| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1363 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 63.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1363 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "traced" | | 2 | "standard" | | 3 | "silk" | | 4 | "magnetic" | | 5 | "furrowing" | | 6 | "chill" | | 7 | "synthetic" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1359 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.39% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 809 | | uniqueNames | 12 | | maxNameDensity | 2.47 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Miller" | | discoveredNames | | Quinn | 20 | | Camden | 1 | | High | 1 | | Street | 1 | | Northern | 1 | | Line | 1 | | Constable | 1 | | Miller | 15 | | Chen | 1 | | Blitz | 1 | | Victorian | 2 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Constable" | | 2 | "Miller" | | 3 | "Chen" | | 4 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Northern" |
| | globalScore | 0.264 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 52.83% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.472 | | wordCount | 1359 | | matches | | 0 | "Not cellar-cold, but freezing, the kind of dry chill" | | 1 | "not into red sand, but into fine, ivory-white powder" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 18.88 | | std | 14 | | cv | 0.742 | | sampleLengths | | 0 | 40 | | 1 | 45 | | 2 | 19 | | 3 | 18 | | 4 | 12 | | 5 | 25 | | 6 | 3 | | 7 | 13 | | 8 | 53 | | 9 | 19 | | 10 | 4 | | 11 | 16 | | 12 | 12 | | 13 | 5 | | 14 | 9 | | 15 | 3 | | 16 | 22 | | 17 | 30 | | 18 | 5 | | 19 | 4 | | 20 | 12 | | 21 | 17 | | 22 | 35 | | 23 | 10 | | 24 | 32 | | 25 | 15 | | 26 | 4 | | 27 | 2 | | 28 | 10 | | 29 | 41 | | 30 | 48 | | 31 | 9 | | 32 | 7 | | 33 | 37 | | 34 | 46 | | 35 | 5 | | 36 | 2 | | 37 | 9 | | 38 | 37 | | 39 | 18 | | 40 | 17 | | 41 | 9 | | 42 | 39 | | 43 | 7 | | 44 | 5 | | 45 | 38 | | 46 | 6 | | 47 | 22 | | 48 | 8 | | 49 | 44 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 124 | | matches | (empty) | |
| 45.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 117 | | ratio | 0.034 | | matches | | 0 | "Down here, the city hummed through bedrock—a low, bowel-shaking vibration from the Northern Line three hundred yards east." | | 1 | "The chalk outline traced an awkward shape—knees pulled to chest, arms splayed outward like broken oars." | | 2 | "Not scrubbed clean—bone dry." | | 3 | "Etchings crisscrossed the perimeter—angular runes cut into the metal with deliberate precision, sharp and unhurried." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 820 | | adjectiveStacks | 3 | | stackExamples | | 0 | "harsh lime-white glare" | | 1 | "rigid, locked dead center" | | 2 | "fine, ivory-white powder." |
| | adverbCount | 12 | | adverbRatio | 0.014634146341463415 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003658536585365854 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 11.62 | | std | 8.76 | | cv | 0.755 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 7 | | 3 | 4 | | 4 | 12 | | 5 | 22 | | 6 | 14 | | 7 | 5 | | 8 | 18 | | 9 | 12 | | 10 | 10 | | 11 | 15 | | 12 | 3 | | 13 | 13 | | 14 | 53 | | 15 | 6 | | 16 | 6 | | 17 | 7 | | 18 | 4 | | 19 | 16 | | 20 | 12 | | 21 | 5 | | 22 | 9 | | 23 | 3 | | 24 | 22 | | 25 | 8 | | 26 | 15 | | 27 | 7 | | 28 | 5 | | 29 | 4 | | 30 | 12 | | 31 | 17 | | 32 | 6 | | 33 | 13 | | 34 | 16 | | 35 | 10 | | 36 | 12 | | 37 | 20 | | 38 | 5 | | 39 | 6 | | 40 | 4 | | 41 | 4 | | 42 | 2 | | 43 | 10 | | 44 | 41 | | 45 | 19 | | 46 | 7 | | 47 | 22 | | 48 | 6 | | 49 | 3 |
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| 66.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.42735042735042733 | | totalSentences | 117 | | uniqueOpeners | 50 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Directly into the brick wall" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 74 | | matches | | 0 | "She tucked her chin into" | | 1 | "Her brown eyes stopped on" | | 2 | "Her knees made a faint" | | 3 | "She ignored Miller, focusing on" | | 4 | "She stepped past the tape" | | 5 | "It sat in a dead-flat" | | 6 | "Her gaze tracked along the" | | 7 | "She strode toward the evidence" | | 8 | "It gleamed dull grey, thicker" | | 9 | "It did not point north" | | 10 | "It did not oscillate with" | | 11 | "It pointed dead ahead." | | 12 | "He peered through the plastic," | | 13 | "He flattened the screen against" | | 14 | "She ran her bare fingers" | | 15 | "It was cold." | | 16 | "She pressed her thumb into" |
| | ratio | 0.23 | |
| 41.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 74 | | matches | | 0 | "The iron spiral staircase rattled" | | 1 | "Harlow Quinn checked her worn" | | 2 | "The strap pinched her wrist," | | 3 | "She tucked her chin into" | | 4 | "Detective Constable Miller leaned against" | | 5 | "Quinn took in the tunnel" | | 6 | "Her brown eyes stopped on" | | 7 | "Miller swallowed his coffee and" | | 8 | "Quinn crouched at the platform" | | 9 | "Her knees made a faint" | | 10 | "She ignored Miller, focusing on" | | 11 | "Quinn ran an index finger" | | 12 | "Miller sighed, stepping closer with" | | 13 | "Quinn pointed her torch beam" | | 14 | "Miller shifted his weight." | | 15 | "Quinn stood up, her jaw" | | 16 | "She stepped past the tape" | | 17 | "The chalk outline traced an" | | 18 | "Miller countered, gesturing with his" | | 19 | "Quinn knelt by the wall." |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, her voice dropping into a hard, clipped register" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Quinn stated (state)" |
| | dialogueSentences | 40 | | tagDensity | 0.075 | | leniency | 0.15 | | rawRatio | 0.667 | | effectiveRatio | 0.1 | |