| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 110 | | tagDensity | 0.127 | | leniency | 0.255 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2005 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 87.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2005 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "aligned" | | 2 | "pulse" | | 3 | "trembled" |
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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 | 178 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 178 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 274 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2004 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1385 | | uniqueNames | 10 | | maxNameDensity | 2.31 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 32 | | Inspector | 1 | | Bell | 12 | | Voss | 7 | | British | 1 | | Museum | 1 | | Met | 1 | | Eva | 13 | | One | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Voss" | | 4 | "Met" | | 5 | "Eva" | | 6 | "One" |
| | places | | | globalScore | 0.345 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 115 | | 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 | 2004 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 274 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 153 | | mean | 13.1 | | std | 13.01 | | cv | 0.993 | | sampleLengths | | 0 | 15 | | 1 | 75 | | 2 | 23 | | 3 | 7 | | 4 | 29 | | 5 | 3 | | 6 | 15 | | 7 | 1 | | 8 | 50 | | 9 | 31 | | 10 | 31 | | 11 | 5 | | 12 | 54 | | 13 | 3 | | 14 | 12 | | 15 | 4 | | 16 | 8 | | 17 | 4 | | 18 | 40 | | 19 | 6 | | 20 | 6 | | 21 | 2 | | 22 | 2 | | 23 | 43 | | 24 | 3 | | 25 | 12 | | 26 | 2 | | 27 | 25 | | 28 | 3 | | 29 | 1 | | 30 | 53 | | 31 | 11 | | 32 | 12 | | 33 | 1 | | 34 | 11 | | 35 | 4 | | 36 | 6 | | 37 | 8 | | 38 | 17 | | 39 | 5 | | 40 | 1 | | 41 | 7 | | 42 | 11 | | 43 | 5 | | 44 | 29 | | 45 | 13 | | 46 | 28 | | 47 | 3 | | 48 | 3 | | 49 | 5 |
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| 99.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 178 | | matches | | 0 | "was braided" | | 1 | "been broken" | | 2 | "been folded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 274 | | ratio | 0.004 | | matches | | 0 | "The wound had been cut into fabric and skin after he was dead—or after the blood had left him." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1387 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.014419610670511895 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007209805335255948 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 274 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 274 | | mean | 7.31 | | std | 4.87 | | cv | 0.666 | | sampleLengths | | 0 | 15 | | 1 | 16 | | 2 | 13 | | 3 | 8 | | 4 | 12 | | 5 | 26 | | 6 | 8 | | 7 | 9 | | 8 | 6 | | 9 | 7 | | 10 | 9 | | 11 | 20 | | 12 | 3 | | 13 | 15 | | 14 | 1 | | 15 | 6 | | 16 | 14 | | 17 | 3 | | 18 | 9 | | 19 | 18 | | 20 | 14 | | 21 | 11 | | 22 | 6 | | 23 | 5 | | 24 | 26 | | 25 | 5 | | 26 | 23 | | 27 | 11 | | 28 | 20 | | 29 | 3 | | 30 | 12 | | 31 | 4 | | 32 | 3 | | 33 | 5 | | 34 | 4 | | 35 | 8 | | 36 | 13 | | 37 | 4 | | 38 | 15 | | 39 | 6 | | 40 | 6 | | 41 | 2 | | 42 | 2 | | 43 | 5 | | 44 | 7 | | 45 | 4 | | 46 | 12 | | 47 | 4 | | 48 | 11 | | 49 | 3 |
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| 44.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.2956204379562044 | | totalSentences | 274 | | uniqueOpeners | 81 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 156 | | matches | | 0 | "Too narrow for the quantity" | | 1 | "Then the flash died, and" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 156 | | matches | | 0 | "Their lamps burned blue, green," | | 1 | "Her worn leather watch showed" | | 2 | "Her colleague, Inspector Bell, waited" | | 3 | "He had taken off his" | | 4 | "His dark coat had a" | | 5 | "She crouched at the edge" | | 6 | "His fingers had curled against" | | 7 | "His cuffs were dry." | | 8 | "She leaned closer." | | 9 | "She clutched a worn leather" | | 10 | "His grey beard was braided" | | 11 | "Its hands stood at 2:17." | | 12 | "She turned towards Eva." | | 13 | "Her fingers tightened on the" | | 14 | "She walked the length of" | | 15 | "Its shadow fell towards the" | | 16 | "Its edges were too clean." | | 17 | "She leaned closer." | | 18 | "They formed a broken circle," | | 19 | "They had the repeated, careful" |
| | ratio | 0.218 | |
| 27.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 156 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "CAMDEN TOWN, it read, though" | | 2 | "A line of market stalls" | | 3 | "Their lamps burned blue, green," | | 4 | "Quinn took in the scene" | | 5 | "Her worn leather watch showed" | | 6 | "The market should have been" | | 7 | "A uniformed constable near the" | | 8 | "The constable glanced towards the" | | 9 | "Traders and buyers had gathered" | | 10 | "Nobody looked frightened." | | 11 | "A woman in a veil" | | 12 | "A man with antlers braided" | | 13 | "Her colleague, Inspector Bell, waited" | | 14 | "He had taken off his" | | 15 | "A thin cut marked his" | | 16 | "Quinn looked at the body." | | 17 | "Voss lay on the platform" | | 18 | "His dark coat had a" | | 19 | "A pool of blood spread" |
| | ratio | 0.865 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 1 | | matches | | 0 | "Voss lay on the platform between two stalls, his boots aligned with the edge as if someone had placed him there after death." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 110 | | tagDensity | 0.127 | | leniency | 0.255 | | rawRatio | 0.071 | | effectiveRatio | 0.018 | |