| 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 | 1167 | | 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) | |
| 10.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1167 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "carried the weight" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "gloom" | | 1 | "flickered" | | 2 | "rhythmic" | | 3 | "footsteps" | | 4 | "fractured" | | 5 | "carried the weight" | | 6 | "echoed" | | 7 | "weight" | | 8 | "scanned" | | 9 | "blown wide" | | 10 | "etched" | | 11 | "structure" | | 12 | "scanning" | | 13 | "crystalline" | | 14 | "glint" | | 15 | "standard" |
| |
| 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 | 248 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 248 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 248 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 16 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1167 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1167 | | uniqueNames | 20 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | London | 1 | | Raven | 1 | | Nest | 1 | | Camden | 2 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Morris | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Detective | 1 | | Tomás | 8 | | Victorian | 2 | | Water | 3 | | Quinn | 17 | | Eighteen | 4 | | Boots | 4 | | You | 12 | | Eyes | 3 |
| | persons | | 0 | "Raven" | | 1 | "Camden" | | 2 | "Tube" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" | | 9 | "Water" | | 10 | "Quinn" | | 11 | "Boots" | | 12 | "You" | | 13 | "Eyes" |
| | places | | | globalScore | 0.772 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | 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 | 1167 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 248 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 48.63 | | std | 49.76 | | cv | 1.023 | | sampleLengths | | 0 | 142 | | 1 | 115 | | 2 | 141 | | 3 | 92 | | 4 | 4 | | 5 | 12 | | 6 | 13 | | 7 | 11 | | 8 | 24 | | 9 | 13 | | 10 | 32 | | 11 | 69 | | 12 | 4 | | 13 | 24 | | 14 | 57 | | 15 | 3 | | 16 | 94 | | 17 | 15 | | 18 | 79 | | 19 | 4 | | 20 | 14 | | 21 | 7 | | 22 | 34 | | 23 | 164 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 248 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 248 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1177 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.016142735768903994 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005097706032285472 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 248 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 248 | | mean | 4.71 | | std | 2.46 | | cv | 0.524 | | sampleLengths | | 0 | 5 | | 1 | 11 | | 2 | 5 | | 3 | 8 | | 4 | 9 | | 5 | 12 | | 6 | 4 | | 7 | 7 | | 8 | 10 | | 9 | 6 | | 10 | 2 | | 11 | 4 | | 12 | 4 | | 13 | 4 | | 14 | 8 | | 15 | 6 | | 16 | 3 | | 17 | 4 | | 18 | 5 | | 19 | 3 | | 20 | 3 | | 21 | 9 | | 22 | 6 | | 23 | 4 | | 24 | 9 | | 25 | 3 | | 26 | 9 | | 27 | 4 | | 28 | 5 | | 29 | 7 | | 30 | 12 | | 31 | 8 | | 32 | 6 | | 33 | 5 | | 34 | 8 | | 35 | 2 | | 36 | 5 | | 37 | 4 | | 38 | 2 | | 39 | 6 | | 40 | 4 | | 41 | 6 | | 42 | 8 | | 43 | 2 | | 44 | 7 | | 45 | 6 | | 46 | 8 | | 47 | 5 | | 48 | 8 | | 49 | 5 |
| |
| 65.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.4274193548387097 | | totalSentences | 248 | | uniqueOpeners | 106 | |
| 16.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 200 | | matches | | 0 | "Only the bone token in" |
| | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 200 | | matches | | 0 | "She cut through the downpour" | | 1 | "She knew how to track" | | 2 | "She followed the rhythmic patter" | | 3 | "She pressed forward." | | 4 | "Her closely cropped salt-and-pepper hair" | | 5 | "She didn't break pace." | | 6 | "She moved toward the rear." | | 7 | "She stepped into the concealed" | | 8 | "She kept her weight forward." | | 9 | "She recognized the layout from" | | 10 | "Her partner, Morris, vanished three" | | 11 | "She pressed on." | | 12 | "He stood just beyond the" | | 13 | "You are trespassing, Detective." | | 14 | "She kept her aim steady." | | 15 | "You think this is your" | | 16 | "I will drag you back" | | 17 | "Your colleague learned the hard" | | 18 | "You want passage." | | 19 | "You pay the toll." |
| | ratio | 0.24 | |
| 67.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 157 | | totalSentences | 200 | | matches | | 0 | "Rain needled the London pavement." | | 1 | "Water sheeted off brick facades" | | 2 | "Quinn boots slapped the asphalt." | | 3 | "She cut through the downpour" | | 4 | "She knew how to track" | | 5 | "The suspect ran ahead." | | 6 | "A figure swallowed by the" | | 7 | "Quinn adjusted the worn leather" | | 8 | "The face glowed in the" | | 9 | "Streetlights flickered and died." | | 10 | "She followed the rhythmic patter" | | 11 | "The suspect vanished around a" | | 12 | "Quinn rounded it." | | 13 | "Brick walls closed in." | | 14 | "Puddles reflected the fractured sky." | | 15 | "She pressed forward." | | 16 | "Boots displaced water." | | 17 | "Her closely cropped salt-and-pepper hair" | | 18 | "Water ran down her sharp" | | 19 | "She didn't break pace." |
| | ratio | 0.785 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 200 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 13 | | 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 | |