| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1450 | | 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) | |
| 41.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1450 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "shattered" | | 1 | "perfect" | | 2 | "familiar" | | 3 | "aligned" | | 4 | "traced" | | 5 | "pulsed" | | 6 | "electric" | | 7 | "scanned" | | 8 | "silence" | | 9 | "echoed" | | 10 | "standard" | | 11 | "footsteps" |
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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 | 1 | | narrationSentences | 174 | | matches | | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 174 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1450 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 91.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1450 | | uniqueNames | 9 | | maxNameDensity | 1.17 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Tube | 2 | | Camden | 1 | | Ellis | 15 | | Eva | 2 | | Kowalski | 1 | | Morris | 2 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tube" | | 3 | "Ellis" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Morris" |
| | places | | | globalScore | 0.914 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 122 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared inside the cover of one volume" |
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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 | 1450 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 174 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 21.32 | | std | 16.93 | | cv | 0.794 | | sampleLengths | | 0 | 88 | | 1 | 16 | | 2 | 11 | | 3 | 50 | | 4 | 24 | | 5 | 5 | | 6 | 10 | | 7 | 14 | | 8 | 42 | | 9 | 11 | | 10 | 4 | | 11 | 10 | | 12 | 17 | | 13 | 47 | | 14 | 4 | | 15 | 5 | | 16 | 11 | | 17 | 10 | | 18 | 34 | | 19 | 3 | | 20 | 6 | | 21 | 16 | | 22 | 38 | | 23 | 9 | | 24 | 37 | | 25 | 7 | | 26 | 10 | | 27 | 58 | | 28 | 5 | | 29 | 10 | | 30 | 14 | | 31 | 57 | | 32 | 10 | | 33 | 24 | | 34 | 3 | | 35 | 15 | | 36 | 9 | | 37 | 31 | | 38 | 56 | | 39 | 10 | | 40 | 5 | | 41 | 45 | | 42 | 7 | | 43 | 14 | | 44 | 5 | | 45 | 16 | | 46 | 35 | | 47 | 39 | | 48 | 27 | | 49 | 20 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 174 | | matches | | 0 | "been sealed" | | 1 | "was arranged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 242 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1452 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.0337465564738292 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.009641873278236915 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 8.33 | | std | 4.18 | | cv | 0.502 | | sampleLengths | | 0 | 14 | | 1 | 15 | | 2 | 16 | | 3 | 9 | | 4 | 8 | | 5 | 9 | | 6 | 17 | | 7 | 7 | | 8 | 9 | | 9 | 3 | | 10 | 2 | | 11 | 2 | | 12 | 4 | | 13 | 5 | | 14 | 8 | | 15 | 10 | | 16 | 11 | | 17 | 16 | | 18 | 6 | | 19 | 8 | | 20 | 10 | | 21 | 5 | | 22 | 10 | | 23 | 3 | | 24 | 11 | | 25 | 8 | | 26 | 8 | | 27 | 4 | | 28 | 7 | | 29 | 15 | | 30 | 11 | | 31 | 4 | | 32 | 10 | | 33 | 7 | | 34 | 4 | | 35 | 6 | | 36 | 6 | | 37 | 12 | | 38 | 5 | | 39 | 9 | | 40 | 15 | | 41 | 4 | | 42 | 5 | | 43 | 11 | | 44 | 3 | | 45 | 7 | | 46 | 4 | | 47 | 8 | | 48 | 10 | | 49 | 7 |
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| 59.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3850574712643678 | | totalSentences | 174 | | uniqueOpeners | 67 | |
| 39.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 168 | | matches | | 0 | "Only squatters come down here." | | 1 | "Probably wandered in by mistake." |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 168 | | matches | | 0 | "Her flashlight beam sliced through" | | 1 | "She adjusted the worn leather" | | 2 | "He clicked his own light" | | 3 | "She examined the wound edges" | | 4 | "She lifted the victim's cold" | | 5 | "Its surface bore tiny carvings" | | 6 | "They use all kinds of" | | 7 | "She picked the instrument up" | | 8 | "Her light passed over abandoned" | | 9 | "She flipped another page." | | 10 | "We've cracked tougher ciphers in" | | 11 | "She moved toward the archway" | | 12 | "She ran her fingers along" | | 13 | "You see connections everywhere tonight." | | 14 | "She stepped back and compared" | | 15 | "He could have known the" | | 16 | "She opened the flap and" | | 17 | "Her research brought her here." | | 18 | "She compared it to marks" | | 19 | "She crouched again and traced" |
| | ratio | 0.226 | |
| 28.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 145 | | totalSentences | 168 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "Her flashlight beam sliced through" | | 2 | "She adjusted the worn leather" | | 3 | "The platform opened out below" | | 4 | "A body lay centered on" | | 5 | "DC Ellis stood a few" | | 6 | "He clicked his own light" | | 7 | "Another rough one." | | 8 | "Robbery that turned lethal." | | 9 | "Quinn knelt beside the victim." | | 10 | "She examined the wound edges" | | 11 | "The cut showed no jagged" | | 12 | "The blood had not sprayed" | | 13 | "Footprints circled the body in" | | 14 | "She lifted the victim's cold" | | 15 | "A small bone token rested" | | 16 | "Its surface bore tiny carvings" | | 17 | "This token served a purpose." | | 18 | "Ellis shifted his boots against" | | 19 | "Some gang password." |
| | ratio | 0.863 | |
| 29.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 168 | | matches | | 0 | "Even if you're right about" |
| | ratio | 0.006 | |
| 96.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 4 | | matches | | 0 | "Her flashlight beam sliced through layers of dust that hung motionless in the stale air." | | 1 | "Her light passed over abandoned stalls that held remnants of recent activity." | | 2 | "Her notes reference a veil that separates what we see from what operates underneath." | | 3 | "She turned it over and found a tiny engraving that matched one on the compass casing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |