| 70.97% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn turned back [back]" | | 1 | "Quinn knelt once [once]" |
| | dialogueSentences | 31 | | tagDensity | 0.419 | | leniency | 0.839 | | rawRatio | 0.154 | | effectiveRatio | 0.129 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1220 | | 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) | |
| 79.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1220 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "velvet" | | 1 | "pulsed" | | 2 | "echoing" | | 3 | "scanned" | | 4 | "etched" |
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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 | 75 | | matches | (empty) | |
| 66.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 819 | | uniqueNames | 12 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Camden | 2 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Northern | 1 | | Line | 1 | | Platform | 1 | | Kowalski | 1 | | Eva | 12 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.767 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to writhe in the torchlight" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.821 | | wordCount | 1218 | | matches | | 0 | "not at the corpse, but toward the far end of the platform" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 38.06 | | std | 33.24 | | cv | 0.873 | | sampleLengths | | 0 | 77 | | 1 | 79 | | 2 | 45 | | 3 | 78 | | 4 | 53 | | 5 | 46 | | 6 | 51 | | 7 | 110 | | 8 | 4 | | 9 | 5 | | 10 | 1 | | 11 | 66 | | 12 | 14 | | 13 | 12 | | 14 | 97 | | 15 | 38 | | 16 | 4 | | 17 | 12 | | 18 | 15 | | 19 | 60 | | 20 | 14 | | 21 | 10 | | 22 | 101 | | 23 | 24 | | 24 | 68 | | 25 | 7 | | 26 | 10 | | 27 | 76 | | 28 | 12 | | 29 | 5 | | 30 | 3 | | 31 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 134 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 92 | | ratio | 0.011 | | matches | | 0 | "She checked the worn leather watch on her left wrist—2:47 AM—and adjusted her grip on her torch." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.018137847642079808 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0036275695284159614 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 13.24 | | std | 9.64 | | cv | 0.728 | | sampleLengths | | 0 | 12 | | 1 | 17 | | 2 | 17 | | 3 | 19 | | 4 | 12 | | 5 | 9 | | 6 | 23 | | 7 | 19 | | 8 | 28 | | 9 | 16 | | 10 | 16 | | 11 | 13 | | 12 | 21 | | 13 | 9 | | 14 | 10 | | 15 | 17 | | 16 | 21 | | 17 | 14 | | 18 | 7 | | 19 | 15 | | 20 | 9 | | 21 | 8 | | 22 | 6 | | 23 | 11 | | 24 | 3 | | 25 | 4 | | 26 | 20 | | 27 | 2 | | 28 | 10 | | 29 | 8 | | 30 | 33 | | 31 | 2 | | 32 | 9 | | 33 | 9 | | 34 | 21 | | 35 | 17 | | 36 | 20 | | 37 | 32 | | 38 | 4 | | 39 | 5 | | 40 | 1 | | 41 | 10 | | 42 | 37 | | 43 | 19 | | 44 | 7 | | 45 | 7 | | 46 | 12 | | 47 | 8 | | 48 | 7 | | 49 | 36 |
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| 58.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.391304347826087 | | totalSentences | 92 | | uniqueOpeners | 36 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Just water and brick and" |
| | ratio | 0.015 | |
| 70.75% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 67 | | matches | | 0 | "Her boots struck the final" | | 1 | "She checked the worn leather" | | 2 | "She moved with military precision" | | 3 | "His tailored waistcoat was expensive," | | 4 | "Her fingers shook as she" | | 5 | "She clutched her worn leather" | | 6 | "Her freckled complexion looked pale" | | 7 | "She noted the victim's hands," | | 8 | "She shifted her satchel to" | | 9 | "Her closely cropped salt-and-pepper hair" | | 10 | "She scanned the platform, her" | | 11 | "She noticed a brass compass" | | 12 | "She stepped closer to the" | | 13 | "Her green eyes reflected the" | | 14 | "It felt cold, heavier than" | | 15 | "She walked toward the tracks," | | 16 | "Her red curls fell forward." | | 17 | "Her fingers went to her" | | 18 | "Her sharp jaw set with" | | 19 | "She checked the time again," |
| | ratio | 0.373 | |
| 4.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 67 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "Her boots struck the final" | | 2 | "She checked the worn leather" | | 3 | "The beam carved yellow wedges" | | 4 | "Water dripped somewhere in the" | | 5 | "The Veil Market occupied the" | | 6 | "Stalls lined the curved walls," | | 7 | "The air tasted of copper" | | 8 | "Quinn had entered with a" | | 9 | "She moved with military precision" | | 10 | "Tonight, the intelligence had led" | | 11 | "The corpse lay sprawled across" | | 12 | "A male vendor, middle-aged, throat" | | 13 | "His tailored waistcoat was expensive," | | 14 | "A silver pocket watch remained" | | 15 | "Blood pooled beneath him in" | | 16 | "Eva Kowalski emerged from behind" | | 17 | "The shorter woman adjusted her" | | 18 | "Her fingers shook as she" | | 19 | "She clutched her worn leather" |
| | ratio | 0.91 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "Her boots struck the final step with a metallic clang that rang through the abandoned Tube station." | | 1 | "Blood pooled beneath him in a dark mirror, reflecting the single hanging bulb that swayed in the draft from the tunnel." | | 2 | "The casing bore a patina of verdigris, the face etched with protective sigils that seemed to writhe in the torchlight." | | 3 | "The rails gleamed, disappearing into black water that lapped at the sleepers, stagnant and oily." | | 4 | "Her sharp jaw set with the certainty that had carried her through eighteen years of decorated service, through the case that had taken DS Morris three years ago…" |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "She walked, her bearing precise, each step deliberate" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |