| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said softly [softly]" |
| | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.111 | | effectiveRatio | 0.1 | |
| 81.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1339 | | totalAiIsmAdverbs | 5 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "deliberately" | | 1 | "suddenly" | | 2 | "softly" | | 3 | "carefully" | | 4 | "really" |
| |
| 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) | |
| 43.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1339 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "silence" | | 1 | "sanctuary" | | 2 | "magnetic" | | 3 | "rhythmic" | | 4 | "scanning" | | 5 | "flicker" | | 6 | "familiar" | | 7 | "unreadable" | | 8 | "stark" | | 9 | "navigated" | | 10 | "unspoken" | | 11 | "intensity" | | 12 | "tracing" | | 13 | "flickered" |
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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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1332 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1133 | | uniqueNames | 17 | | maxNameDensity | 2.03 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 3 | | Soho | 1 | | Carter | 1 | | London | 2 | | Golden | 2 | | Empress | 2 | | Rory | 23 | | Silas | 6 | | Blackwood | 1 | | Prague | 2 | | Cardiff | 3 | | Evan | 2 | | Aurora | 1 | | Eva | 13 | | Pre-Law | 1 | | Irish | 1 |
| | persons | | 0 | "Nest" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Blackwood" | | 5 | "Evan" | | 6 | "Eva" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "London" | | 3 | "Golden" | | 4 | "Prague" | | 5 | "Cardiff" |
| | globalScore | 0.485 | | windowScore | 0.167 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "quite reach her eyes" | | 1 | "felt like a lifetime ago" |
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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 | 1332 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 49.33 | | std | 33.07 | | cv | 0.67 | | sampleLengths | | 0 | 112 | | 1 | 15 | | 2 | 101 | | 3 | 120 | | 4 | 1 | | 5 | 17 | | 6 | 48 | | 7 | 62 | | 8 | 20 | | 9 | 75 | | 10 | 9 | | 11 | 104 | | 12 | 17 | | 13 | 41 | | 14 | 52 | | 15 | 86 | | 16 | 39 | | 17 | 42 | | 18 | 35 | | 19 | 71 | | 20 | 34 | | 21 | 12 | | 22 | 33 | | 23 | 36 | | 24 | 18 | | 25 | 57 | | 26 | 75 |
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| 80.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 71 | | matches | | 0 | "were papered" | | 1 | "was required" | | 2 | "been polished" | | 3 | "was tied" | | 4 | "been forced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 168 | | matches | | 0 | "was polishing" | | 1 | "was wearing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 82 | | ratio | 0.073 | | matches | | 0 | "She checked her phone—her shift at the Golden Empress was done, and the rest of her evening belonged to the cramped flat she kept above the bar, though the silence of that small room had begun to feel like a slow suffocation." | | 1 | "Cool-headedness was a survival mechanism; since fleeing Cardiff since Evan, Rory had learned to catalog every exit, every flicker of the crowd." | | 2 | "Her gaze fixed on one of the black-and-white maps on the wall—a photograph of a narrow street in Prague." | | 3 | "She checked her watch—a delicate, expensive piece—and stood." | | 4 | "The conversation had circled its drain; they both knew the bridge between their lives had burned a long time ago." | | 5 | "Eva leaned down, pressing a quick, brief kiss to Rory's cheek—a gesture from a lifetime ago—before turning to leave." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1150 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 26 | | adverbRatio | 0.022608695652173914 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009565217391304347 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 16.24 | | std | 9.56 | | cv | 0.588 | | sampleLengths | | 0 | 21 | | 1 | 28 | | 2 | 21 | | 3 | 42 | | 4 | 15 | | 5 | 23 | | 6 | 21 | | 7 | 28 | | 8 | 29 | | 9 | 7 | | 10 | 23 | | 11 | 20 | | 12 | 15 | | 13 | 33 | | 14 | 22 | | 15 | 1 | | 16 | 15 | | 17 | 2 | | 18 | 16 | | 19 | 31 | | 20 | 1 | | 21 | 9 | | 22 | 24 | | 23 | 13 | | 24 | 16 | | 25 | 9 | | 26 | 11 | | 27 | 10 | | 28 | 16 | | 29 | 42 | | 30 | 7 | | 31 | 9 | | 32 | 22 | | 33 | 9 | | 34 | 17 | | 35 | 28 | | 36 | 28 | | 37 | 11 | | 38 | 6 | | 39 | 10 | | 40 | 5 | | 41 | 26 | | 42 | 5 | | 43 | 14 | | 44 | 33 | | 45 | 3 | | 46 | 31 | | 47 | 18 | | 48 | 9 | | 49 | 25 |
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| 50.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.34146341463414637 | | totalSentences | 82 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 90.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 65 | | matches | | 0 | "She checked her phone—her shift" | | 1 | "She pushed through the heavy" | | 2 | "He was polishing a highball" | | 3 | "He said nothing, waiting for" | | 4 | "She had the same rounded" | | 5 | "She, Aurora to the courts" | | 6 | "Her straight, shoulder-length black hair" | | 7 | "She was wearing her delivery" | | 8 | "She moved through the crowded" | | 9 | "She turned her head, her" | | 10 | "She was still the cool-headed" | | 11 | "She had left the Pre-Law" | | 12 | "She had left the safety," | | 13 | "Her gaze fixed on one" | | 14 | "She hadn't lost her intelligence" | | 15 | "She had gained something else" | | 16 | "It wasn't entirely a lie." | | 17 | "She liked the autonomy she’d" | | 18 | "She liked Silas, who asked" | | 19 | "She checked her watch—a delicate," |
| | ratio | 0.323 | |
| 21.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 65 | | matches | | 0 | "The neon sign above the" | | 1 | "Rory Carter stood beneath the" | | 2 | "She checked her phone—her shift" | | 3 | "She pushed through the heavy" | | 4 | "The air inside was thick" | | 5 | "The Nest was dimly lit," | | 6 | "Maps of cities with names" | | 7 | "Silas Blackwood caught her eye," | | 8 | "The slight limp in his" | | 9 | "He was polishing a highball" | | 10 | "He said nothing, waiting for" | | 11 | "The voice was familiar, a" | | 12 | "A woman stood near the" | | 13 | "She had the same rounded" | | 14 | "She, Aurora to the courts" | | 15 | "Her straight, shoulder-length black hair" | | 16 | "She was wearing her delivery" | | 17 | "Rory managed, her voice steadier" | | 18 | "Eva offered a smile that" | | 19 | "She moved through the crowded" |
| | ratio | 0.877 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 65 | | matches | | 0 | "Now, however, it felt less" | | 1 | "Now she navigated the shadows" |
| | ratio | 0.031 | |
| 66.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 5 | | matches | | 0 | "Maps of cities with names Rory barely recognized rubbed shoulders with candid shots of strangers drinking in dim light, capturing a world that felt both dangero…" | | 1 | "She had the same rounded chin, the same soft, warm eyes Rory had known growing up, but the face belonged to a woman who had been polished by privilege and time." | | 2 | "She was still the cool-headed girl who could untangle a knotted line with surgical precision, but Eva's presence stripped away the armor of her new life." | | 3 | "She liked Silas, who asked no questions about her past and never judged her for it." | | 4 | "Rory watched her go, watching the way the woman moved through the world without checking for traps or reading the room for threats." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "Rory managed, her voice steadier than she felt" |
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| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Rory lied (lie)" | | 1 | "Eva murmured (murmur)" |
| | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.286 | | effectiveRatio | 0.2 | |