| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "She glanced back [back]" |
| | dialogueSentences | 43 | | tagDensity | 0.233 | | leniency | 0.465 | | rawRatio | 0.1 | | effectiveRatio | 0.047 | |
| 93.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1522 | | totalAiIsmAdverbs | 2 | | 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) | |
| 47.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1522 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "oppressive" | | 3 | "velvet" | | 4 | "pulsed" | | 5 | "standard" | | 6 | "weight" | | 7 | "perfect" | | 8 | "etched" | | 9 | "crystal" | | 10 | "profound" | | 11 | "pawn" | | 12 | "raced" | | 13 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 8 | | totalWords | 1520 | | ratio | 0.005 | | matches | | 0 | "Veil Compass – attuned. One careful owner." | | 1 | "this" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 784 | | uniqueNames | 13 | | maxNameDensity | 1.91 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Town | 1 | | Tube | 1 | | Detective | 1 | | Harlow | 15 | | Quinn | 1 | | Met | 1 | | Veil | 1 | | Market | 1 | | Miller | 5 | | Finch | 3 | | Kowalski | 1 | | Eva | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Miller" | | 4 | "Finch" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | | globalScore | 0.543 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1520 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 33.78 | | std | 27.47 | | cv | 0.813 | | sampleLengths | | 0 | 70 | | 1 | 105 | | 2 | 80 | | 3 | 20 | | 4 | 58 | | 5 | 3 | | 6 | 34 | | 7 | 82 | | 8 | 4 | | 9 | 37 | | 10 | 48 | | 11 | 8 | | 12 | 32 | | 13 | 9 | | 14 | 21 | | 15 | 61 | | 16 | 8 | | 17 | 51 | | 18 | 4 | | 19 | 17 | | 20 | 6 | | 21 | 53 | | 22 | 1 | | 23 | 66 | | 24 | 7 | | 25 | 62 | | 26 | 71 | | 27 | 7 | | 28 | 6 | | 29 | 2 | | 30 | 6 | | 31 | 53 | | 32 | 7 | | 33 | 36 | | 34 | 27 | | 35 | 46 | | 36 | 8 | | 37 | 4 | | 38 | 47 | | 39 | 34 | | 40 | 70 | | 41 | 8 | | 42 | 56 | | 43 | 21 | | 44 | 64 |
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| 92.73% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 84 | | matches | | 0 | "been hauled" | | 1 | "was ransacked" | | 2 | "were scattered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 127 | | matches | | |
| 70.22% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 118 | | ratio | 0.025 | | matches | | 0 | "The edges were not torn or ragged; they looked seared, cauterized." | | 1 | "In his right hand, the bone token—a yellowed knucklebone etched with tiny, spiralling symbols." | | 2 | "The ransacking was violent—trays flung, the case forced—but it was focused." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 972 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, hand-written placard" |
| | adverbCount | 22 | | adverbRatio | 0.02263374485596708 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.012345679012345678 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 12.88 | | std | 12.11 | | cv | 0.94 | | sampleLengths | | 0 | 20 | | 1 | 28 | | 2 | 11 | | 3 | 11 | | 4 | 9 | | 5 | 24 | | 6 | 5 | | 7 | 17 | | 8 | 11 | | 9 | 5 | | 10 | 18 | | 11 | 8 | | 12 | 8 | | 13 | 12 | | 14 | 19 | | 15 | 16 | | 16 | 26 | | 17 | 7 | | 18 | 4 | | 19 | 15 | | 20 | 1 | | 21 | 13 | | 22 | 7 | | 23 | 38 | | 24 | 3 | | 25 | 4 | | 26 | 30 | | 27 | 6 | | 28 | 1 | | 29 | 9 | | 30 | 12 | | 31 | 14 | | 32 | 11 | | 33 | 6 | | 34 | 4 | | 35 | 5 | | 36 | 14 | | 37 | 4 | | 38 | 37 | | 39 | 8 | | 40 | 3 | | 41 | 13 | | 42 | 9 | | 43 | 8 | | 44 | 4 | | 45 | 3 | | 46 | 8 | | 47 | 32 | | 48 | 9 | | 49 | 21 |
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| 69.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4406779661016949 | | totalSentences | 118 | | uniqueOpeners | 52 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "Only one case was open." | | 1 | "Then she looked at his" |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 77 | | matches | | 0 | "Her footsteps echoed off the" | | 1 | "She snapped on a pair" | | 2 | "His face was pale under" | | 3 | "She crouched by the body," | | 4 | "She checked his hands." | | 5 | "It was ransacked." | | 6 | "She picked it up with" | | 7 | "She adjusted the worn leather" | | 8 | "She turned, her gaze critical" | | 9 | "She wore a worn leather" | | 10 | "She glanced back at the" | | 11 | "She looked past the obvious," | | 12 | "She studied the ground around" | | 13 | "She looked at the stall" | | 14 | "She took a small evidence" | | 15 | "They held a faint, smoky" | | 16 | "She looked at Finch’s hand," | | 17 | "His expression wasn’t one of" | | 18 | "It was blank, almost serene." | | 19 | "She looked at Eva" |
| | ratio | 0.26 | |
| 24.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 77 | | matches | | 0 | "The stale, damp air of" | | 1 | "Her footsteps echoed off the" | | 2 | "The call had been bizarre," | | 3 | "A body, found in a" | | 4 | "The platform stretched before her," | | 5 | "Glass jars held luminescent fungi." | | 6 | "A stack of leather-bound books" | | 7 | "This was the Veil Market." | | 8 | "The briefing had been thin," | | 9 | "The victim had one clutched" | | 10 | "The scene was a jarring" | | 11 | "Uniforms had cordoned off a" | | 12 | "The forensic team in their" | | 13 | "A single, precise wound punctured" | | 14 | "Harlow’s sharp jaw tightened." | | 15 | "She snapped on a pair" | | 16 | "Sergeant Miller, a man built" | | 17 | "His face was pale under" | | 18 | "Miller shifted his weight." | | 19 | "Harlow glanced at her own" |
| | ratio | 0.87 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "Makeshift stalls fashioned from rusted scaffolding poles and moth-eaten velvet drapes lined the walls, their counters littered with objects that made her skin p…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "She turned, her gaze critical" | | 1 | "Harlow said, her voice low" | | 2 | "Harlow stood, her bearing rigid" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Harlow continued (continue)" |
| | dialogueSentences | 43 | | tagDensity | 0.093 | | leniency | 0.186 | | rawRatio | 0.25 | | effectiveRatio | 0.047 | |