| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "She crouched again [again]" | | 1 | "she said quietly [quietly]" |
| | dialogueSentences | 54 | | tagDensity | 0.315 | | leniency | 0.63 | | rawRatio | 0.118 | | effectiveRatio | 0.074 | |
| 64.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1278 | | totalAiIsmAdverbs | 9 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | adverb | "deliberately" | | count | 1 |
| | 5 | |
| | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "precisely" | | 3 | "slightly" | | 4 | "deliberately" | | 5 | "carefully" |
| |
| 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) | |
| 76.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1278 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "tracing" | | 1 | "standard" | | 2 | "unreadable" | | 3 | "echoing" | | 4 | "flickered" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "clenched fist" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1272 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 807 | | uniqueNames | 6 | | maxNameDensity | 1.61 | | worstName | "Webb" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Webb" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Marcus | 1 | | Webb | 13 | | Pale | 1 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marcus" | | 3 | "Webb" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.695 | | windowScore | 0.5 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 2 | | matches | | 0 | "quite fit the standard mould" | | 1 | "quite mineral" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1272 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 22.71 | | std | 18.68 | | cv | 0.823 | | sampleLengths | | 0 | 63 | | 1 | 28 | | 2 | 40 | | 3 | 28 | | 4 | 4 | | 5 | 30 | | 6 | 44 | | 7 | 7 | | 8 | 16 | | 9 | 2 | | 10 | 23 | | 11 | 5 | | 12 | 8 | | 13 | 55 | | 14 | 5 | | 15 | 37 | | 16 | 9 | | 17 | 1 | | 18 | 4 | | 19 | 12 | | 20 | 28 | | 21 | 7 | | 22 | 48 | | 23 | 1 | | 24 | 36 | | 25 | 11 | | 26 | 23 | | 27 | 25 | | 28 | 43 | | 29 | 8 | | 30 | 2 | | 31 | 1 | | 32 | 58 | | 33 | 12 | | 34 | 53 | | 35 | 41 | | 36 | 35 | | 37 | 7 | | 38 | 54 | | 39 | 35 | | 40 | 1 | | 41 | 12 | | 42 | 3 | | 43 | 57 | | 44 | 4 | | 45 | 46 | | 46 | 26 | | 47 | 33 | | 48 | 2 | | 49 | 43 |
| |
| 82.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 76 | | matches | | 0 | "was supposed" | | 1 | "was clenched" | | 2 | "been handled" | | 3 | "been sealed" | | 4 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 150 | | matches | | |
| 92.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 113 | | ratio | 0.018 | | matches | | 0 | "The arms weren't thrown wide in death's agony—they were deliberate, reaching outward, fingers slightly curled." | | 1 | "She remembered Morris's last case, the one that had ended with his body found in exactly this kind of place—an abandoned underground space, no witnesses, evidence that made no sense." |
| |
| 90.71% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 810 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.050617283950617285 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.019753086419753086 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 11.26 | | std | 7.18 | | cv | 0.638 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 22 | | 3 | 4 | | 4 | 16 | | 5 | 12 | | 6 | 4 | | 7 | 17 | | 8 | 6 | | 9 | 13 | | 10 | 11 | | 11 | 17 | | 12 | 4 | | 13 | 15 | | 14 | 15 | | 15 | 9 | | 16 | 19 | | 17 | 16 | | 18 | 4 | | 19 | 3 | | 20 | 2 | | 21 | 14 | | 22 | 2 | | 23 | 6 | | 24 | 17 | | 25 | 5 | | 26 | 8 | | 27 | 2 | | 28 | 15 | | 29 | 12 | | 30 | 15 | | 31 | 11 | | 32 | 5 | | 33 | 16 | | 34 | 21 | | 35 | 3 | | 36 | 6 | | 37 | 1 | | 38 | 4 | | 39 | 3 | | 40 | 9 | | 41 | 9 | | 42 | 19 | | 43 | 7 | | 44 | 7 | | 45 | 24 | | 46 | 17 | | 47 | 1 | | 48 | 20 | | 49 | 7 |
| |
| 71.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4690265486725664 | | totalSentences | 113 | | uniqueOpeners | 53 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 70 | | matches | | 0 | "Pale blue, almost luminescent in" | | 1 | "Instead, she reached into her" | | 2 | "Finally, with a dry crack," | | 3 | "Then she stepped into the" |
| | ratio | 0.057 | |
| 71.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 70 | | matches | | 0 | "She didn't answer immediately." | | 1 | "Her gaze had caught something" | | 2 | "She crouched, pulling a pen" | | 3 | "She also knew how a" | | 4 | "She walked the perimeter, boots" | | 5 | "She turned to face him" | | 6 | "She pointed at the symbol" | | 7 | "She gestured toward the platform's" | | 8 | "She crouched again, examining the" | | 9 | "She reached out, stopped just" | | 10 | "It was clenched, knuckles white," | | 11 | "She approached slowly, bending at" | | 12 | "He studied her for a" | | 13 | "He pulled out his phone" | | 14 | "She didn't answer." | | 15 | "She slid it carefully over" | | 16 | "She applied more pressure, feeling" | | 17 | "She held it up to" | | 18 | "She remembered Morris's last case," | | 19 | "she said quietly" |
| | ratio | 0.371 | |
| 24.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 70 | | matches | | 0 | "The body lay at the" | | 1 | "Detective Harlow Quinn stood at" | | 2 | "This one was screaming." | | 3 | "She didn't answer immediately." | | 4 | "Her gaze had caught something" | | 5 | "A symbol, scratched into the" | | 6 | "She crouched, pulling a pen" | | 7 | "Webb continued, glancing at his" | | 8 | "Webb flipped a page" | | 9 | "Quinn stood slowly, her gaze" | | 10 | "The tunnel stretched into darkness" | | 11 | "Emergency lighting cast everything in" | | 12 | "Webb's frown deepened" | | 13 | "She also knew how a" | | 14 | "The body lay too precisely," | | 15 | "The arms weren't thrown wide" | | 16 | "She walked the perimeter, boots" | | 17 | "Webb consulted his notes again" | | 18 | "Quinn stopped walking." | | 19 | "Webb's eyes narrowed." |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "She applied more pressure, feeling the resistance of stiffened muscle, the stubbornness of a hand that had died holding onto something." | | 1 | "She remembered Morris's last case, the one that had ended with his body found in exactly this kind of place—an abandoned underground space, no witnesses, eviden…" | | 2 | "Webb stared at her, his face cycling through confusion, disbelief, and finally something that looked almost like fear." |
| |
| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "DI Marcus Webb appeared, his breath fogging in the unseasonably cold air" | | 1 | "She gestured, the darkness pressing in from both ends" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Webb continued (continue)" |
| | dialogueSentences | 54 | | tagDensity | 0.074 | | leniency | 0.148 | | rawRatio | 0.25 | | effectiveRatio | 0.037 | |