| 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 | 1210 | | 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) | |
| 87.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1210 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "scanned" | | 1 | "silence" | | 2 | "weight" |
| |
| 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1210 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.74% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 929 | | uniqueNames | 18 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Soho | 1 | | Harlow | 1 | | Quinn | 19 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Oxford | 1 | | Street | 2 | | Tomás | 12 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Tube | 2 | | Camden | 3 | | High | 1 | | Morris | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Oxford" | | 2 | "Street" | | 3 | "Tottenham" | | 4 | "Court" | | 5 | "Road" | | 6 | "Tube" | | 7 | "Camden" | | 8 | "High" |
| | globalScore | 0.477 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1210 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 30.25 | | std | 20.01 | | cv | 0.661 | | sampleLengths | | 0 | 53 | | 1 | 53 | | 2 | 69 | | 3 | 54 | | 4 | 7 | | 5 | 12 | | 6 | 36 | | 7 | 13 | | 8 | 14 | | 9 | 39 | | 10 | 12 | | 11 | 11 | | 12 | 44 | | 13 | 58 | | 14 | 11 | | 15 | 12 | | 16 | 52 | | 17 | 18 | | 18 | 7 | | 19 | 13 | | 20 | 33 | | 21 | 14 | | 22 | 52 | | 23 | 13 | | 24 | 15 | | 25 | 53 | | 26 | 18 | | 27 | 51 | | 28 | 4 | | 29 | 61 | | 30 | 49 | | 31 | 14 | | 32 | 58 | | 33 | 10 | | 34 | 17 | | 35 | 53 | | 36 | 16 | | 37 | 19 | | 38 | 18 | | 39 | 54 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 159 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 936 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.003205128205128205 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002136752136752137 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 72.90% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 11.63 | | std | 3.87 | | cv | 0.332 | | sampleLengths | | 0 | 19 | | 1 | 12 | | 2 | 13 | | 3 | 9 | | 4 | 8 | | 5 | 7 | | 6 | 13 | | 7 | 10 | | 8 | 15 | | 9 | 10 | | 10 | 11 | | 11 | 8 | | 12 | 10 | | 13 | 13 | | 14 | 17 | | 15 | 13 | | 16 | 18 | | 17 | 23 | | 18 | 7 | | 19 | 12 | | 20 | 10 | | 21 | 9 | | 22 | 10 | | 23 | 7 | | 24 | 13 | | 25 | 14 | | 26 | 12 | | 27 | 10 | | 28 | 6 | | 29 | 11 | | 30 | 12 | | 31 | 11 | | 32 | 9 | | 33 | 10 | | 34 | 11 | | 35 | 14 | | 36 | 14 | | 37 | 16 | | 38 | 3 | | 39 | 13 | | 40 | 12 | | 41 | 11 | | 42 | 12 | | 43 | 15 | | 44 | 13 | | 45 | 6 | | 46 | 12 | | 47 | 6 | | 48 | 18 | | 49 | 7 |
| |
| 53.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.375 | | totalSentences | 104 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 80.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 83 | | matches | | 0 | "Her sharp jaw flexed as" | | 1 | "His short curly dark brown" | | 2 | "His olive skin showed rain" | | 3 | "His warm brown eyes found" | | 4 | "His Saint Christopher medallion swung" | | 5 | "His scar ran along his" | | 6 | "She held her cuffs in" | | 7 | "She planted her boots beside" | | 8 | "Her brown eyes stayed on" | | 9 | "He knocked his glass across" | | 10 | "She landed in a puddle" | | 11 | "She kept the cuffs in" | | 12 | "Her boots skidded." | | 13 | "She struck the rail with" | | 14 | "He weaved between a bus" | | 15 | "She vaulted a bus stop" | | 16 | "Her breath rasped in her" | | 17 | "She ignored the static and" | | 18 | "He opened the medical bag" | | 19 | "He tied the knot, patted" |
| | ratio | 0.349 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 83 | | matches | | 0 | "The green neon sign over" | | 1 | "Detective Harlow Quinn ducked beneath" | | 2 | "Her sharp jaw flexed as" | | 3 | "Lamps threw amber pools over" | | 4 | "The bass from a speaker" | | 5 | "Quinn moved past drinkers with" | | 6 | "Tomás Herrera sat at the" | | 7 | "His short curly dark brown" | | 8 | "His olive skin showed rain" | | 9 | "His warm brown eyes found" | | 10 | "His Saint Christopher medallion swung" | | 11 | "His scar ran along his" | | 12 | "Quinn crossed the floor and" | | 13 | "She held her cuffs in" | | 14 | "She planted her boots beside" | | 15 | "Tomás stood and hauled the" | | 16 | "The bartender froze with a" | | 17 | "Quinn pointed at the stool" | | 18 | "Her brown eyes stayed on" | | 19 | "He knocked his glass across" |
| | ratio | 0.952 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |