| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 79 | | tagDensity | 0.228 | | leniency | 0.456 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1746 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "very" | | 2 | "sharply" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 94.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1746 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 144 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 205 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1741 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1281 | | uniqueNames | 5 | | maxNameDensity | 2.19 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 28 | | Patel | 12 | | Bell | 10 | | Kowalski | 1 | | Eva | 15 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Bell" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.407 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like them, had been scratched arou" |
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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 | 1741 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 205 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 110 | | mean | 15.83 | | std | 15.05 | | cv | 0.951 | | sampleLengths | | 0 | 18 | | 1 | 41 | | 2 | 4 | | 3 | 10 | | 4 | 45 | | 5 | 27 | | 6 | 36 | | 7 | 17 | | 8 | 3 | | 9 | 29 | | 10 | 1 | | 11 | 4 | | 12 | 56 | | 13 | 34 | | 14 | 12 | | 15 | 4 | | 16 | 4 | | 17 | 44 | | 18 | 5 | | 19 | 3 | | 20 | 25 | | 21 | 11 | | 22 | 27 | | 23 | 5 | | 24 | 56 | | 25 | 5 | | 26 | 12 | | 27 | 18 | | 28 | 2 | | 29 | 12 | | 30 | 1 | | 31 | 11 | | 32 | 8 | | 33 | 8 | | 34 | 21 | | 35 | 11 | | 36 | 13 | | 37 | 7 | | 38 | 5 | | 39 | 18 | | 40 | 16 | | 41 | 4 | | 42 | 8 | | 43 | 1 | | 44 | 4 | | 45 | 5 | | 46 | 5 | | 47 | 1 | | 48 | 13 | | 49 | 8 |
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| 88.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 144 | | matches | | 0 | "been scratched" | | 1 | "been pressed" | | 2 | "was caught" | | 3 | "been brought" | | 4 | "were edged" | | 5 | "was stained" | | 6 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 215 | | matches | | |
| 59.23% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 205 | | ratio | 0.029 | | matches | | 0 | "Not polished—clean in the particular way shoes looked after a walk on dry pavement." | | 1 | "Green corrosion marked its casing; protective symbols, or what looked like them, had been scratched around its face." | | 2 | "No dust from the platform on the satchel’s bottom, either; she had kept it on her shoulder or held it off the floor." | | 3 | "He had knelt somewhere wet and gritty, then walked—or been brought—through the door." | | 4 | "Behind her, Eva made a sound—no more than a breath drawn too sharply." | | 5 | "Light showed through—amber light, shifting with the movement of people on the other side." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1288 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.02872670807453416 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004658385093167702 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 205 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 205 | | mean | 8.49 | | std | 5.73 | | cv | 0.675 | | sampleLengths | | 0 | 11 | | 1 | 7 | | 2 | 19 | | 3 | 8 | | 4 | 14 | | 5 | 4 | | 6 | 5 | | 7 | 5 | | 8 | 19 | | 9 | 13 | | 10 | 13 | | 11 | 16 | | 12 | 4 | | 13 | 7 | | 14 | 4 | | 15 | 32 | | 16 | 3 | | 17 | 14 | | 18 | 3 | | 19 | 29 | | 20 | 1 | | 21 | 4 | | 22 | 4 | | 23 | 16 | | 24 | 18 | | 25 | 4 | | 26 | 14 | | 27 | 2 | | 28 | 8 | | 29 | 12 | | 30 | 12 | | 31 | 12 | | 32 | 4 | | 33 | 4 | | 34 | 3 | | 35 | 9 | | 36 | 18 | | 37 | 14 | | 38 | 5 | | 39 | 3 | | 40 | 21 | | 41 | 4 | | 42 | 11 | | 43 | 8 | | 44 | 2 | | 45 | 6 | | 46 | 11 | | 47 | 5 | | 48 | 9 | | 49 | 19 |
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| 62.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3902439024390244 | | totalSentences | 205 | | uniqueOpeners | 80 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 124 | | matches | | 0 | "Then Patel returned." | | 1 | "More yellow grit was caught" | | 2 | "Only a wall, his dropped" | | 3 | "Then a voice called something" | | 4 | "Then the door slammed against" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 124 | | matches | | 0 | "His coat was open." | | 1 | "His shoes were clean." | | 2 | "His fingers had curled around" | | 3 | "She wanted the platform as" | | 4 | "They had put her in" | | 5 | "She had round glasses, curly" | | 6 | "She rose when Quinn entered." | | 7 | "Her freckles stood out against" | | 8 | "He stepped into the corridor" | | 9 | "Her mouth tightened." | | 10 | "Its concrete was grey and" | | 11 | "She walked toward the tiled" | | 12 | "Its paper was brittle, but" | | 13 | "He came over." | | 14 | "He looked from the clean" | | 15 | "She remembered Bell’s clean shoes" | | 16 | "He had knelt somewhere wet" | | 17 | "She must have asked to" | | 18 | "She stared at the door" | | 19 | "Their soles were edged with" |
| | ratio | 0.242 | |
| 72.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 124 | | matches | | 0 | "The last train through the" | | 1 | "Someone had swept the platform" | | 2 | "Harlow Quinn stopped at the" | | 3 | "The air smelled of wet" | | 4 | "DS Patel followed her gaze." | | 5 | "The clean strip ran from" | | 6 | "Dust lay thick everywhere else," | | 7 | "A man lay on his" | | 8 | "His coat was open." | | 9 | "Blood had dried in his" | | 10 | "Quinn looked up." | | 11 | "The gantry crossed the tracks" | | 12 | "That covered very little." | | 13 | "Quinn stepped around the body," | | 14 | "Bell wore an expensive wool" | | 15 | "His shoes were clean." | | 16 | "Patel’s interpretation had the virtue" | | 17 | "Bell had climbed the gantry," | | 18 | "An abandoned station offered privacy" | | 19 | "Quinn leaned closer." |
| | ratio | 0.774 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 124 | | matches | | 0 | "Whoever had swept had done" | | 1 | "By the time she reached" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 2 | | matches | | 0 | "Only a wall, his dropped radio, and a line of wet footprints that ended against the tiles." | | 1 | "She could hear footsteps beyond it, more than one set, moving away across a floor that sounded tiled." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 79 | | tagDensity | 0.19 | | leniency | 0.38 | | rawRatio | 0.067 | | effectiveRatio | 0.025 | |