| 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 | 2189 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 88.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2189 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanned" | | 1 | "silence" | | 2 | "coded" | | 3 | "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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 159 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2189 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 917 | | uniqueNames | 15 | | maxNameDensity | 2.07 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Aurora | 1 | | Carter | 1 | | Golden | 2 | | Empress | 2 | | Blackwood | 1 | | Welsh | 1 | | Cardiff | 1 | | London-clipped | 1 | | Prague | 1 | | Berlin | 1 | | Rory | 19 | | Eva | 12 | | Silas | 7 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Carter" | | 4 | "Blackwood" | | 5 | "Rory" | | 6 | "Eva" | | 7 | "Silas" |
| | places | | 0 | "Cardiff" | | 1 | "London-clipped" | | 2 | "Prague" | | 3 | "Berlin" |
| | globalScore | 0.464 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 2189 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 159 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 117 | | mean | 18.71 | | std | 15.65 | | cv | 0.837 | | sampleLengths | | 0 | 63 | | 1 | 44 | | 2 | 62 | | 3 | 6 | | 4 | 6 | | 5 | 7 | | 6 | 16 | | 7 | 40 | | 8 | 53 | | 9 | 16 | | 10 | 7 | | 11 | 18 | | 12 | 27 | | 13 | 8 | | 14 | 1 | | 15 | 27 | | 16 | 2 | | 17 | 41 | | 18 | 59 | | 19 | 3 | | 20 | 14 | | 21 | 1 | | 22 | 9 | | 23 | 23 | | 24 | 5 | | 25 | 26 | | 26 | 4 | | 27 | 9 | | 28 | 28 | | 29 | 28 | | 30 | 12 | | 31 | 10 | | 32 | 11 | | 33 | 14 | | 34 | 1 | | 35 | 16 | | 36 | 5 | | 37 | 11 | | 38 | 10 | | 39 | 16 | | 40 | 50 | | 41 | 3 | | 42 | 3 | | 43 | 16 | | 44 | 12 | | 45 | 16 | | 46 | 2 | | 47 | 39 | | 48 | 20 | | 49 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 144 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 159 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 923 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.013001083423618635 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 159 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 159 | | mean | 13.77 | | std | 11.01 | | cv | 0.8 | | sampleLengths | | 0 | 30 | | 1 | 33 | | 2 | 21 | | 3 | 15 | | 4 | 8 | | 5 | 13 | | 6 | 18 | | 7 | 31 | | 8 | 6 | | 9 | 6 | | 10 | 7 | | 11 | 16 | | 12 | 9 | | 13 | 12 | | 14 | 8 | | 15 | 1 | | 16 | 1 | | 17 | 9 | | 18 | 14 | | 19 | 39 | | 20 | 16 | | 21 | 7 | | 22 | 18 | | 23 | 10 | | 24 | 17 | | 25 | 8 | | 26 | 1 | | 27 | 3 | | 28 | 7 | | 29 | 2 | | 30 | 2 | | 31 | 13 | | 32 | 2 | | 33 | 26 | | 34 | 15 | | 35 | 4 | | 36 | 18 | | 37 | 10 | | 38 | 12 | | 39 | 15 | | 40 | 3 | | 41 | 14 | | 42 | 1 | | 43 | 9 | | 44 | 23 | | 45 | 5 | | 46 | 26 | | 47 | 4 | | 48 | 9 | | 49 | 28 |
| |
| 43.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.2830188679245283 | | totalSentences | 159 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 67 | | matches | | 0 | "Her straight black hair stuck" | | 1 | "His grey-streaked auburn beard had" | | 2 | "She shook water from a" | | 3 | "Her face did the work" | | 4 | "Her hand closed around the" | | 5 | "He gave them the whole" | | 6 | "She had lost the nose" | | 7 | "Her suit had cost money" | | 8 | "She stopped a foot from" | | 9 | "She caught the movement and" | | 10 | "It filled the pause and" | | 11 | "She covered it with her" | | 12 | "She stopped it with a" | | 13 | "She set it face-down and" | | 14 | "She offered the packet across." | | 15 | "Her phone lit face-down against" |
| | ratio | 0.239 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 67 | | matches | | 0 | "The green neon above The" | | 1 | "Rory came down the stairs" | | 2 | "Her straight black hair stuck" | | 3 | "A receipt had welded itself" | | 4 | "Silas Blackwood stood behind the" | | 5 | "The silver signet ring on" | | 6 | "His grey-streaked auburn beard had" | | 7 | "Silas poured water and pushed" | | 8 | "Rory drank and watched the" | | 9 | "The mirror gave the room" | | 10 | "The maps with their borders" | | 11 | "The door opened and let" | | 12 | "She shook water from a" | | 13 | "Rory set the glass down" | | 14 | "The woman ordered at the" | | 15 | "The Welsh in her voice" | | 16 | "Each word landed flat, London-clipped," | | 17 | "Rory spoke before sense got" | | 18 | "The woman turned." | | 19 | "Her face did the work" |
| | ratio | 0.985 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 61.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 5 | | matches | | 0 | "The green neon above The Raven's Nest threw a sick light across the wet pavement outside, and rain needled the windows in gusts that rattled the glass in its fr…" | | 1 | "Inside, old maps papered the walls between black-and-white photographs of men in coats on foreign streets, and the air held polish, beer, and the ginger heat th…" | | 2 | "She shook water from a cropped head of hair that had once hung past her waist in red ropes, closed a black umbrella with a snap, and scanned the room with the b…" | | 3 | "Each word landed flat, London-clipped, except home, which came out with a bend she caught too late." | | 4 | "Eva took a breath that moved her suit at the ribs." |
| |
| 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 | |