| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped aside [aside]" |
| | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.143 | | effectiveRatio | 0.071 | |
| 95.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1141 | | totalAiIsmAdverbs | 1 | | 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) | |
| 56.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1141 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "trembled" | | 1 | "velvet" | | 2 | "electric" | | 3 | "charged" | | 4 | "etched" | | 5 | "intricate" | | 6 | "magnetic" | | 7 | "perfect" | | 8 | "pristine" | | 9 | "traced" |
| |
| 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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1141 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 820 | | uniqueNames | 11 | | maxNameDensity | 2.2 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 18 | | Tube | 1 | | Camden | 2 | | Metropolitan | 1 | | Police | 1 | | Veil | 2 | | Market | 2 | | Kowalski | 1 | | Crane | 6 | | Compass | 1 | | Eva | 8 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Crane" | | 5 | "Eva" |
| | places | | | globalScore | 0.402 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.876 | | wordCount | 1141 | | matches | | 0 | "Not pointing to any rift, but straight up, useless" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 27.17 | | std | 21.55 | | cv | 0.793 | | sampleLengths | | 0 | 70 | | 1 | 15 | | 2 | 11 | | 3 | 8 | | 4 | 15 | | 5 | 1 | | 6 | 8 | | 7 | 8 | | 8 | 9 | | 9 | 81 | | 10 | 51 | | 11 | 14 | | 12 | 5 | | 13 | 8 | | 14 | 59 | | 15 | 24 | | 16 | 46 | | 17 | 40 | | 18 | 22 | | 19 | 12 | | 20 | 3 | | 21 | 2 | | 22 | 28 | | 23 | 63 | | 24 | 55 | | 25 | 23 | | 26 | 20 | | 27 | 24 | | 28 | 10 | | 29 | 27 | | 30 | 3 | | 31 | 36 | | 32 | 46 | | 33 | 5 | | 34 | 46 | | 35 | 16 | | 36 | 67 | | 37 | 19 | | 38 | 25 | | 39 | 54 | | 40 | 13 | | 41 | 49 |
| |
| 81.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 90 | | matches | | 0 | "were spattered" | | 1 | "was centered" | | 2 | "being killed" | | 3 | "been killed" | | 4 | "were curled" | | 5 | "was jammed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 822 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.014598540145985401 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006082725060827251 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 10.28 | | std | 8.4 | | cv | 0.817 | | sampleLengths | | 0 | 7 | | 1 | 18 | | 2 | 19 | | 3 | 17 | | 4 | 9 | | 5 | 6 | | 6 | 9 | | 7 | 5 | | 8 | 6 | | 9 | 5 | | 10 | 3 | | 11 | 15 | | 12 | 1 | | 13 | 8 | | 14 | 3 | | 15 | 5 | | 16 | 9 | | 17 | 15 | | 18 | 30 | | 19 | 12 | | 20 | 24 | | 21 | 23 | | 22 | 14 | | 23 | 14 | | 24 | 14 | | 25 | 5 | | 26 | 8 | | 27 | 17 | | 28 | 5 | | 29 | 18 | | 30 | 8 | | 31 | 11 | | 32 | 1 | | 33 | 5 | | 34 | 9 | | 35 | 9 | | 36 | 7 | | 37 | 39 | | 38 | 8 | | 39 | 7 | | 40 | 2 | | 41 | 2 | | 42 | 1 | | 43 | 20 | | 44 | 5 | | 45 | 17 | | 46 | 12 | | 47 | 2 | | 48 | 1 | | 49 | 2 |
| |
| 67.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.43243243243243246 | | totalSentences | 111 | | uniqueOpeners | 48 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 76 | | matches | | 0 | "She descended into the dark," | | 1 | "He held a torch that" | | 2 | "He stepped aside" | | 3 | "She knew who that was." | | 4 | "She pushed through the archway" | | 5 | "She clutched her worn leather" | | 6 | "She looked up as Quinn" | | 7 | "She didn't touch the body." | | 8 | "She looked at the floor." | | 9 | "She circled the body, her" | | 10 | "She crouched again, closer this" | | 11 | "She examined the compass." | | 12 | "She turned a page." | | 13 | "She reached into Crane's coat" | | 14 | "Her fingers found a bone" | | 15 | "She withdrew it and held" | | 16 | "It was warm." | | 17 | "Her eyes tracked the scuff" | | 18 | "She touched Crane's wrist." | | 19 | "She checked her leather watch." |
| | ratio | 0.289 | |
| 45.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 76 | | matches | | 0 | "The iron stairs groaned beneath" | | 1 | "She descended into the dark," | | 2 | "Salt-and-pepper hair, cropped close to" | | 3 | "This one smelled of ozone," | | 4 | "A constable blocked the arched" | | 5 | "He held a torch that" | | 6 | "He stepped aside" | | 7 | "Quinn flashed her warrant card." | | 8 | "Quinn's jaw tightened." | | 9 | "She knew who that was." | | 10 | "She pushed through the archway" | | 11 | "The platform stretched before her," | | 12 | "Stalls lined the former tracks," | | 13 | "The air hung thick, electric," | | 14 | "A full moon must be" | | 15 | "Eva Kowalski knelt beside a" | | 16 | "She clutched her worn leather" | | 17 | "She looked up as Quinn" | | 18 | "Quinn dropped into a crouch," | | 19 | "She didn't touch the body." |
| | ratio | 0.829 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 76 | | matches | | 0 | "If he had fallen after" | | 1 | "If he had been killed" |
| | ratio | 0.026 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 1 | | matches | | 0 | "Stalls lined the former tracks, draped in velvet and hessian, displaying glass jars of luminescent liquid, bones carved into jewelry, and tomes bound in skin th…" |
| |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "She slipped, her fingers brushing the sigils" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |