| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 1 | | adverbTags | | 0 | "the stallholder said quickly [quickly]" |
| | dialogueSentences | 52 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0.05 | | effectiveRatio | 0.038 | |
| 96.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1259 | | 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) | |
| 56.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1259 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silence" | | 1 | "stomach" | | 2 | "footsteps" | | 3 | "etched" | | 4 | "perfect" | | 5 | "traced" | | 6 | "scanned" | | 7 | "pulsed" | | 8 | "raced" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1259 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 851 | | uniqueNames | 10 | | maxNameDensity | 2 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 17 | | Footsteps | 1 | | Kowalski | 1 | | Eva | 14 | | Veil | 2 | | Market | 1 | | Tube | 1 | | Camden | 2 | | Holborn | 1 | | Compass | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Footsteps" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Camden" | | 5 | "Compass" |
| | places | (empty) | | globalScore | 0.501 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1259 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 20.64 | | std | 20.5 | | cv | 0.993 | | sampleLengths | | 0 | 4 | | 1 | 13 | | 2 | 45 | | 3 | 4 | | 4 | 27 | | 5 | 69 | | 6 | 25 | | 7 | 10 | | 8 | 51 | | 9 | 6 | | 10 | 18 | | 11 | 9 | | 12 | 1 | | 13 | 10 | | 14 | 107 | | 15 | 14 | | 16 | 5 | | 17 | 30 | | 18 | 33 | | 19 | 7 | | 20 | 7 | | 21 | 31 | | 22 | 14 | | 23 | 7 | | 24 | 4 | | 25 | 39 | | 26 | 29 | | 27 | 31 | | 28 | 4 | | 29 | 17 | | 30 | 13 | | 31 | 2 | | 32 | 39 | | 33 | 5 | | 34 | 7 | | 35 | 47 | | 36 | 8 | | 37 | 18 | | 38 | 5 | | 39 | 1 | | 40 | 8 | | 41 | 3 | | 42 | 10 | | 43 | 46 | | 44 | 17 | | 45 | 8 | | 46 | 23 | | 47 | 21 | | 48 | 7 | | 49 | 7 |
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| 97.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 94 | | matches | | 0 | "been turned" | | 1 | "was bricked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 126 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 857 | | adjectiveStacks | 1 | | stackExamples | | 0 | "wide behind round glasses." |
| | adverbCount | 21 | | adverbRatio | 0.024504084014002333 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004667444574095682 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.99 | | std | 7.35 | | cv | 0.736 | | sampleLengths | | 0 | 4 | | 1 | 13 | | 2 | 10 | | 3 | 15 | | 4 | 20 | | 5 | 4 | | 6 | 4 | | 7 | 8 | | 8 | 15 | | 9 | 16 | | 10 | 11 | | 11 | 4 | | 12 | 2 | | 13 | 2 | | 14 | 22 | | 15 | 12 | | 16 | 9 | | 17 | 16 | | 18 | 10 | | 19 | 7 | | 20 | 23 | | 21 | 7 | | 22 | 14 | | 23 | 6 | | 24 | 11 | | 25 | 7 | | 26 | 9 | | 27 | 1 | | 28 | 10 | | 29 | 6 | | 30 | 23 | | 31 | 21 | | 32 | 16 | | 33 | 11 | | 34 | 10 | | 35 | 20 | | 36 | 14 | | 37 | 5 | | 38 | 5 | | 39 | 25 | | 40 | 6 | | 41 | 13 | | 42 | 14 | | 43 | 7 | | 44 | 3 | | 45 | 2 | | 46 | 2 | | 47 | 5 | | 48 | 26 | | 49 | 4 |
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| 53.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.38095238095238093 | | totalSentences | 126 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 72 | | matches | | 0 | "Her left wrist caught the" | | 1 | "His eyes stayed open." | | 2 | "His shirt rode up and" | | 3 | "Her curly red hair had" | | 4 | "She tucked a strand behind" | | 5 | "She tugged her satchel open" | | 6 | "It spun, lazy and wild," | | 7 | "It never settled." | | 8 | "Her hand shook and she" | | 9 | "She scanned the crates." | | 10 | "His pockets had been turned" | | 11 | "She looked at Quinn." | | 12 | "Her boots knocked one of" | | 13 | "It was a second bone" | | 14 | "she said to Eva" | | 15 | "He wore a lovely little" | | 16 | "He painted with the blood," | | 17 | "He looked up and Quinn" |
| | ratio | 0.25 | |
| 8.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 72 | | matches | | 0 | "Quinn's voice cut through the" | | 1 | "The kid froze with his" | | 2 | "The compass sat on the" | | 3 | "The stallholder stumbled back." | | 4 | "Quinn stepped over the chalk" | | 5 | "Her left wrist caught the" | | 6 | "The dead man lay spread-eagled" | | 7 | "His eyes stayed open." | | 8 | "His shirt rode up and" | | 9 | "The air around him smelled" | | 10 | "the stallholder said" | | 11 | "Footsteps clattered on the old" | | 12 | "Eva Kowalski ducked under the" | | 13 | "Her curly red hair had" | | 14 | "She tucked a strand behind" | | 15 | "Quinn said to the stallholder" | | 16 | "Eva crouched beside her and" | | 17 | "The Veil Market breathed around" | | 18 | "The abandoned Tube station beneath" | | 19 | "This month it had taken" |
| | ratio | 0.903 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "Late twenties, pale, market-stall apron stained with something that glistened green under the strip lights." | | 1 | "The dead man lay spread-eagled across three overturned crates behind a stall that sold bottled shadows." | | 2 | "The small brass compass with a face etched with protective sigils, its casing with a patina of verdigris that spoke of old hands and older bargains." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 52 | | tagDensity | 0.365 | | leniency | 0.731 | | rawRatio | 0.053 | | effectiveRatio | 0.038 | |