| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1286 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "really" |
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| 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) | |
| 80.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1286 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "echoed" | | 1 | "silence" | | 2 | "flickered" | | 3 | "pulse" | | 4 | "anticipation" |
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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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1286 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1014 | | uniqueNames | 13 | | maxNameDensity | 1.58 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Brewer | 2 | | Street | 2 | | Quinn | 16 | | Northbridge | 1 | | Tube | 1 | | Soho | 1 | | London | 3 | | Underground | 1 | | Slowly | 1 | | Silence | 1 | | Morris | 1 | | Three | 4 | | Tommy | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Tommy" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Soho" | | 3 | "London" |
| | globalScore | 0.711 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a disused Tube entrance" |
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| 44.48% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.555 | | wordCount | 1286 | | matches | | 0 | "Not the target herself, but close enough to follow" | | 1 | "not toward Quinn, but toward the threshold of the door" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 26.79 | | std | 21.62 | | cv | 0.807 | | sampleLengths | | 0 | 9 | | 1 | 56 | | 2 | 37 | | 3 | 52 | | 4 | 42 | | 5 | 41 | | 6 | 34 | | 7 | 4 | | 8 | 76 | | 9 | 14 | | 10 | 63 | | 11 | 2 | | 12 | 40 | | 13 | 50 | | 14 | 10 | | 15 | 70 | | 16 | 25 | | 17 | 3 | | 18 | 4 | | 19 | 6 | | 20 | 70 | | 21 | 7 | | 22 | 40 | | 23 | 18 | | 24 | 31 | | 25 | 1 | | 26 | 40 | | 27 | 5 | | 28 | 14 | | 29 | 26 | | 30 | 1 | | 31 | 45 | | 32 | 52 | | 33 | 3 | | 34 | 33 | | 35 | 17 | | 36 | 25 | | 37 | 33 | | 38 | 5 | | 39 | 23 | | 40 | 61 | | 41 | 39 | | 42 | 6 | | 43 | 7 | | 44 | 34 | | 45 | 6 | | 46 | 3 | | 47 | 3 |
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| 92.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 133 | | matches | | 0 | "was plastered" | | 1 | "were tiled" | | 2 | "was rolled" | | 3 | "being told" | | 4 | "was overworked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 175 | | matches | | 0 | "was watching" | | 1 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 149 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1019 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.023552502453385672 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0068694798822374874 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 8.63 | | std | 7.49 | | cv | 0.867 | | sampleLengths | | 0 | 4 | | 1 | 5 | | 2 | 28 | | 3 | 7 | | 4 | 9 | | 5 | 12 | | 6 | 17 | | 7 | 7 | | 8 | 9 | | 9 | 4 | | 10 | 8 | | 11 | 13 | | 12 | 15 | | 13 | 4 | | 14 | 9 | | 15 | 2 | | 16 | 1 | | 17 | 9 | | 18 | 2 | | 19 | 9 | | 20 | 9 | | 21 | 5 | | 22 | 8 | | 23 | 6 | | 24 | 21 | | 25 | 5 | | 26 | 9 | | 27 | 2 | | 28 | 16 | | 29 | 5 | | 30 | 11 | | 31 | 4 | | 32 | 10 | | 33 | 20 | | 34 | 15 | | 35 | 7 | | 36 | 2 | | 37 | 2 | | 38 | 2 | | 39 | 5 | | 40 | 7 | | 41 | 5 | | 42 | 1 | | 43 | 9 | | 44 | 3 | | 45 | 2 | | 46 | 2 | | 47 | 7 | | 48 | 15 | | 49 | 3 |
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| 52.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.3959731543624161 | | totalSentences | 149 | | uniqueOpeners | 59 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | 0 | "Somewhere above, a cat yowled" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 104 | | matches | | 0 | "She'd clocked the woman three" | | 1 | "She wore a dark coat," | | 2 | "She'd glanced back twice." | | 3 | "Her leather watch was soaked" | | 4 | "She wiped it with her" | | 5 | "She took the steps." | | 6 | "She didn't draw." | | 7 | "She moved along the platform," | | 8 | "She let her eyes adjust." | | 9 | "It was closing." | | 10 | "Her fingers caught the edge." | | 11 | "She wedged her boot against" | | 12 | "She pulled the door open" | | 13 | "It was pity." | | 14 | "She spun, hand finally drawing" | | 15 | "He wore a worn leather" | | 16 | "His tone was calm" | | 17 | "He raised both hands, palms" | | 18 | "He nodded toward the steps" | | 19 | "He reached into his satchel" |
| | ratio | 0.279 | |
| 51.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 104 | | matches | | 0 | "The suspect was fast." | | 1 | "Quinn's boots struck wet cobblestone" | | 2 | "The crash echoed off the" | | 3 | "She'd clocked the woman three" | | 4 | "A known associate of the" | | 5 | "The woman ran like someone" | | 6 | "She wore a dark coat," | | 7 | "She'd glanced back twice." | | 8 | "Both times, Quinn saw the" | | 9 | "Quinn reached the end of" | | 10 | "Her leather watch was soaked" | | 11 | "She wiped it with her" | | 12 | "The woman had at least" | | 13 | "The street ahead split three" | | 14 | "A main road, a service" | | 15 | "The kind of place tourists" | | 16 | "The main road carried the" | | 17 | "The service lane was quiet." | | 18 | "The iron steps were silent" | | 19 | "She took the steps." |
| | ratio | 0.817 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 69.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 4 | | matches | | 0 | "Carrying a smell she couldn't place, like sandalwood and something older, something that made the back of her throat tighten." | | 1 | "He wore a worn leather satchel and a medallion around his neck that caught the amber light from below." | | 2 | "Three years of closed files and unhelpful coroners and witnesses who forgot what they'd seen overnight." | | 3 | "A single clear note that shouldn't have been audible through stone and distance, but was." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0 | | effectiveRatio | 0 | |