| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned slowly [slowly]" |
| | dialogueSentences | 43 | | tagDensity | 0.163 | | leniency | 0.326 | | rawRatio | 0.143 | | effectiveRatio | 0.047 | |
| 82.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1176 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "carefully" | | 2 | "perfectly" |
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| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1176 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1176 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 671 | | uniqueNames | 8 | | maxNameDensity | 0.89 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Dunne" | | discoveredNames | | Tube | 1 | | Quinn | 6 | | Ray | 1 | | Pollard | 3 | | Sergeant | 1 | | Gareth | 1 | | Dunne | 6 | | Bible | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Ray" | | 2 | "Pollard" | | 3 | "Sergeant" | | 4 | "Gareth" | | 5 | "Dunne" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a shallow groove, perfectly c" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1176 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 26.13 | | std | 26.39 | | cv | 1.01 | | sampleLengths | | 0 | 68 | | 1 | 39 | | 2 | 1 | | 3 | 3 | | 4 | 30 | | 5 | 60 | | 6 | 10 | | 7 | 1 | | 8 | 2 | | 9 | 5 | | 10 | 102 | | 11 | 48 | | 12 | 4 | | 13 | 9 | | 14 | 51 | | 15 | 3 | | 16 | 18 | | 17 | 73 | | 18 | 3 | | 19 | 25 | | 20 | 4 | | 21 | 32 | | 22 | 10 | | 23 | 5 | | 24 | 23 | | 25 | 3 | | 26 | 68 | | 27 | 11 | | 28 | 3 | | 29 | 6 | | 30 | 14 | | 31 | 62 | | 32 | 21 | | 33 | 45 | | 34 | 27 | | 35 | 66 | | 36 | 10 | | 37 | 13 | | 38 | 89 | | 39 | 21 | | 40 | 1 | | 41 | 2 | | 42 | 39 | | 43 | 11 | | 44 | 35 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 48 | | matches | | 0 | "been taken" | | 1 | "being lifted" | | 2 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 103 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 84 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 671 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.029806259314456036 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00894187779433681 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 14 | | std | 10.71 | | cv | 0.765 | | sampleLengths | | 0 | 27 | | 1 | 22 | | 2 | 10 | | 3 | 9 | | 4 | 17 | | 5 | 22 | | 6 | 1 | | 7 | 3 | | 8 | 17 | | 9 | 13 | | 10 | 20 | | 11 | 20 | | 12 | 5 | | 13 | 15 | | 14 | 10 | | 15 | 1 | | 16 | 2 | | 17 | 5 | | 18 | 17 | | 19 | 30 | | 20 | 6 | | 21 | 27 | | 22 | 4 | | 23 | 18 | | 24 | 14 | | 25 | 34 | | 26 | 4 | | 27 | 9 | | 28 | 24 | | 29 | 27 | | 30 | 3 | | 31 | 18 | | 32 | 12 | | 33 | 12 | | 34 | 5 | | 35 | 4 | | 36 | 5 | | 37 | 35 | | 38 | 3 | | 39 | 25 | | 40 | 4 | | 41 | 20 | | 42 | 12 | | 43 | 6 | | 44 | 4 | | 45 | 2 | | 46 | 3 | | 47 | 23 | | 48 | 3 | | 49 | 26 |
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| 95.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.6666666666666666 | | totalSentences | 84 | | uniqueOpeners | 56 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | | ratio | 0.022 | |
| 72.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 46 | | matches | | 0 | "He had the grey, overcautious" | | 1 | "Her watch, the old leather" | | 2 | "She pushed it back up." | | 3 | "She stepped through the gap." | | 4 | "His eyes were open." | | 5 | "His mouth was open too," | | 6 | "He was a broad man," | | 7 | "His trousers had a faint" | | 8 | "She let her gaze travel" | | 9 | "Her colleague sighed through his" | | 10 | "She stood and brushed the" | | 11 | "She turned slowly, studying the" | | 12 | "She stepped carefully along the" | | 13 | "It wasn't a stain or" | | 14 | "It looked like a shallow" | | 15 | "She held it up to" | | 16 | "She let the fibre drop" |
| | ratio | 0.37 | |
| 25.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 46 | | matches | | 0 | "The Tube station had been" | | 1 | "Harlow Quinn counted six of" | | 2 | "Water ran down the tiled" | | 3 | "The air tasted of wet" | | 4 | "Constable Ray Pollard waited at" | | 5 | "He had the grey, overcautious" | | 6 | "Pollard swung the torch toward" | | 7 | "Quinn pulled a pair of" | | 8 | "Her watch, the old leather" | | 9 | "She pushed it back up." | | 10 | "She stepped through the gap." | | 11 | "The platform stretched in both" | | 12 | "Someone had laid out a" | | 13 | "Each held a dull, grey" | | 14 | "His eyes were open." | | 15 | "His mouth was open too," | | 16 | "Detective Sergeant Gareth Dunne stood" | | 17 | "He was a broad man," | | 18 | "Dunne nodded at the dishes" | | 19 | "Quinn crouched at the edge" |
| | ratio | 0.87 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "He had the grey, overcautious face of a man who had already been sick once and was deciding whether to go again." | | 1 | "He was a broad man, thick through the neck, and he had the look of someone who had been waiting for a sensible explanation to arrive so he could stop thinking a…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 43 | | tagDensity | 0.023 | | leniency | 0.047 | | rawRatio | 1 | | effectiveRatio | 0.047 | |