| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1078 | | 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) | |
| 67.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1078 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "etched" | | 1 | "shattered" | | 2 | "velvet" | | 3 | "silk" | | 4 | "flicked" | | 5 | "trembled" |
| |
| 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 | 96 | | matches | (empty) | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1078 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 824 | | uniqueNames | 19 | | maxNameDensity | 2.06 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 17 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Shade | 2 | | Greg | 1 | | Ives | 9 | | Vance | 3 | | Shade-spun | 1 | | Kowalski | 2 | | British | 1 | | Museum | 1 | | Ancient | 1 | | History | 1 | | Aurora | 1 | | Morris | 1 | | Eva | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Greg" | | 5 | "Ives" | | 6 | "Vance" | | 7 | "Kowalski" | | 8 | "Museum" | | 9 | "Morris" | | 10 | "Eva" |
| | places | | | globalScore | 0.468 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.928 | | wordCount | 1078 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 26.29 | | std | 24.84 | | cv | 0.945 | | sampleLengths | | 0 | 98 | | 1 | 76 | | 2 | 73 | | 3 | 31 | | 4 | 3 | | 5 | 72 | | 6 | 13 | | 7 | 2 | | 8 | 4 | | 9 | 15 | | 10 | 39 | | 11 | 10 | | 12 | 24 | | 13 | 2 | | 14 | 19 | | 15 | 74 | | 16 | 3 | | 17 | 51 | | 18 | 47 | | 19 | 4 | | 20 | 16 | | 21 | 17 | | 22 | 2 | | 23 | 11 | | 24 | 12 | | 25 | 39 | | 26 | 8 | | 27 | 41 | | 28 | 2 | | 29 | 19 | | 30 | 56 | | 31 | 18 | | 32 | 2 | | 33 | 17 | | 34 | 47 | | 35 | 9 | | 36 | 11 | | 37 | 46 | | 38 | 22 | | 39 | 15 | | 40 | 8 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 143 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 832 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.014423076923076924 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.001201923076923077 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 9.63 | | std | 7.37 | | cv | 0.765 | | sampleLengths | | 0 | 16 | | 1 | 17 | | 2 | 19 | | 3 | 5 | | 4 | 8 | | 5 | 8 | | 6 | 8 | | 7 | 5 | | 8 | 12 | | 9 | 13 | | 10 | 10 | | 11 | 20 | | 12 | 13 | | 13 | 8 | | 14 | 5 | | 15 | 7 | | 16 | 14 | | 17 | 5 | | 18 | 5 | | 19 | 3 | | 20 | 18 | | 21 | 8 | | 22 | 20 | | 23 | 17 | | 24 | 14 | | 25 | 3 | | 26 | 7 | | 27 | 24 | | 28 | 10 | | 29 | 9 | | 30 | 9 | | 31 | 13 | | 32 | 2 | | 33 | 4 | | 34 | 7 | | 35 | 2 | | 36 | 4 | | 37 | 15 | | 38 | 8 | | 39 | 3 | | 40 | 6 | | 41 | 22 | | 42 | 10 | | 43 | 24 | | 44 | 2 | | 45 | 19 | | 46 | 5 | | 47 | 7 | | 48 | 21 | | 49 | 2 |
| |
| 58.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.375 | | totalSentences | 112 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 87 | | matches | | 0 | "She straightened her coat, the" | | 1 | "Her cropped salt-and-pepper hair caught" | | 2 | "She descended the stopped escalator," | | 3 | "She didn't like places that" | | 4 | "He held out a hand." | | 5 | "He didn't move." | | 6 | "She produced a bone token" | | 7 | "His right hand clamped around" | | 8 | "She didn't touch him." | | 9 | "She leaned closer." | | 10 | "Her sharp jaw tightened." | | 11 | "Her freckled face was pale." | | 12 | "She tucked a strand of" | | 13 | "Her green eyes were wide" | | 14 | "She crouched again, this time" | | 15 | "She pulled a pen from" | | 16 | "She checked his shoes." | | 17 | "She thought of DS Morris," | | 18 | "She had learned to trust" | | 19 | "She pointed the pen at" |
| | ratio | 0.253 | |
| 17.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 87 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The abandoned Tube station beneath" | | 2 | "Lanterns burned with a greenish" | | 3 | "Tarpaulins flapped in the draught." | | 4 | "The air tasted of ozone" | | 5 | "She straightened her coat, the" | | 6 | "Her cropped salt-and-pepper hair caught" | | 7 | "Brown eyes swept the platform." | | 8 | "The worn leather watch on" | | 9 | "She descended the stopped escalator," | | 10 | "The Veil Market had rooted" | | 11 | "A Shade artisan hammered at" | | 12 | "Clientele gathered in nervous knots," | | 13 | "Harlow's hand brushed her watch." | | 14 | "She didn't like places that" | | 15 | "A gatekeeper stood by the" | | 16 | "He held out a hand." | | 17 | "Harlow flashed her warrant card." | | 18 | "He didn't move." | | 19 | "She produced a bone token" |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow followed him past stalls stacked with jars of murky fluid, bundles of dried herbs, and cages holding things that skittered when she passed." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |