| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 915 | | 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) | |
| 83.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 915 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "scanned" | | 1 | "flicked" | | 2 | "raced" |
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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 | 59 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 916 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "But more than that, she breathed in, there was something seriously supernatural swirling in the ether." |
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| 63.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 695 | | uniqueNames | 15 | | maxNameDensity | 1.73 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Tube | 1 | | Richtung | 1 | | Eston | 1 | | Place | 1 | | Clingmans | 1 | | Porshe | 1 | | Warren | 5 | | Richt | 1 | | Kip | 1 | | Eva | 3 | | Magic | 1 | | Gathering | 1 | | Leary | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Richtung" | | 3 | "Warren" | | 4 | "Richt" | | 5 | "Kip" | | 6 | "Eva" | | 7 | "Leary" |
| | places | | | globalScore | 0.637 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 916 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 33.49% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 53.88 | | std | 14.4 | | cv | 0.267 | | sampleLengths | | 0 | 58 | | 1 | 23 | | 2 | 56 | | 3 | 63 | | 4 | 45 | | 5 | 58 | | 6 | 82 | | 7 | 71 | | 8 | 40 | | 9 | 50 | | 10 | 59 | | 11 | 71 | | 12 | 43 | | 13 | 42 | | 14 | 50 | | 15 | 68 | | 16 | 37 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 59 | | matches | | |
| 99.75% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 133 | | matches | | 0 | "was explaining" | | 1 | "was watching" |
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| 53.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 64 | | ratio | 0.031 | | matches | | 0 | "By 11, the station emptied, the tables scraped back, and only the crushed goblets left the wild rumpus; as if they'd been guests to nothing more than a raucous pub night." | | 1 | "She looked at her watch - 14:00, she'd best limp on in to the ME and see what Kip had cooked up, phone in hand, already on course for the morgue, the mess he'd made of the file and the shoddy fresh instagram photos locked in her father's camera." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 537 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.03165735567970205 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0074487895716946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 14.31 | | std | 10.77 | | cv | 0.752 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 22 | | 3 | 15 | | 4 | 8 | | 5 | 5 | | 6 | 4 | | 7 | 8 | | 8 | 21 | | 9 | 18 | | 10 | 23 | | 11 | 22 | | 12 | 18 | | 13 | 14 | | 14 | 31 | | 15 | 20 | | 16 | 22 | | 17 | 16 | | 18 | 27 | | 19 | 14 | | 20 | 21 | | 21 | 20 | | 22 | 34 | | 23 | 37 | | 24 | 14 | | 25 | 26 | | 26 | 18 | | 27 | 32 | | 28 | 41 | | 29 | 18 | | 30 | 7 | | 31 | 5 | | 32 | 49 | | 33 | 3 | | 34 | 7 | | 35 | 9 | | 36 | 5 | | 37 | 7 | | 38 | 9 | | 39 | 2 | | 40 | 5 | | 41 | 6 | | 42 | 8 | | 43 | 3 | | 44 | 10 | | 45 | 6 | | 46 | 9 | | 47 | 6 | | 48 | 8 | | 49 | 42 |
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| 98.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.671875 | | totalSentences | 64 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 56 | | matches | | 0 | "Just before closing yesterday, a" | | 1 | "Of course she'd be hauled" | | 2 | "Always talking about her esoteric" |
| | ratio | 0.054 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 56 | | matches | | 0 | "She popped the lid on" | | 1 | "Their not-dconsult dismissed, Quinn let" | | 2 | "He rubbed his temples and" | | 3 | "She narrowed her eyes and" | | 4 | "She looked at her watch" | | 5 | "Her mind raced, turning in" | | 6 | "She stared at the game" | | 7 | "He looked up from his" | | 8 | "She sighed, resigning herself to" |
| | ratio | 0.161 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 56 | | matches | | 0 | "Detective Harlow Quinn tilted her" | | 1 | "Another flick, and the flash" | | 2 | "The abandoned Tube station was" | | 3 | "Quinn scanned the trashed office." | | 4 | "This crime scene was...unexpected." | | 5 | "She popped the lid on" | | 6 | "Quinn's money was on an" | | 7 | "Their not-dconsult dismissed, Quinn let" | | 8 | "Warren was a developer and" | | 9 | "Warren jogged up and bumped" | | 10 | "He rubbed his temples and" | | 11 | "She narrowed her eyes and" | | 12 | "Warren rolled his eyes and" | | 13 | "DJed thumbprint, he pocketed and" | | 14 | "Quinn shook her head, disbelief" | | 15 | "The game was watching back." | | 16 | "She looked at her watch" | | 17 | "Warren had spoken." | | 18 | "Quinn knew what she needed" | | 19 | "This weighed like a lasso" |
| | ratio | 0.589 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 56 | | matches | | 0 | "If so, what kind of" | | 1 | "If she had to hear" |
| | ratio | 0.036 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 1 | | matches | | 0 | "She narrowed her eyes and tightened her fists, staring at Warren who was explaining." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn shook, disbelief seeping in" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 1 | | effectiveRatio | 0.222 | |