| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Aurora said calmly [calmly]" |
| | dialogueSentences | 30 | | tagDensity | 0.567 | | leniency | 1 | | rawRatio | 0.059 | | effectiveRatio | 0.059 | |
| 77.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1337 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "suddenly" | | 1 | "slowly" | | 2 | "very" | | 3 | "truly" | | 4 | "quickly" |
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| 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) | |
| 17.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1337 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "electric" | | 1 | "could feel" | | 2 | "measured" | | 3 | "rhythmic" | | 4 | "sanctuary" | | 5 | "gloom" | | 6 | "scanned" | | 7 | "pulse" | | 8 | "whisper" | | 9 | "silence" | | 10 | "echoing" | | 11 | "lilt" | | 12 | "weight" | | 13 | "stomach" | | 14 | "oppressive" | | 15 | "tension" | | 16 | "unreadable" | | 17 | "flicker" | | 18 | "familiar" |
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| 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 | 0 | | narrationSentences | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1331 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 31.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1136 | | uniqueNames | 9 | | maxNameDensity | 2.38 | | worstName | "Aurora" | | maxWindowNameDensity | 4 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Aurora | 27 | | Soho | 2 | | Silas | 5 | | October | 1 | | Cardiff | 1 | | Eva | 20 | | Evan | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Evan" |
| | places | | | globalScore | 0.312 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a stranger wearing a friend's" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.751 | | wordCount | 1331 | | matches | | 0 | "not in miles, but in the way they viewed the world" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 35.03 | | std | 23.82 | | cv | 0.68 | | sampleLengths | | 0 | 81 | | 1 | 99 | | 2 | 18 | | 3 | 24 | | 4 | 44 | | 5 | 75 | | 6 | 18 | | 7 | 18 | | 8 | 53 | | 9 | 62 | | 10 | 22 | | 11 | 23 | | 12 | 35 | | 13 | 10 | | 14 | 26 | | 15 | 73 | | 16 | 13 | | 17 | 24 | | 18 | 8 | | 19 | 40 | | 20 | 11 | | 21 | 8 | | 22 | 90 | | 23 | 40 | | 24 | 35 | | 25 | 53 | | 26 | 19 | | 27 | 24 | | 28 | 60 | | 29 | 18 | | 30 | 36 | | 31 | 16 | | 32 | 32 | | 33 | 43 | | 34 | 18 | | 35 | 4 | | 36 | 35 | | 37 | 23 |
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| 91.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 79 | | matches | | 0 | "was draped" | | 1 | "was pulled" | | 2 | "was made" |
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| 85.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 175 | | matches | | 0 | "was still wearing" | | 1 | "weren't saying" | | 2 | "was watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 4 | | flaggedSentences | 8 | | totalSentences | 92 | | ratio | 0.087 | | matches | | 0 | "He didn't ask for details; he knew the architecture of her life well enough." | | 1 | "The silence between them wasn't empty; it was crowded with the ghosts of Cardiff, the echoing halls of the university, and the frantic, whispered phone calls in the dead of night three years ago." | | 2 | "Her voice had changed too—it was lower now, stripped of its childhood lilt, replaced by a curated professional cadence." | | 3 | "A shadow of something—pity, perhaps, or disappointment—crossed her features." | | 4 | "Eva stepped closer, the scent of expensive perfume—something floral and oppressive—cutting through the smell of the bar." | | 5 | "She saw the woman she had once loved like a sister, but she also saw the distance between them—a distance measured not in miles, but in the way they viewed the world." | | 6 | "Eva saw a tragedy in Aurora's delivery uniform; Aurora saw a mask in Eva's camel coat." | | 7 | "She realized that Eva didn't want to reconnect; she wanted to verify the wreckage." |
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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 | 33 | | adverbRatio | 0.028695652173913042 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.010434782608695653 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 14.47 | | std | 8.01 | | cv | 0.554 | | sampleLengths | | 0 | 29 | | 1 | 23 | | 2 | 29 | | 3 | 19 | | 4 | 31 | | 5 | 7 | | 6 | 16 | | 7 | 26 | | 8 | 18 | | 9 | 10 | | 10 | 14 | | 11 | 6 | | 12 | 14 | | 13 | 24 | | 14 | 27 | | 15 | 22 | | 16 | 26 | | 17 | 2 | | 18 | 16 | | 19 | 18 | | 20 | 3 | | 21 | 8 | | 22 | 8 | | 23 | 34 | | 24 | 13 | | 25 | 9 | | 26 | 28 | | 27 | 12 | | 28 | 3 | | 29 | 19 | | 30 | 8 | | 31 | 15 | | 32 | 11 | | 33 | 9 | | 34 | 15 | | 35 | 5 | | 36 | 5 | | 37 | 16 | | 38 | 10 | | 39 | 11 | | 40 | 1 | | 41 | 24 | | 42 | 20 | | 43 | 17 | | 44 | 13 | | 45 | 4 | | 46 | 3 | | 47 | 17 | | 48 | 8 | | 49 | 17 |
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| 40.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.31521739130434784 | | totalSentences | 92 | | uniqueOpeners | 29 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 68 | | matches | | 0 | "Then, the mask slid back" |
| | ratio | 0.015 | |
| 78.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 68 | | matches | | 0 | "It was a dim space," | | 1 | "She was still wearing her" | | 2 | "She watched Silas move behind" | | 3 | "He moved with a measured," | | 4 | "she replied, her voice flat" | | 5 | "He didn't ask for details;" | | 6 | "He was the one who" | | 7 | "She was draped in a" | | 8 | "Her blonde hair was pulled" | | 9 | "She turned slowly, her gaze" | | 10 | "She looked like a stranger" | | 11 | "Her voice had changed too—it" | | 12 | "She instinctively pulled her sleeve" | | 13 | "She thought of the textbooks" | | 14 | "She didn't smile." | | 15 | "She reached out as if" | | 16 | "They had both fled the" | | 17 | "He was watching them from" | | 18 | "He knew the value of" | | 19 | "She saw the woman she" |
| | ratio | 0.353 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 68 | | matches | | 0 | "The green neon sign of" | | 1 | "It was a dim space," | | 2 | "Aurora leaned against the brass" | | 3 | "She was still wearing her" | | 4 | "She watched Silas move behind" | | 5 | "He moved with a measured," | | 6 | "The silver signet ring on" | | 7 | "Silas asked, his voice a" | | 8 | "she replied, her voice flat" | | 9 | "Silas offered a thin, knowing" | | 10 | "He didn't ask for details;" | | 11 | "He was the one who" | | 12 | "The door groaned open, admitting" | | 13 | "She was draped in a" | | 14 | "Her blonde hair was pulled" | | 15 | "The glass of water in" | | 16 | "the word escaped Aurora as" | | 17 | "The woman stopped." | | 18 | "She turned slowly, her gaze" | | 19 | "The silence between them wasn't" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 36.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 7 | | matches | | 0 | "It was a dim space, the kind of place where the shadows felt intentional, clinging to the corners and the edges of the black-and-white photographs that lined th…" | | 1 | "She was still wearing her delivery gear, the fabric worn thin at the elbows, a smudge of grease across her forearm from a bike chain that had slipped two street…" | | 2 | "He was the one who provided the flat upstairs and the quiet sanctuary of the Nest, a man who understood the necessity of disappearing." | | 3 | "The door groaned open, admitting a gust of cold October air and a woman who looked entirely out of place in the subterranean gloom of the bar." | | 4 | "Her blonde hair was pulled back into a severe, architectural bun, and her eyes scanned the room with a clinical efficiency that made Aurora’s pulse spike." | | 5 | "The Eva Aurora had known was a girl of oversized sweaters and ink-stained fingers, a girl who laughed too loudly in the library and dreamed of social justice." | | 6 | "She reached out as if to touch Aurora’s arm, then hesitated, her hand hovering in the air before dropping back to her side." |
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| 36.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 3 | | matches | | 0 | "she replied, her voice flat but not unkind" | | 1 | "Eva replied, her eyes flicking down to Aurora’s delivery uniform" | | 2 | "Aurora said, her voice regaining its cool edge" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 30 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0.063 | | effectiveRatio | 0.063 | |