| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 62 | | tagDensity | 0.29 | | leniency | 0.581 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1633 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 69.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1633 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "measured" | | 1 | "silence" | | 2 | "velvet" | | 3 | "weight" | | 4 | "etched" | | 5 | "perfect" | | 6 | "trembled" | | 7 | "quivered" |
| |
| 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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 177 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1633 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1146 | | uniqueNames | 12 | | maxNameDensity | 2.18 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Broome" | | discoveredNames | | Detective | 2 | | Harlow | 1 | | Quinn | 25 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 4 | | Sergeant | 1 | | Callum | 1 | | Broome | 25 | | Venn | 8 | | One | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Sergeant" | | 4 | "Broome" | | 5 | "Venn" |
| | places | | | globalScore | 0.409 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1633 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 177 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 83 | | mean | 19.67 | | std | 16.01 | | cv | 0.814 | | sampleLengths | | 0 | 54 | | 1 | 51 | | 2 | 4 | | 3 | 9 | | 4 | 63 | | 5 | 18 | | 6 | 23 | | 7 | 18 | | 8 | 48 | | 9 | 9 | | 10 | 59 | | 11 | 11 | | 12 | 17 | | 13 | 11 | | 14 | 27 | | 15 | 6 | | 16 | 26 | | 17 | 48 | | 18 | 28 | | 19 | 10 | | 20 | 9 | | 21 | 40 | | 22 | 3 | | 23 | 23 | | 24 | 16 | | 25 | 21 | | 26 | 6 | | 27 | 3 | | 28 | 5 | | 29 | 29 | | 30 | 4 | | 31 | 3 | | 32 | 41 | | 33 | 22 | | 34 | 9 | | 35 | 11 | | 36 | 14 | | 37 | 44 | | 38 | 6 | | 39 | 10 | | 40 | 34 | | 41 | 19 | | 42 | 23 | | 43 | 1 | | 44 | 8 | | 45 | 36 | | 46 | 15 | | 47 | 10 | | 48 | 11 | | 49 | 11 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 188 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 177 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.008703220191470844 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0017406440382941688 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 177 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 177 | | mean | 9.23 | | std | 6.24 | | cv | 0.676 | | sampleLengths | | 0 | 22 | | 1 | 5 | | 2 | 4 | | 3 | 23 | | 4 | 11 | | 5 | 21 | | 6 | 19 | | 7 | 4 | | 8 | 7 | | 9 | 2 | | 10 | 4 | | 11 | 13 | | 12 | 8 | | 13 | 22 | | 14 | 16 | | 15 | 18 | | 16 | 13 | | 17 | 10 | | 18 | 14 | | 19 | 4 | | 20 | 16 | | 21 | 32 | | 22 | 9 | | 23 | 14 | | 24 | 19 | | 25 | 7 | | 26 | 19 | | 27 | 11 | | 28 | 10 | | 29 | 7 | | 30 | 11 | | 31 | 3 | | 32 | 24 | | 33 | 6 | | 34 | 10 | | 35 | 16 | | 36 | 6 | | 37 | 11 | | 38 | 7 | | 39 | 9 | | 40 | 15 | | 41 | 14 | | 42 | 14 | | 43 | 4 | | 44 | 6 | | 45 | 9 | | 46 | 7 | | 47 | 15 | | 48 | 9 | | 49 | 9 |
| |
| 42.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3050847457627119 | | totalSentences | 177 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 128 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "Her boots found the old" | | 2 | "He wore a dark wool" | | 3 | "Its casing wore a green" | | 4 | "She leaned closer to the" | | 5 | "His collar stood stiff with" | | 6 | "She moved to the victim's" | | 7 | "His knuckles showed no bruise," | | 8 | "She touched the compass casing" | | 9 | "It ignored the body." | | 10 | "It pointed past the blood," | | 11 | "She glanced at Venn's fingers" | | 12 | "She lifted Venn's hand by" | | 13 | "It sat open, velvet lining" | | 14 | "She crossed to it." | | 15 | "She tilted the box." | | 16 | "She lifted one foot." | | 17 | "She followed the edge with" | | 18 | "She looked back at the" | | 19 | "She used the pen to" |
| | ratio | 0.18 | |
| 26.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 128 | | matches | | 0 | "The bone token warmed in" | | 1 | "The Veil Market had chewed" | | 2 | "Quinn kept her shoulders square," | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "The worn leather watch on" | | 5 | "The gate groaned open." | | 6 | "A uniformed constable flinched at" | | 7 | "Quinn moved past him." | | 8 | "Her boots found the old" | | 9 | "The Veil Market had shuttered" | | 10 | "The air held the sour" | | 11 | "Detective Sergeant Callum Broome stood" | | 12 | "Quinn snapped on gloves" | | 13 | "Broome nodded toward the body" | | 14 | "Quinn studied the scene before" | | 15 | "Silas Venn lay on his" | | 16 | "He wore a dark wool" | | 17 | "A brass compass rested on" | | 18 | "Its casing wore a green" | | 19 | "Quinn lifted the tape with" |
| | ratio | 0.867 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "He wore a dark wool coat, expensive, cut for a man who wanted respectability while trading in forbidden goods." | | 1 | "The dark pool around Venn's head showed narrow arcs, broader heels, and one long drag that curved toward the service door behind the advertisement." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |