 | 29 | | 4 | 11 | | 5 | 63 | | 6 | 32 | | 7 | 31 | | 8 | 27 | | 9 | 26 | | 10 | 79 | | 11 | 40 | | 12 | 17 | | 13 | 19 | | 14 | 20 | | 15 | 24 | | 16 | 21 | | 17 | 19 | | 18 | 19 | | 19 | 24 | | 20 | 49 | | 21 | 73 | | 22 | 35 | | 23 | 24 |
| |
| 92.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 53 | | matches | | 0 | "was tucked" | | 1 | "been scored" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 113 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 688 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.030523255813953487 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007267441860465116 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 13.13 | | std | 6.05 | | cv | 0.46 | | sampleLengths | | 0 | 28 | | 1 | 21 | | 2 | 21 | | 3 | 16 | | 4 | 17 | | 5 | 15 | | 6 | 21 | | 7 | 16 | | 8 | 13 | | 9 | 23 | | 10 | 10 | | 11 | 23 | | 12 | 10 | | 13 | 19 | | 14 | 8 | | 15 | 3 | | 16 | 11 | | 17 | 17 | | 18 | 17 | | 19 | 18 | | 20 | 10 | | 21 | 18 | | 22 | 4 | | 23 | 5 | | 24 | 26 | | 25 | 10 | | 26 | 12 | | 27 | 5 | | 28 | 8 | | 29 | 18 | | 30 | 24 | | 31 | 25 | | 32 | 13 | | 33 | 17 | | 34 | 17 | | 35 | 4 | | 36 | 6 | | 37 | 13 | | 38 | 12 | | 39 | 5 | | 40 | 10 | | 41 | 9 | | 42 | 12 | | 43 | 8 | | 44 | 5 | | 45 | 19 | | 46 | 9 | | 47 | 12 | | 48 | 7 | | 49 | 12 |
| |
| 58.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.42028985507246375 | | totalSentences | 69 | | uniqueOpeners | 29 | |
| 62.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 53 | | matches | | 0 | "Then, with a determination that" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 53 | | matches | | 0 | "Her boots echoed on the" | | 1 | "Her eyes, brown as London's" | | 2 | "He stepped aside, revealing a" | | 3 | "It was a circle with" | | 4 | "She traced the symbol with" | | 5 | "she pressed, her eyes meeting" | | 6 | "She remembered Morris, her partner" | | 7 | "She remembered the unexplained, the" | | 8 | "She pulled out her phone," | | 9 | "She held it out to" | | 10 | "She stepped away from Thompson," | | 11 | "She pulled out her Veil" | | 12 | "She was going to find" | | 13 | "She was going to find" | | 14 | "She sought the truth, no" |
| | ratio | 0.283 | |
| 92.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 53 | | matches | | 0 | "Her boots echoed on the" | | 1 | "The station, long forgotten by" | | 2 | "The platform was a tableau" | | 3 | "Quinn approached, her salt-and-pepper hair" | | 4 | "Her eyes, brown as London's" | | 5 | "The victim lay sprawled on" | | 6 | "Quinn's gaze lingered on the" | | 7 | "Thompson greeted, his voice a" | | 8 | "Quinn nodded, her eyes never" | | 9 | "Thompson replied, pointing to the" | | 10 | "He stepped aside, revealing a" | | 11 | "It was a circle with" | | 12 | "Quinn crouched down, her worn" | | 13 | "She traced the symbol with" | | 14 | "Thompson shrugged his massive shoulders." | | 15 | "Quinn stood, her military precision" | | 16 | "she pressed, her eyes meeting" | | 17 | "Thompson hesitated, then leaned in," | | 18 | "Quinn's gaze flicked back to" | | 19 | "She remembered Morris, her partner" |
| | ratio | 0.736 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 53 | | matches | | 0 | "Even if it meant walking" | | 1 | "Even if it meant confronting" | | 2 | "Even if it meant risking" | | 3 | "Because that was what Detective" |
| | ratio | 0.075 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "She remembered the unexplained, the inexplicable, the things that defied logic and reason." | | 1 | "Then, with a determination that was almost palpable, she stepped off the platform and into the darkness, leaving Thompson and the crime scene behind." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "Thompson greeted, his voice a rumble like distant thunder" | | 1 | "she pressed, her eyes meeting his" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 22 | | tagDensity | 0.136 | | leniency | 0.273 | | rawRatio | 0.333 | | effectiveRatio | 0.091 | |