| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.304 | | leniency | 0.609 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1423 | | 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) | |
| 92.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1423 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1423 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1115 | | uniqueNames | 6 | | maxNameDensity | 1.97 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Shah" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Sergeant | 1 | | Shah | 19 | | Quinn | 22 | | Morris | 2 |
| | persons | | 0 | "Sergeant" | | 1 | "Shah" | | 2 | "Quinn" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.513 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | 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 | 1423 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 19.76 | | std | 17.04 | | cv | 0.862 | | sampleLengths | | 0 | 23 | | 1 | 51 | | 2 | 33 | | 3 | 50 | | 4 | 3 | | 5 | 33 | | 6 | 28 | | 7 | 3 | | 8 | 51 | | 9 | 22 | | 10 | 1 | | 11 | 24 | | 12 | 6 | | 13 | 12 | | 14 | 46 | | 15 | 9 | | 16 | 14 | | 17 | 25 | | 18 | 44 | | 19 | 6 | | 20 | 6 | | 21 | 10 | | 22 | 28 | | 23 | 4 | | 24 | 2 | | 25 | 2 | | 26 | 8 | | 27 | 27 | | 28 | 6 | | 29 | 5 | | 30 | 62 | | 31 | 5 | | 32 | 78 | | 33 | 5 | | 34 | 3 | | 35 | 17 | | 36 | 42 | | 37 | 6 | | 38 | 29 | | 39 | 3 | | 40 | 3 | | 41 | 2 | | 42 | 31 | | 43 | 37 | | 44 | 9 | | 45 | 37 | | 46 | 9 | | 47 | 6 | | 48 | 10 | | 49 | 17 |
| |
| 87.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 120 | | matches | | 0 | "been lifted" | | 1 | "was buttoned" | | 2 | "were coated" | | 3 | "been bricked" | | 4 | "were furred" | | 5 | "been signed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 184 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 152 | | ratio | 0.007 | | matches | | 0 | "She held it beside the steel handrail; it trembled but kept its bearing." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1121 | | adjectiveStacks | 1 | | stackExamples | | 0 | "shallow circular hollow, polished cleaner" |
| | adverbCount | 20 | | adverbRatio | 0.01784121320249777 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0026761819803746653 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 9.36 | | std | 5.87 | | cv | 0.627 | | sampleLengths | | 0 | 9 | | 1 | 14 | | 2 | 14 | | 3 | 14 | | 4 | 7 | | 5 | 16 | | 6 | 10 | | 7 | 23 | | 8 | 6 | | 9 | 16 | | 10 | 6 | | 11 | 22 | | 12 | 3 | | 13 | 23 | | 14 | 10 | | 15 | 6 | | 16 | 7 | | 17 | 11 | | 18 | 4 | | 19 | 3 | | 20 | 9 | | 21 | 6 | | 22 | 9 | | 23 | 27 | | 24 | 13 | | 25 | 9 | | 26 | 1 | | 27 | 9 | | 28 | 12 | | 29 | 3 | | 30 | 2 | | 31 | 4 | | 32 | 12 | | 33 | 19 | | 34 | 12 | | 35 | 15 | | 36 | 9 | | 37 | 14 | | 38 | 15 | | 39 | 10 | | 40 | 5 | | 41 | 13 | | 42 | 10 | | 43 | 10 | | 44 | 6 | | 45 | 6 | | 46 | 6 | | 47 | 10 | | 48 | 11 | | 49 | 3 |
| |
| 78.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.48026315789473684 | | totalSentences | 152 | | uniqueOpeners | 73 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 103 | | matches | | 0 | "His grey coat was buttoned" | | 1 | "She took in the position" | | 2 | "It made her a good" | | 3 | "Its hands were furred with" | | 4 | "Her worn leather watch showed" | | 5 | "She looked toward the tunnel" | | 6 | "He had left his phone" | | 7 | "She had taken it for" | | 8 | "Its casing was green with" | | 9 | "She held it beside the" | | 10 | "They were the same blue" | | 11 | "She left the compass with" | | 12 | "They held two broad, faint" | | 13 | "She thought of Morris’s empty" |
| | ratio | 0.136 | |
| 76.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 103 | | matches | | 0 | "The body lay at the" | | 1 | "Quinn counted them on her" | | 2 | "The rails had been lifted" | | 3 | "Water had found the track" | | 4 | "Detective Sergeant Shah waited beside" | | 5 | "Quinn stopped on the last" | | 6 | "The man lay on his" | | 7 | "Blood had pooled behind his" | | 8 | "His grey coat was buttoned" | | 9 | "Shah glanced at her notebook" | | 10 | "Quinn looked back up the" | | 11 | "A cordon ran across the" | | 12 | "A scenes-of-crime officer stepped aside" | | 13 | "Quinn crouched near the man’s" | | 14 | "The soles of his boots" | | 15 | "The steps she had just" | | 16 | "Shah said, following her gaze" | | 17 | "The lower stair treads bore" | | 18 | "None led down." | | 19 | "She took in the position" |
| | ratio | 0.767 | |
| 97.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 103 | | matches | | 0 | "By the time she had" | | 1 | "Now she looked across a" |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.283 | | leniency | 0.565 | | rawRatio | 0 | | effectiveRatio | 0 | |