| 42.11% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 3 | | adverbTags | | 0 | "she said flatly [flatly]" | | 1 | "She gestured vaguely [vaguely]" | | 2 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 27 | | tagDensity | 0.704 | | leniency | 1 | | rawRatio | 0.158 | | effectiveRatio | 0.158 | |
| 82.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 866 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 1.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 866 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "carried the weight" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "pulse" | | 1 | "chill" | | 2 | "scanned" | | 3 | "familiar" | | 4 | "etched" | | 5 | "carried the weight" | | 6 | "unreadable" | | 7 | "echoed" | | 8 | "clandestine" | | 9 | "whisper" | | 10 | "unspoken" | | 11 | "flicker" | | 12 | "profound" | | 13 | "sense of" | | 14 | "weight" | | 15 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 49 | | matches | | |
| 84.55% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 49 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 859 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 690 | | uniqueNames | 16 | | maxNameDensity | 0.43 | | worstName | "Silas" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Carter | 1 | | Rory | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Eva | 1 | | You | 1 | | Silas | 3 | | Blackwood | 1 | | Evan | 1 | | London | 1 | | Irish | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Rory" | | 4 | "Yu-Fei" | | 5 | "Cheung" | | 6 | "Empress" | | 7 | "Eva" | | 8 | "Silas" | | 9 | "Blackwood" | | 10 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 3.492 | | wordCount | 859 | | matches | | 0 | "Not the sharp-suited, impeccably groomed man she remembered from university, but a figure draped in a worn tweed jacket, his grey-streaked au" | | 1 | "not just curiosity, but a profound sadness, a sense of loss" | | 2 | "not just on her shoulders, but on the space between them" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 56 | | matches | | |
| 58.52% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 71.58 | | std | 25.4 | | cv | 0.355 | | sampleLengths | | 0 | 128 | | 1 | 101 | | 2 | 50 | | 3 | 51 | | 4 | 50 | | 5 | 71 | | 6 | 57 | | 7 | 65 | | 8 | 59 | | 9 | 58 | | 10 | 58 | | 11 | 111 |
| |
| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 49 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 107 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 56 | | ratio | 0.071 | | matches | | 0 | "Inside, the air hung thick with the scent of stale beer, damp wool, and something faintly metallic—like old coins." | | 1 | "She’d been avoiding this place for months, but Eva’s insistent texts—“You *have* to come down, Rory! Just one drink, promise?”—had worn her down." | | 2 | "She saw it then, the flicker of something in his eyes—not just curiosity, but a profound sadness, a sense of loss." | | 3 | "The past wasn’t just behind them; it was a living, breathing presence in the dim light of The Raven’s Nest." |
| |
| 98.49% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 695 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.041726618705035974 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.017266187050359712 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 15.34 | | std | 6.99 | | cv | 0.456 | | sampleLengths | | 0 | 22 | | 1 | 19 | | 2 | 22 | | 3 | 28 | | 4 | 23 | | 5 | 14 | | 6 | 9 | | 7 | 30 | | 8 | 21 | | 9 | 13 | | 10 | 14 | | 11 | 14 | | 12 | 17 | | 13 | 21 | | 14 | 12 | | 15 | 11 | | 16 | 6 | | 17 | 24 | | 18 | 10 | | 19 | 22 | | 20 | 22 | | 21 | 6 | | 22 | 14 | | 23 | 13 | | 24 | 20 | | 25 | 10 | | 26 | 14 | | 27 | 9 | | 28 | 25 | | 29 | 3 | | 30 | 18 | | 31 | 2 | | 32 | 21 | | 33 | 3 | | 34 | 7 | | 35 | 21 | | 36 | 13 | | 37 | 6 | | 38 | 24 | | 39 | 21 | | 40 | 8 | | 41 | 7 | | 42 | 8 | | 43 | 26 | | 44 | 6 | | 45 | 11 | | 46 | 12 | | 47 | 13 | | 48 | 22 | | 49 | 11 |
| |
| 62.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.42857142857142855 | | totalSentences | 56 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 32.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 47 | | matches | | 0 | "She’d taken the night shift" | | 1 | "She’d been avoiding this place" | | 2 | "He wasn’t old, but he" | | 3 | "he murmured, his voice a" | | 4 | "He pushed himself upright, the" | | 5 | "He didn’t offer a hand," | | 6 | "She blinked, the name rolling" | | 7 | "he replied, his eyes flicking" | | 8 | "She’d fled London, changed her" | | 9 | "she said flatly, forcing a" | | 10 | "She gestured vaguely towards the" | | 11 | "His tone wasn’t accusatory, just" | | 12 | "She flinched slightly, the memory" | | 13 | "She didn’t add that it" | | 14 | "He chuckled, a low, gravelly" | | 15 | "He gestured vaguely towards the" | | 16 | "He paused, studying her" | | 17 | "she said, her voice tight" | | 18 | "She instinctively pulled her sleeve" | | 19 | "His voice dropped, becoming a" |
| | ratio | 0.468 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 47 | | matches | | 0 | "The Raven’s Nest’s green neon" | | 1 | "Aurora Carter, or Rory, as" | | 2 | "She’d taken the night shift" | | 3 | "She’d been avoiding this place" | | 4 | "There, near the scarred oak" | | 5 | "A faint limp marked his" | | 6 | "The signet ring on his" | | 7 | "Time had etched lines around" | | 8 | "He wasn’t old, but he" | | 9 | "he murmured, his voice a" | | 10 | "He pushed himself upright, the" | | 11 | "He didn’t offer a hand," | | 12 | "She blinked, the name rolling" | | 13 | "The word felt strange, distant" | | 14 | "The truth was, he looked" | | 15 | "The limp was new, a" | | 16 | "he replied, his eyes flicking" | | 17 | "A ghost of a smile" | | 18 | "The mention of Evan, her" | | 19 | "She’d fled London, changed her" |
| | ratio | 0.936 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 47 | | matches | | 0 | "Now, blinking against the low," |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "The man who’d once been her mentor, her confidant, now looked at her with the eyes of someone who’d lost a piece of himself." |
| |
| 46.05% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 3 | | matches | | 0 | "The word felt, distant" | | 1 | "she said, her voice tight" | | 2 | "He paused, his hazel eyes searching hers" |
| |
| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "She didn’t (do)" |
| | dialogueSentences | 27 | | tagDensity | 0.185 | | leniency | 0.37 | | rawRatio | 0.4 | | effectiveRatio | 0.148 | |