| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1676 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 82.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1676 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "gloom" | | 2 | "eyebrow" | | 3 | "flicked" | | 4 | "unspoken" | | 5 | "familiar" |
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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 | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 99 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 8 | | totalWords | 1676 | | ratio | 0.005 | | matches | | 0 | "The Spy Who Came in from the Cold" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1656 | | uniqueNames | 34 | | maxNameDensity | 0.66 | | worstName | "Elara" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | Tuesday | 1 | | Soho | 1 | | Raven | 1 | | Nest | 3 | | Frith | 1 | | Street | 1 | | Billie | 1 | | Holiday | 2 | | Blackwood | 1 | | Berlin | 1 | | Czech | 1 | | Voss | 1 | | Prague | 5 | | London | 4 | | New | 1 | | Zealand | 1 | | University | 1 | | Otago | 1 | | Mike | 1 | | TikTok | 1 | | Elara | 11 | | Spy | 1 | | Came | 1 | | Lila | 1 | | Cardiff | 1 | | Severn | 1 | | Silas | 7 | | Aurora | 1 | | Golden | 1 | | Empress | 1 | | Si | 2 | | Pacific | 1 | | Rory | 1 | | You | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Billie" | | 3 | "Holiday" | | 4 | "Blackwood" | | 5 | "Otago" | | 6 | "Mike" | | 7 | "Elara" | | 8 | "Came" | | 9 | "Lila" | | 10 | "Silas" | | 11 | "Aurora" | | 12 | "Rory" | | 13 | "You" |
| | places | | 0 | "Soho" | | 1 | "Frith" | | 2 | "Street" | | 3 | "Berlin" | | 4 | "Prague" | | 5 | "London" | | 6 | "New" | | 7 | "Zealand" | | 8 | "Cardiff" | | 9 | "Severn" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.597 | | wordCount | 1676 | | matches | | |
| 99.33% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 99 | | matches | | 0 | "thought that limp" | | 1 | "let that happen" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 79.81 | | std | 49.1 | | cv | 0.615 | | sampleLengths | | 0 | 188 | | 1 | 149 | | 2 | 56 | | 3 | 46 | | 4 | 23 | | 5 | 47 | | 6 | 29 | | 7 | 73 | | 8 | 168 | | 9 | 70 | | 10 | 40 | | 11 | 71 | | 12 | 128 | | 13 | 74 | | 14 | 90 | | 15 | 112 | | 16 | 18 | | 17 | 91 | | 18 | 33 | | 19 | 29 | | 20 | 141 |
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| 87.54% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 99 | | matches | | 0 | "was, caught" | | 1 | "were twisted" | | 2 | "was meant" | | 3 | "was bugged" | | 4 | "was told" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 272 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1668 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.02697841726618705 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.00539568345323741 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 16.93 | | std | 13.14 | | cv | 0.776 | | sampleLengths | | 0 | 3 | | 1 | 6 | | 2 | 21 | | 3 | 43 | | 4 | 33 | | 5 | 44 | | 6 | 38 | | 7 | 14 | | 8 | 31 | | 9 | 41 | | 10 | 27 | | 11 | 36 | | 12 | 2 | | 13 | 18 | | 14 | 36 | | 15 | 19 | | 16 | 27 | | 17 | 1 | | 18 | 22 | | 19 | 19 | | 20 | 28 | | 21 | 1 | | 22 | 7 | | 23 | 21 | | 24 | 23 | | 25 | 4 | | 26 | 6 | | 27 | 6 | | 28 | 9 | | 29 | 9 | | 30 | 2 | | 31 | 5 | | 32 | 9 | | 33 | 16 | | 34 | 36 | | 35 | 35 | | 36 | 35 | | 37 | 46 | | 38 | 42 | | 39 | 28 | | 40 | 10 | | 41 | 8 | | 42 | 4 | | 43 | 18 | | 44 | 2 | | 45 | 18 | | 46 | 11 | | 47 | 10 | | 48 | 17 | | 49 | 13 |
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| 41.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.24242424242424243 | | totalSentences | 99 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 92 | | matches | | 0 | "It’s nine p.m." | | 1 | "His auburn hair, streaked through" | | 2 | "He set the bourbon down" | | 3 | "His silver signet ring, worn" | | 4 | "Her hair, once bleach-blonde and" | | 5 | "She carried a canvas tote" | | 6 | "His partner for ten years," | | 7 | "They hadn’t spoken in nineteen" | | 8 | "She crossed the room, boots" | | 9 | "Her gaze flicked over the" | | 10 | "I thought that limp was" | | 11 | "He laughed, the sound rough," | | 12 | "He grabbed two clean rocks" | | 13 | "I thought you moved to" | | 14 | "She snorted, slipping off her" | | 15 | "I came to scatter Mike’s" | | 16 | "He always wanted to see" | | 17 | "I saw a TikTok of" | | 18 | "I had to stop in." | | 19 | "He poured two fingers of" |
| | ratio | 0.565 | |
| 3.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 92 | | matches | | 0 | "It’s nine p.m." | | 1 | "The Raven’s Nest’s green neon" | | 2 | "Silas Blackwood wiped a ring" | | 3 | "His auburn hair, streaked through" | | 4 | "The brass bell strung above" | | 5 | "He set the bourbon down" | | 6 | "His silver signet ring, worn" | | 7 | "The woman pushed wire-rimmed glasses" | | 8 | "Her hair, once bleach-blonde and" | | 9 | "She carried a canvas tote" | | 10 | "His partner for ten years," | | 11 | "They hadn’t spoken in nineteen" | | 12 | "She crossed the room, boots" | | 13 | "Her gaze flicked over the" | | 14 | "I thought that limp was" | | 15 | "He laughed, the sound rough," | | 16 | "He grabbed two clean rocks" | | 17 | "The hell are you doing" | | 18 | "I thought you moved to" | | 19 | "She snorted, slipping off her" |
| | ratio | 0.913 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 92 | | matches | | 0 | "If I didn’t pull you" | | 1 | "If you’d known, you’d have" |
| | ratio | 0.022 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 11 | | matches | | 0 | "The brass bell strung above the front door jingled, and cold rain wind curled in around the woman who stepped over the threshold, shaking water off a frayed woo…" | | 1 | "They hadn’t spoken in nineteen years, not since the Prague op that ended his career, left him with a permanent limp and a grudge that had settled in his bones a…" | | 2 | "I thought you moved to New Zealand to raise sheep and ignore every MI6 pension memo that landed in your inbox." | | 3 | "The regulars in the back filtered out one by one, pulling their coats tight against the rain, until the only sounds in the bar were the rain lashing the windows…" | | 4 | "He pulled the copy of *The Spy Who Came in from the Cold* that served as the lever, and the bookshelf swung open, revealing a small room with a wooden table and…" | | 5 | "The air thickened, the unspoken thing that had sat between them for almost two decades taking up more space than the two of them in the small room." | | 6 | "The memory of his sister showing up at his hospital room a week after the op, her hands shaking as she told him someone had left a dead cat on her porch, floode…" | | 7 | "Silas yelled back that they were fine, and she waved, heading up the stairs to her flat above the bar, the thud of her steps fading until it was just the two of…" | | 8 | "That kid, the girl who’d run to London from an abusive ex, the quick-witted, sharp-tongued delivery driver he’d taken under his wing, was part of the life he’d …" | | 9 | "They sat there for another two hours, drinking the last of the single malt, trading stories of the lives they’d built apart, the years that had stretched betwee…" | | 10 | "Elara talked about her grandkids, the sheep that kept escaping their pasture, the small cottage she’d bought on a cliff overlooking the Pacific." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |