| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 26 | | tagDensity | 0.423 | | leniency | 0.846 | | rawRatio | 0.091 | | effectiveRatio | 0.077 | |
| 90.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1093 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 63.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1093 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "echoing" | | 1 | "etched" | | 2 | "magnetic" | | 3 | "footsteps" | | 4 | "silence" | | 5 | "processed" | | 6 | "chill" | | 7 | "pulse" |
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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 | 94 | | matches | (empty) | |
| 82.07% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1098 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 956 | | uniqueNames | 14 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 15 | | Victorian-era | 1 | | Patel | 7 | | Young | 1 | | Royal | 1 | | Military | 1 | | Police | 1 | | Met | 1 | | Morris | 4 | | Silence | 1 | | Southwark | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Patel" | | 3 | "Met" | | 4 | "Morris" | | 5 | "Silence" |
| | places | | | globalScore | 0.715 | | windowScore | 0.833 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 3 | | matches | | 0 | "something like ozone and copper, but wrong s" | | 1 | "something like this before, three years ago" | | 2 | "Not quite pointing at it, but toward the space where it lay" |
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| 17.85% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.821 | | wordCount | 1098 | | matches | | 0 | "not toward magnetic north, but toward the tunnel mouth behind her" | | 1 | "Not quite pointing at it, but toward the space" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 109 | | matches | | 0 | "screamed that something" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 27.45 | | std | 19.15 | | cv | 0.697 | | sampleLengths | | 0 | 54 | | 1 | 15 | | 2 | 4 | | 3 | 30 | | 4 | 9 | | 5 | 61 | | 6 | 49 | | 7 | 12 | | 8 | 14 | | 9 | 45 | | 10 | 30 | | 11 | 13 | | 12 | 62 | | 13 | 21 | | 14 | 49 | | 15 | 16 | | 16 | 38 | | 17 | 12 | | 18 | 4 | | 19 | 5 | | 20 | 6 | | 21 | 4 | | 22 | 33 | | 23 | 54 | | 24 | 47 | | 25 | 5 | | 26 | 17 | | 27 | 34 | | 28 | 28 | | 29 | 9 | | 30 | 74 | | 31 | 16 | | 32 | 27 | | 33 | 29 | | 34 | 5 | | 35 | 51 | | 36 | 10 | | 37 | 44 | | 38 | 26 | | 39 | 36 |
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| 94.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 94 | | matches | | 0 | "were splayed" | | 1 | "being dumped" | | 2 | "been closed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 157 | | matches | | 0 | "was leaving" | | 1 | "was finally beginning" |
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| 11.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 109 | | ratio | 0.046 | | matches | | 0 | "The man's mouth hung open in a silent scream, and when Quinn leaned closer, she caught the faint smell beneath the station's must — something like ozone and copper, but wrong somehow." | | 1 | "The casing had a green patina of verdigris, and when she tilted the bag, the needle swung — not toward magnetic north, but toward the tunnel mouth behind her." | | 2 | "And there — her beam caught something on the far wall." | | 3 | "And those scrapes on his hands —" | | 4 | "Or —" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 955 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.0387434554973822 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007329842931937173 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 10.07 | | std | 6.34 | | cv | 0.63 | | sampleLengths | | 0 | 11 | | 1 | 23 | | 2 | 20 | | 3 | 8 | | 4 | 5 | | 5 | 2 | | 6 | 4 | | 7 | 17 | | 8 | 13 | | 9 | 5 | | 10 | 4 | | 11 | 10 | | 12 | 17 | | 13 | 24 | | 14 | 10 | | 15 | 8 | | 16 | 22 | | 17 | 14 | | 18 | 2 | | 19 | 3 | | 20 | 10 | | 21 | 2 | | 22 | 10 | | 23 | 4 | | 24 | 7 | | 25 | 5 | | 26 | 32 | | 27 | 1 | | 28 | 13 | | 29 | 17 | | 30 | 8 | | 31 | 5 | | 32 | 10 | | 33 | 12 | | 34 | 11 | | 35 | 29 | | 36 | 4 | | 37 | 17 | | 38 | 10 | | 39 | 20 | | 40 | 11 | | 41 | 8 | | 42 | 7 | | 43 | 9 | | 44 | 4 | | 45 | 4 | | 46 | 15 | | 47 | 3 | | 48 | 12 | | 49 | 6 |
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| 82.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5137614678899083 | | totalSentences | 109 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 85 | | matches | | 0 | "Maybe six hours." | | 1 | "Too precise, too intentional." | | 2 | "More gouges, a trail of" |
| | ratio | 0.035 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 85 | | matches | | 0 | "She'd received the anonymous tip" | | 1 | "They emerged onto what had" | | 2 | "His wallet sat open beside" | | 3 | "She'd learned military precision during" | | 4 | "Her left hand moved unconsciously" | | 5 | "She stood and swept her" | | 6 | "They formed a rough circle" | | 7 | "She kept her voice level" | | 8 | "She turned back to the" | | 9 | "She played her torch along" | | 10 | "They led to the platform" | | 11 | "She pressed her palm against" | | 12 | "Her palm tingled, pins and" | | 13 | "She'd felt something like this" | | 14 | "They'd been investigating a series" | | 15 | "She thought of Morris, of" | | 16 | "she said finally" | | 17 | "Her watch felt heavy on" | | 18 | "She checked it without thinking:" |
| | ratio | 0.224 | |
| 95.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 85 | | matches | | 0 | "The abandoned Camden Tube station" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The beam from her torch" | | 3 | "She'd received the anonymous tip" | | 4 | "Body in the old station." | | 5 | "Quinn never came alone." | | 6 | "DS Patel's voice bounced off" | | 7 | "They emerged onto what had" | | 8 | "Someone had set up battery-powered" | | 9 | "The body lay near the" | | 10 | "His wallet sat open beside" | | 11 | "Quinn knelt, careful not to" | | 12 | "She'd learned military precision during" | | 13 | "The victim's eyes stared at" | | 14 | "Patel said, circling to the" | | 15 | "Quinn shifted her torch beam" | | 16 | "The fingers were splayed, knuckles" | | 17 | "The man's mouth hung open" | | 18 | "Her left hand moved unconsciously" | | 19 | "Patel held up an evidence" |
| | ratio | 0.729 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn descended the last metal staircase, her boots echoing against concrete that hadn't seen maintenance since the station closed in 1952." | | 1 | "The body lay near the platform edge, a man in his thirties wearing expensive trainers and a leather jacket that had seen better days." | | 2 | "The compass tucked beneath him as if he'd clutched it to his chest." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.192 | | leniency | 0.385 | | rawRatio | 0 | | effectiveRatio | 0 | |