| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.094 | | leniency | 0.188 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1731 | | totalAiIsmAdverbs | 1 | | 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) | |
| 53.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1731 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "intensity" | | 1 | "crystal" | | 2 | "flicked" | | 3 | "tracing" | | 4 | "scanned" | | 5 | "silence" | | 6 | "cataloged" | | 7 | "weight" | | 8 | "comfortable" | | 9 | "reminder" | | 10 | "glinting" | | 11 | "etched" | | 12 | "warmth" | | 13 | "flickered" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 87 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1725 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1144 | | uniqueNames | 10 | | maxNameDensity | 1.14 | | worstName | "Julian" | | maxWindowNameDensity | 3 | | worstWindowName | "Julian" | | discoveredNames | | Dean | 1 | | Street | 1 | | Raven | 2 | | Nest | 2 | | Cardiff | 2 | | Silas | 12 | | Julian | 13 | | Prague | 2 | | Aurora | 8 | | London | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Julian" | | 4 | "Aurora" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Cardiff" | | 3 | "Prague" | | 4 | "London" |
| | globalScore | 0.932 | | windowScore | 0.667 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 3 | | matches | | 0 | "quite shake despite the limp that now defined his stride" | | 1 | "sounded like a ghost's inhalation" | | 2 | "quite hide the way his eyes cataloged Julian’s frayed cuffs and the nervous tic in his jaw" |
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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 | 1725 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 116 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 27.38 | | std | 20.22 | | cv | 0.738 | | sampleLengths | | 0 | 79 | | 1 | 41 | | 2 | 49 | | 3 | 1 | | 4 | 42 | | 5 | 1 | | 6 | 46 | | 7 | 17 | | 8 | 18 | | 9 | 11 | | 10 | 11 | | 11 | 8 | | 12 | 25 | | 13 | 9 | | 14 | 49 | | 15 | 30 | | 16 | 24 | | 17 | 46 | | 18 | 17 | | 19 | 50 | | 20 | 8 | | 21 | 25 | | 22 | 35 | | 23 | 13 | | 24 | 43 | | 25 | 12 | | 26 | 8 | | 27 | 23 | | 28 | 5 | | 29 | 42 | | 30 | 24 | | 31 | 56 | | 32 | 42 | | 33 | 7 | | 34 | 10 | | 35 | 37 | | 36 | 8 | | 37 | 41 | | 38 | 24 | | 39 | 26 | | 40 | 33 | | 41 | 9 | | 42 | 11 | | 43 | 50 | | 44 | 16 | | 45 | 3 | | 46 | 11 | | 47 | 7 | | 48 | 42 | | 49 | 45 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 60.38% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 191 | | matches | | 0 | "was trying" | | 1 | "was thinning" | | 2 | "was walking" | | 3 | "wasn't leaving" |
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| 93.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 116 | | ratio | 0.017 | | matches | | 0 | "Silas moved past them to adjust a framed photograph on the wall—a black and white shot of a bridge in Prague." | | 1 | "The maps on the wall didn't show the way home; they only showed where people had been before they got lost." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.024369016536118365 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006092254134029591 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 14.87 | | std | 10.67 | | cv | 0.717 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 20 | | 3 | 30 | | 4 | 19 | | 5 | 22 | | 6 | 3 | | 7 | 13 | | 8 | 15 | | 9 | 2 | | 10 | 16 | | 11 | 1 | | 12 | 7 | | 13 | 5 | | 14 | 12 | | 15 | 18 | | 16 | 1 | | 17 | 7 | | 18 | 24 | | 19 | 15 | | 20 | 17 | | 21 | 18 | | 22 | 11 | | 23 | 11 | | 24 | 8 | | 25 | 4 | | 26 | 3 | | 27 | 18 | | 28 | 9 | | 29 | 33 | | 30 | 16 | | 31 | 30 | | 32 | 7 | | 33 | 5 | | 34 | 12 | | 35 | 46 | | 36 | 9 | | 37 | 8 | | 38 | 50 | | 39 | 8 | | 40 | 8 | | 41 | 17 | | 42 | 35 | | 43 | 13 | | 44 | 43 | | 45 | 12 | | 46 | 8 | | 47 | 9 | | 48 | 14 | | 49 | 5 |
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| 40.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.23275862068965517 | | totalSentences | 116 | | uniqueOpeners | 27 | |
| 39.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 84 | | matches | | 0 | "Then he thought about the" |
| | ratio | 0.012 | |
| 43.81% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 84 | | matches | | 0 | "His hazel eyes flicked toward" | | 1 | "She stared into a pint" | | 2 | "He scanned the room, his" | | 3 | "She gripped her glass until" | | 4 | "She turned her head slowly," | | 5 | "He looked leaner than the" | | 6 | "He reached for a stool" | | 7 | "He moved with the deliberate," | | 8 | "He slid the glass across" | | 9 | "She looked at Julian’s hands." | | 10 | "They were stained with ink" | | 11 | "He winced as the alcohol" | | 12 | "She turned her stool to" | | 13 | "He leaned in, the smell" | | 14 | "It was a place for" | | 15 | "It stayed flat, precise, and" | | 16 | "She stood up." | | 17 | "She reached for her jacket," | | 18 | "It was a desperate, clumsy" | | 19 | "He didn't say a word," |
| | ratio | 0.44 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 84 | | matches | | 0 | "Rain smeared the neon green" | | 1 | "Silas polished a highball glass" | | 2 | "His hazel eyes flicked toward" | | 3 | "Aurora sat at the corner" | | 4 | "She stared into a pint" | | 5 | "The door creaked." | | 6 | "A man stepped in, shedding" | | 7 | "He scanned the room, his" | | 8 | "The dampness from his coat" | | 9 | "The name sounded like a" | | 10 | "Aurora didn't move at first." | | 11 | "She gripped her glass until" | | 12 | "She turned her head slowly," | | 13 | "The man took three careful" | | 14 | "He looked leaner than the" | | 15 | "He reached for a stool" | | 16 | "Julian gestured to the maps," | | 17 | "Aurora took a sip of" | | 18 | "Julian flagged Silas down." | | 19 | "Silas didn't rush." |
| | ratio | 0.964 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 72.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas poured the liquid without a word, his expression a mask of professional indifference that didn't quite hide the way his eyes cataloged Julian’s frayed cuf…" | | 1 | "She reached for her jacket, a utility coat that bore the faint smell of sesame oil and exhaust." | | 2 | "Silas was across the bar in an instant, his hand resting on the wood, the silver ring glinting under the green neon glare." | | 3 | "It only cared about the next delivery, the next rainstorm, and the people who were strong enough to move through the dark without a map." | | 4 | "He pushed through the door, the bell tolling his departure, leaving only the scent of wet wool and the muffled sound of a distant siren to fill the gap." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Julian whispered (whisper)" |
| | dialogueSentences | 32 | | tagDensity | 0.094 | | leniency | 0.188 | | rawRatio | 0.333 | | effectiveRatio | 0.063 | |