| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.245 | | leniency | 0.49 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1406 | | 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) | |
| 78.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1406 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "etched" | | 2 | "flicked" | | 3 | "flicker" | | 4 | "silence" |
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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 | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 86 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1406 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 937 | | uniqueNames | 12 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Kentish" | | discoveredNames | | Kentish | 3 | | Town | 3 | | Road | 1 | | Northern | 1 | | Doyle | 6 | | Camden | 1 | | Quinn | 7 | | Museum | 1 | | Lividity | 1 | | Bermondsey | 1 | | Morris | 2 | | Met | 1 |
| | persons | | 0 | "Doyle" | | 1 | "Quinn" | | 2 | "Museum" | | 3 | "Lividity" | | 4 | "Morris" | | 5 | "Met" |
| | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "Road" | | 3 | "Camden" | | 4 | "Bermondsey" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1406 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 136 | | matches | | 0 | "used that door" | | 1 | "seen that sigil" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 23.05 | | std | 20.84 | | cv | 0.904 | | sampleLengths | | 0 | 31 | | 1 | 77 | | 2 | 32 | | 3 | 3 | | 4 | 2 | | 5 | 18 | | 6 | 42 | | 7 | 23 | | 8 | 37 | | 9 | 7 | | 10 | 39 | | 11 | 5 | | 12 | 2 | | 13 | 5 | | 14 | 14 | | 15 | 42 | | 16 | 10 | | 17 | 6 | | 18 | 56 | | 19 | 4 | | 20 | 13 | | 21 | 2 | | 22 | 32 | | 23 | 57 | | 24 | 35 | | 25 | 27 | | 26 | 13 | | 27 | 6 | | 28 | 46 | | 29 | 3 | | 30 | 1 | | 31 | 11 | | 32 | 11 | | 33 | 18 | | 34 | 13 | | 35 | 68 | | 36 | 45 | | 37 | 1 | | 38 | 16 | | 39 | 5 | | 40 | 2 | | 41 | 60 | | 42 | 7 | | 43 | 47 | | 44 | 41 | | 45 | 5 | | 46 | 9 | | 47 | 3 | | 48 | 37 | | 49 | 19 |
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| 98.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 99 | | matches | | 0 | "been chained" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 141 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 945 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.021164021164021163 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010582010582010583 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 10.34 | | std | 9.96 | | cv | 0.963 | | sampleLengths | | 0 | 12 | | 1 | 19 | | 2 | 13 | | 3 | 28 | | 4 | 4 | | 5 | 6 | | 6 | 26 | | 7 | 16 | | 8 | 16 | | 9 | 3 | | 10 | 2 | | 11 | 14 | | 12 | 4 | | 13 | 6 | | 14 | 10 | | 15 | 6 | | 16 | 20 | | 17 | 10 | | 18 | 13 | | 19 | 15 | | 20 | 14 | | 21 | 2 | | 22 | 4 | | 23 | 1 | | 24 | 1 | | 25 | 7 | | 26 | 14 | | 27 | 6 | | 28 | 12 | | 29 | 7 | | 30 | 5 | | 31 | 2 | | 32 | 5 | | 33 | 9 | | 34 | 5 | | 35 | 6 | | 36 | 13 | | 37 | 6 | | 38 | 17 | | 39 | 10 | | 40 | 6 | | 41 | 14 | | 42 | 14 | | 43 | 28 | | 44 | 4 | | 45 | 13 | | 46 | 2 | | 47 | 17 | | 48 | 13 | | 49 | 2 |
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| 92.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5882352941176471 | | totalSentences | 136 | | uniqueOpeners | 80 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 83 | | matches | | 0 | "Even the rats kept to" | | 1 | "Everywhere else the air sat" | | 2 | "Then, from the other side" |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 83 | | matches | | 0 | "Her torch beam slid over" | | 1 | "She crouched beside the body" | | 2 | "She lifted the dead man's" | | 3 | "She slipped it back and" | | 4 | "She swept her torch along" | | 5 | "She opened the dead man's" | | 6 | "It pointed at the tiled" | | 7 | "She had seen that sigil" | | 8 | "Her watch ticked loud in" | | 9 | "She photographed the compass twice" | | 10 | "She checked the waistcoat pocket" | | 11 | "She walked the platform edge" | | 12 | "Her breath fogged, she noticed," | | 13 | "She pressed her palm to" | | 14 | "He snapped the notebook shut" | | 15 | "He shifted his weight" | | 16 | "She held the compass flat" | | 17 | "It spun once, twice, and" | | 18 | "It came through the soles" |
| | ratio | 0.229 | |
| 74.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 83 | | matches | | 0 | "The tip-off came four hours" | | 1 | "Quinn had the call log" | | 2 | "Her torch beam slid over" | | 3 | "South Kentish Town station." | | 4 | "The Northern line still ran" | | 5 | "DS Doyle stood with his" | | 6 | "She crouched beside the body" | | 7 | "She lifted the dead man's" | | 8 | "Italian leather, hand-stitched, the sole" | | 9 | "Doyle read from the wallet" | | 10 | "Quinn took the wallet" | | 11 | "A neat little signature." | | 12 | "She slipped it back and" | | 13 | "The grille up top had" | | 14 | "Spiders had strung webs across" | | 15 | "Nobody had used that door" | | 16 | "She swept her torch along" | | 17 | "The dust told its own" | | 18 | "A man out for a" | | 19 | "The prints stopped dead in" |
| | ratio | 0.771 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "It came through the soles of her shoes now, a slow tide of it, and the dust on the ledge lifted and streamed sideways, off the platform, towards the seam, as if…" |
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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 | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 49 | | tagDensity | 0.082 | | leniency | 0.163 | | rawRatio | 0.25 | | effectiveRatio | 0.041 | |