| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 3 | | adverbTags | | 0 | "Okafor stepped around [around]" | | 1 | "Quinn straightened again [again]" | | 2 | "Quinn sat back [back]" |
| | dialogueSentences | 36 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.25 | | effectiveRatio | 0.167 | |
| 81.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1066 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "precisely" | | 1 | "gently" | | 2 | "slightly" |
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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) | |
| 67.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1066 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "efficient" | | 1 | "glint" | | 2 | "etched" | | 3 | "magnetic" | | 4 | "unwavering" | | 5 | "weight" | | 6 | "measured" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 8 | | totalWords | 1064 | | ratio | 0.008 | | matches | | 0 | "Third market from the left, bone pays passage" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.98% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 781 | | uniqueNames | 7 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Okafor" | | discoveredNames | | Tube | 1 | | Priya | 1 | | Okafor | 8 | | Quinn | 10 | | Camden | 2 | | Morris | 1 | | Borough | 1 |
| | persons | | 0 | "Okafor" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.86 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 1064 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 24.74 | | std | 25.16 | | cv | 1.017 | | sampleLengths | | 0 | 6 | | 1 | 80 | | 2 | 36 | | 3 | 4 | | 4 | 10 | | 5 | 66 | | 6 | 33 | | 7 | 7 | | 8 | 11 | | 9 | 76 | | 10 | 6 | | 11 | 47 | | 12 | 2 | | 13 | 65 | | 14 | 5 | | 15 | 3 | | 16 | 15 | | 17 | 71 | | 18 | 5 | | 19 | 47 | | 20 | 84 | | 21 | 5 | | 22 | 1 | | 23 | 1 | | 24 | 4 | | 25 | 13 | | 26 | 32 | | 27 | 6 | | 28 | 11 | | 29 | 46 | | 30 | 20 | | 31 | 2 | | 32 | 3 | | 33 | 2 | | 34 | 27 | | 35 | 47 | | 36 | 7 | | 37 | 65 | | 38 | 12 | | 39 | 5 | | 40 | 27 | | 41 | 22 | | 42 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 126 | | matches | | 0 | "was wearing" | | 1 | "was beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 94.72% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 782 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.04603580562659847 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01918158567774936 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 10.43 | | std | 8.5 | | cv | 0.814 | | sampleLengths | | 0 | 6 | | 1 | 21 | | 2 | 22 | | 3 | 23 | | 4 | 3 | | 5 | 7 | | 6 | 4 | | 7 | 22 | | 8 | 14 | | 9 | 4 | | 10 | 5 | | 11 | 5 | | 12 | 10 | | 13 | 19 | | 14 | 8 | | 15 | 24 | | 16 | 5 | | 17 | 16 | | 18 | 17 | | 19 | 7 | | 20 | 11 | | 21 | 10 | | 22 | 7 | | 23 | 24 | | 24 | 2 | | 25 | 4 | | 26 | 15 | | 27 | 9 | | 28 | 5 | | 29 | 6 | | 30 | 23 | | 31 | 24 | | 32 | 2 | | 33 | 5 | | 34 | 3 | | 35 | 15 | | 36 | 1 | | 37 | 2 | | 38 | 21 | | 39 | 6 | | 40 | 5 | | 41 | 7 | | 42 | 5 | | 43 | 3 | | 44 | 5 | | 45 | 4 | | 46 | 6 | | 47 | 36 | | 48 | 4 | | 49 | 31 |
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| 73.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.5 | | totalSentences | 102 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 65 | | matches | | 0 | "Slightly too regular." | | 1 | "Slightly too clean." |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 65 | | matches | | 0 | "Her torch swept the length" | | 1 | "She filed that away." | | 2 | "She'd seen similar once before," | | 3 | "She hadn't known what to" | | 4 | "She was beginning to now." | | 5 | "She looked at his face." | | 6 | "It was something closer to" | | 7 | "His jacket pockets held a" | | 8 | "His wallet was intact." | | 9 | "Her torch caught something near" | | 10 | "She walked to it without" | | 11 | "She didn't pick it up." | | 12 | "She believed in evidence, sequence," | | 13 | "She believed in what could" | | 14 | "She pulled out her phone" | | 15 | "She photographed the section of" | | 16 | "Her torch found the compass" | | 17 | "She pressed two gloved fingers" |
| | ratio | 0.277 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 65 | | matches | | 0 | "The body was wearing mismatched" | | 1 | "Quinn crouched beside it, one" | | 2 | "The abandoned Tube station smelled" | | 3 | "Her torch swept the length" | | 4 | "She filed that away." | | 5 | "DS Priya Okafor appeared at" | | 6 | "Quinn straightened and looked around" | | 7 | "The station was Camden's forgotten" | | 8 | "Chalk markings ran in long," | | 9 | "These lines curved with purpose." | | 10 | "Okafor continued, pulling up photos" | | 11 | "Quinn walked the perimeter of" | | 12 | "The chalk markings were fresh," | | 13 | "The dust on the floor" | | 14 | "She'd seen similar once before," | | 15 | "She hadn't known what to" | | 16 | "She was beginning to now." | | 17 | "Okafor stepped around the body" | | 18 | "The DS angled her torch." | | 19 | "Quinn leant in." |
| | ratio | 0.8 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 19.70% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn crouched beside it, one knee millimetres from the puddle of congealed blood that had spread across the aged tile floor." | | 1 | "Her torch swept the length of the victim: male, early thirties, dressed in an expensive wool coat that hadn't done him much good." | | 2 | "The face bore markings around its circumference that weren't cardinal directions." | | 3 | "The needle, when she tilted it gently with a gloved fingertip, swung away from magnetic north and pointed steadily toward the far wall, toward a section of bare…" | | 4 | "Quinn had spent eighteen years building cases that held in court." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Okafor continued (continue)" |
| | dialogueSentences | 36 | | tagDensity | 0.139 | | leniency | 0.278 | | rawRatio | 0.2 | | effectiveRatio | 0.056 | |