| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1861 | | 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) | |
| 89.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1861 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "weight" | | 2 | "etch" | | 3 | "traced" |
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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 | 129 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1861 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1523 | | uniqueNames | 10 | | maxNameDensity | 1.25 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 19 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 3 | | Callum | 1 | | Rourke | 13 | | Kowalski | 1 | | Eva | 13 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Rourke" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0.876 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | 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 | 1861 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 31.54 | | std | 21.91 | | cv | 0.695 | | sampleLengths | | 0 | 64 | | 1 | 68 | | 2 | 57 | | 3 | 63 | | 4 | 25 | | 5 | 37 | | 6 | 61 | | 7 | 14 | | 8 | 43 | | 9 | 32 | | 10 | 8 | | 11 | 16 | | 12 | 61 | | 13 | 12 | | 14 | 11 | | 15 | 11 | | 16 | 50 | | 17 | 21 | | 18 | 12 | | 19 | 77 | | 20 | 6 | | 21 | 90 | | 22 | 53 | | 23 | 14 | | 24 | 23 | | 25 | 65 | | 26 | 9 | | 27 | 9 | | 28 | 35 | | 29 | 14 | | 30 | 27 | | 31 | 80 | | 32 | 21 | | 33 | 20 | | 34 | 55 | | 35 | 7 | | 36 | 38 | | 37 | 14 | | 38 | 19 | | 39 | 51 | | 40 | 34 | | 41 | 13 | | 42 | 17 | | 43 | 18 | | 44 | 10 | | 45 | 26 | | 46 | 39 | | 47 | 50 | | 48 | 63 | | 49 | 25 |
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| 67.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 14 | | totalSentences | 129 | | matches | | 0 | "been sealed" | | 1 | "been dampened" | | 2 | "being told" | | 3 | "been forced" | | 4 | "been fixed" | | 5 | "been pressed" | | 6 | "been redrawn" | | 7 | "been pressed" | | 8 | "been softened" | | 9 | "was completed" | | 10 | "been arranged" | | 11 | "been followed" | | 12 | "was laid" | | 13 | "been meant" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 242 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 152 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1526 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.020314547837483616 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.003931847968545216 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 12.24 | | std | 7.01 | | cv | 0.573 | | sampleLengths | | 0 | 16 | | 1 | 38 | | 2 | 10 | | 3 | 8 | | 4 | 45 | | 5 | 15 | | 6 | 8 | | 7 | 20 | | 8 | 20 | | 9 | 9 | | 10 | 10 | | 11 | 11 | | 12 | 8 | | 13 | 23 | | 14 | 11 | | 15 | 7 | | 16 | 18 | | 17 | 4 | | 18 | 7 | | 19 | 12 | | 20 | 14 | | 21 | 21 | | 22 | 6 | | 23 | 15 | | 24 | 19 | | 25 | 4 | | 26 | 10 | | 27 | 18 | | 28 | 25 | | 29 | 5 | | 30 | 13 | | 31 | 14 | | 32 | 8 | | 33 | 8 | | 34 | 8 | | 35 | 7 | | 36 | 16 | | 37 | 10 | | 38 | 15 | | 39 | 13 | | 40 | 7 | | 41 | 5 | | 42 | 5 | | 43 | 6 | | 44 | 8 | | 45 | 3 | | 46 | 20 | | 47 | 30 | | 48 | 6 | | 49 | 15 |
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| 40.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.24342105263157895 | | totalSentences | 152 | | uniqueOpeners | 37 | |
| 27.78% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 120 | | matches | | 0 | "Instead, the line had been" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 120 | | matches | | 0 | "She stopped at the bottom" | | 1 | "Her worn leather watch on" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "Her brown eyes moved from" | | 4 | "His hands stayed clasped between" | | 5 | "He did not need to" | | 6 | "He lifted his torch and" | | 7 | "She tucked a strand behind" | | 8 | "She leaned over the chalk" | | 9 | "She looked at the needle." | | 10 | "It sat locked in place," | | 11 | "She straightened and checked the" | | 12 | "She turned it toward the" | | 13 | "Its edges had not been" | | 14 | "She took a photograph with" | | 15 | "She checked his trouser pocket." | | 16 | "She crouched again and followed" | | 17 | "She held it up." | | 18 | "Her fingers traced the edge" | | 19 | "She looked back at the" |
| | ratio | 0.208 | |
| 18.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 120 | | matches | | 0 | "The bone token sat in" | | 1 | "She stopped at the bottom" | | 2 | "Her worn leather watch on" | | 3 | "The platform had been sealed" | | 4 | "The Veil Market had moved" | | 5 | "Quinn crossed the tape with" | | 6 | "Her closely cropped salt-and-pepper hair" | | 7 | "Her brown eyes moved from" | | 8 | "A sharp jawline tightened when" | | 9 | "A man lay on his" | | 10 | "A worn coat, no blood" | | 11 | "A bone token rested in" | | 12 | "The needle pointed straight to" | | 13 | "DS Callum Rourke crouched beyond" | | 14 | "His hands stayed clasped between" | | 15 | "He did not need to" | | 16 | "He lifted his torch and" | | 17 | "Eva Kowalski appeared from the" | | 18 | "She tucked a strand behind" | | 19 | "The worn leather bag sagged" |
| | ratio | 0.883 | |
| 83.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 120 | | matches | | 0 | "If the man had drawn" | | 1 | "If someone had stepped out" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva Kowalski appeared from the far side of the stall, her curly red hair escaping the band that held it back." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |