| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1227 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 38.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1227 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "calculated" | | 1 | "scanned" | | 2 | "shimmered" | | 3 | "silence" | | 4 | "flickered" | | 5 | "glinting" | | 6 | "gloom" | | 7 | "echoed" | | 8 | "echoing" | | 9 | "velvet" | | 10 | "pulsed" | | 11 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 161 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 161 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1227 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1227 | | uniqueNames | 11 | | maxNameDensity | 1.39 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Quinn | 2 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 | | Detective | 2 | | Christopher | 1 | | Harlow | 17 | | You | 6 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Morris" | | 3 | "Detective" | | 4 | "Christopher" | | 5 | "Harlow" | | 6 | "You" |
| | places | | | globalScore | 0.807 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1227 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 161 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 21.53 | | std | 19.4 | | cv | 0.901 | | sampleLengths | | 0 | 73 | | 1 | 68 | | 2 | 1 | | 3 | 55 | | 4 | 5 | | 5 | 48 | | 6 | 10 | | 7 | 61 | | 8 | 10 | | 9 | 52 | | 10 | 56 | | 11 | 23 | | 12 | 6 | | 13 | 19 | | 14 | 7 | | 15 | 3 | | 16 | 37 | | 17 | 6 | | 18 | 16 | | 19 | 6 | | 20 | 7 | | 21 | 13 | | 22 | 11 | | 23 | 30 | | 24 | 4 | | 25 | 23 | | 26 | 16 | | 27 | 61 | | 28 | 4 | | 29 | 24 | | 30 | 8 | | 31 | 50 | | 32 | 46 | | 33 | 18 | | 34 | 7 | | 35 | 26 | | 36 | 2 | | 37 | 20 | | 38 | 28 | | 39 | 13 | | 40 | 2 | | 41 | 23 | | 42 | 4 | | 43 | 9 | | 44 | 8 | | 45 | 25 | | 46 | 10 | | 47 | 10 | | 48 | 5 | | 49 | 23 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 161 | | matches | | 0 | "been disturbed" | | 1 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 224 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 161 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1228 | | adjectiveStacks | 2 | | stackExamples | | 0 | "heavy, pressing against her" | | 1 | "small, leather-bound book." |
| | adverbCount | 25 | | adverbRatio | 0.02035830618892508 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003257328990228013 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 7.62 | | std | 4.42 | | cv | 0.58 | | sampleLengths | | 0 | 16 | | 1 | 18 | | 2 | 12 | | 3 | 13 | | 4 | 14 | | 5 | 11 | | 6 | 14 | | 7 | 4 | | 8 | 16 | | 9 | 5 | | 10 | 14 | | 11 | 4 | | 12 | 1 | | 13 | 9 | | 14 | 6 | | 15 | 18 | | 16 | 12 | | 17 | 10 | | 18 | 5 | | 19 | 3 | | 20 | 14 | | 21 | 9 | | 22 | 13 | | 23 | 9 | | 24 | 4 | | 25 | 6 | | 26 | 5 | | 27 | 15 | | 28 | 17 | | 29 | 4 | | 30 | 20 | | 31 | 10 | | 32 | 4 | | 33 | 11 | | 34 | 4 | | 35 | 3 | | 36 | 13 | | 37 | 17 | | 38 | 5 | | 39 | 10 | | 40 | 11 | | 41 | 10 | | 42 | 20 | | 43 | 7 | | 44 | 2 | | 45 | 14 | | 46 | 6 | | 47 | 6 | | 48 | 6 | | 49 | 7 |
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| 35.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.20496894409937888 | | totalSentences | 161 | | uniqueOpeners | 33 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 152 | | matches | | 0 | "Then you are already dead." | | 1 | "Just concrete and iron." |
| | ratio | 0.013 | |
| 22.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 75 | | totalSentences | 152 | | matches | | 0 | "Her breath came in short," | | 1 | "She scanned the alleyways to" | | 2 | "He moved with unnatural fluidity," | | 3 | "Her boots slapped against the" | | 4 | "She checked her left wrist." | | 5 | "It was nearly midnight." | | 6 | "He reached the edge of" | | 7 | "She raised her weapon, finger" | | 8 | "You have nowhere to go." | | 9 | "He held something in his" | | 10 | "He tossed the object toward" | | 11 | "It landed on the threshold" | | 12 | "They swung inward without a" | | 13 | "She knew the reports." | | 14 | "He slipped through the gap" | | 15 | "Her heart hammered against her" | | 16 | "She remembered DS Morris." | | 17 | "She touched the scar on" | | 18 | "She stepped to the threshold." | | 19 | "He leaned against a tiled" |
| | ratio | 0.493 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 143 | | totalSentences | 152 | | matches | | 0 | "Harlow Quinn ran with the" | | 1 | "Water soaked through her trench" | | 2 | "Her breath came in short," | | 3 | "She scanned the alleyways to" | | 4 | "The suspect wore a dark" | | 5 | "He moved with unnatural fluidity," | | 6 | "Harlow did not slow." | | 7 | "Her boots slapped against the" | | 8 | "She checked her left wrist." | | 9 | "The worn leather of her" | | 10 | "It was nearly midnight." | | 11 | "The shout tore from her" | | 12 | "The figure did not break" | | 13 | "He reached the edge of" | | 14 | "Harlow skidded around a corner," | | 15 | "She raised her weapon, finger" | | 16 | "You have nowhere to go." | | 17 | "The man turned." | | 18 | "A flash of light from" | | 19 | "He held something in his" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 152 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "The air around the station entrance shimmered, distorting the rain that fell near the doorway." | | 1 | "She walked past him, her boots echoing on the metal grating of the stairs." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |