| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.275 | | leniency | 0.549 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1785 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 38.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1785 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "sanctuary" | | 1 | "rhythmic" | | 2 | "mechanical" | | 3 | "tracing" | | 4 | "chaotic" | | 5 | "scanned" | | 6 | "stark" | | 7 | "calculated" | | 8 | "measured" | | 9 | "weight" | | 10 | "furrowing" | | 11 | "silence" | | 12 | "sentinel" | | 13 | "shimmered" | | 14 | "flickered" | | 15 | "gloom" | | 16 | "race" | | 17 | "whisper" | | 18 | "echoing" |
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| 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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1778 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1236 | | uniqueNames | 13 | | maxNameDensity | 1.29 | | worstName | "Evan" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Blackwood | 1 | | Golden | 2 | | Empress | 2 | | Szechuan | 1 | | Rory | 11 | | Silas | 15 | | Aurora | 2 | | Cardiff | 1 | | Evan | 16 | | Spymaster | 1 | | London | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Blackwood" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Aurora" | | 6 | "Evan" | | 7 | "Spymaster" |
| | places | | 0 | "Golden" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.853 | | windowScore | 0.167 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "quite a smile" | | 1 | "quite know what to do with his hands when they weren't holding a smartphone" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1778 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 26.54 | | std | 18.95 | | cv | 0.714 | | sampleLengths | | 0 | 78 | | 1 | 41 | | 2 | 55 | | 3 | 38 | | 4 | 1 | | 5 | 9 | | 6 | 48 | | 7 | 1 | | 8 | 38 | | 9 | 8 | | 10 | 40 | | 11 | 8 | | 12 | 8 | | 13 | 50 | | 14 | 36 | | 15 | 30 | | 16 | 13 | | 17 | 7 | | 18 | 32 | | 19 | 31 | | 20 | 10 | | 21 | 24 | | 22 | 8 | | 23 | 19 | | 24 | 36 | | 25 | 18 | | 26 | 21 | | 27 | 5 | | 28 | 16 | | 29 | 66 | | 30 | 27 | | 31 | 32 | | 32 | 31 | | 33 | 26 | | 34 | 22 | | 35 | 44 | | 36 | 16 | | 37 | 9 | | 38 | 18 | | 39 | 44 | | 40 | 15 | | 41 | 8 | | 42 | 41 | | 43 | 14 | | 44 | 20 | | 45 | 16 | | 46 | 26 | | 47 | 55 | | 48 | 29 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 211 | | matches | | |
| 99.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 132 | | ratio | 0.015 | | matches | | 0 | "He scanned the room with a practiced, predatory caution—a habit Rory remembered from a lifetime ago, though the context had shifted from avoiding school bullies to whatever corporate battlefield he now inhabited." | | 1 | "He opened his mouth to say something—perhaps an apology, perhaps a plea—but the iron set of her shoulders stopped him." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1243 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 36 | | adverbRatio | 0.028962188254223652 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.012067578439259855 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 13.47 | | std | 7.94 | | cv | 0.59 | | sampleLengths | | 0 | 24 | | 1 | 10 | | 2 | 31 | | 3 | 13 | | 4 | 16 | | 5 | 25 | | 6 | 3 | | 7 | 14 | | 8 | 6 | | 9 | 13 | | 10 | 19 | | 11 | 6 | | 12 | 32 | | 13 | 1 | | 14 | 9 | | 15 | 3 | | 16 | 14 | | 17 | 11 | | 18 | 20 | | 19 | 1 | | 20 | 17 | | 21 | 10 | | 22 | 11 | | 23 | 8 | | 24 | 5 | | 25 | 6 | | 26 | 18 | | 27 | 11 | | 28 | 8 | | 29 | 8 | | 30 | 14 | | 31 | 36 | | 32 | 11 | | 33 | 25 | | 34 | 30 | | 35 | 13 | | 36 | 7 | | 37 | 4 | | 38 | 12 | | 39 | 5 | | 40 | 11 | | 41 | 8 | | 42 | 13 | | 43 | 10 | | 44 | 7 | | 45 | 3 | | 46 | 17 | | 47 | 7 | | 48 | 8 | | 49 | 13 |
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| 38.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2727272727272727 | | totalSentences | 132 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 35.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 91 | | matches | | 0 | "His silver signet ring caught" | | 1 | "She had just finished a" | | 2 | "He wore a charcoal wool" | | 3 | "His hair, once a chaotic" | | 4 | "He didn't see her at" | | 5 | "He scanned the room with" | | 6 | "He turned toward the sound," | | 7 | "His eyes, once wide with" | | 8 | "He used her middle name," | | 9 | "He walked toward her, his" | | 10 | "He stopped a foot away." | | 11 | "He smelled of sandalwood and" | | 12 | "He looked at her shoulder-length" | | 13 | "His mouth thinned into a" | | 14 | "His hazel eyes darted toward" | | 15 | "She signaled to Silas." | | 16 | "He slid a pint of" | | 17 | "He reached out as if" | | 18 | "He let his hand drop," | | 19 | "He looked at her again," |
| | ratio | 0.462 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 91 | | matches | | 0 | "The green neon of the" | | 1 | "Silas Blackwood stood behind the" | | 2 | "His silver signet ring caught" | | 3 | "Aurora leaned against the edge" | | 4 | "She had just finished a" | | 5 | "The door creaked." | | 6 | "A draft cut through the" | | 7 | "A man stepped into the" | | 8 | "He wore a charcoal wool" | | 9 | "His hair, once a chaotic" | | 10 | "He didn't see her at" | | 11 | "He scanned the room with" | | 12 | "The name slipped out before" | | 13 | "The man froze." | | 14 | "He turned toward the sound," | | 15 | "His eyes, once wide with" | | 16 | "He used her middle name," | | 17 | "He walked toward her, his" | | 18 | "Every step looked calculated, measured" | | 19 | "He stopped a foot away." |
| | ratio | 0.956 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "The green neon of the Raven's Nest sign hummed, a low-frequency vibration that rattled the marrow of anyone standing too close to the brickwork." | | 1 | "Silas Blackwood stood behind the mahogany sanctuary of his bar, polishing a highball glass with the rhythmic, mechanical precision of a man who had spent decade…" | | 2 | "He wore a charcoal wool coat that cost more than Rory’s monthly rent." | | 3 | "He looked at her again, really looked at her this time, searching for the girl who used to steal his law textbooks and hide them in the rafters of the universit…" | | 4 | "Outside, the city hummed, indifferent to the two people who had once known everything about each other and now knew nothing at all." | | 5 | "She climbed onto the motorbike, the engine turning over with a guttural roar that drowned out the hum of the neon sign." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice dropping an octave" | | 1 | "she said, her voice barely a whisper" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "he whispered (whisper)" | | 1 | "Silas murmured (murmur)" | | 2 | "Silas warned (warn)" |
| | dialogueSentences | 51 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.25 | | effectiveRatio | 0.118 | |