| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1493 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "lightly" | | 1 | "really" | | 2 | "suddenly" |
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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) | |
| 29.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1493 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "scanned" | | 1 | "glinting" | | 2 | "weight" | | 3 | "unspoken" | | 4 | "silence" | | 5 | "tracing" | | 6 | "traced" | | 7 | "clandestine" | | 8 | "coded" | | 9 | "measured" | | 10 | "testament" | | 11 | "familiar" | | 12 | "footsteps" |
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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 | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1493 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "You could have called, she said, the words slipping out before she could cage them." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1493 | | uniqueNames | 16 | | maxNameDensity | 0.4 | | worstName | "You" | | maxWindowNameDensity | 1.5 | | worstWindowName | "You" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Silas | 5 | | Evan | 2 | | Yu-Fei | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | Prague | 3 | | London | 2 | | Cardiff | 1 | | Brendan | 1 | | Jennifer | 1 | | Rory | 3 | | You | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Evan" | | 4 | "Yu-Fei" | | 5 | "Empress" | | 6 | "Blackwood" | | 7 | "Brendan" | | 8 | "Jennifer" | | 9 | "Rory" | | 10 | "You" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 65.73% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like she’d aged decades" | | 1 | "felt like a chasm, bridged only by the" | | 2 | "felt like a shield now felt like a weig" | | 3 | "felt like a weight" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.67 | | wordCount | 1493 | | matches | | 0 | "Not since the night she fled Evan’s apartment with nothing but a duffel bag" |
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| 94.72% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 139 | | matches | | 0 | "headed, that she" | | 1 | "seen that silence" | | 2 | "think that room" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 57.42 | | std | 49.37 | | cv | 0.86 | | sampleLengths | | 0 | 205 | | 1 | 13 | | 2 | 152 | | 3 | 1 | | 4 | 68 | | 5 | 36 | | 6 | 47 | | 7 | 27 | | 8 | 23 | | 9 | 72 | | 10 | 15 | | 11 | 43 | | 12 | 95 | | 13 | 32 | | 14 | 35 | | 15 | 35 | | 16 | 32 | | 17 | 56 | | 18 | 93 | | 19 | 56 | | 20 | 17 | | 21 | 38 | | 22 | 130 | | 23 | 6 | | 24 | 27 | | 25 | 139 |
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| 97.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 139 | | matches | | 0 | "been fooled" | | 1 | "been erased" | | 2 | "was meant" |
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| 80.10% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 278 | | matches | | 0 | "was thinking" | | 1 | "was surviving" | | 2 | "was staring" | | 3 | "was polishing" | | 4 | "were looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 139 | | ratio | 0.007 | | matches | | 0 | "MI6 hadn’t just left him a scar on his knee; it had taught him how to disappear into a room and how to carry the weight of a dozen unspoken missions in his silence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1504 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.03125 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.00997340425531915 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 10.74 | | std | 8.59 | | cv | 0.8 | | sampleLengths | | 0 | 21 | | 1 | 18 | | 2 | 17 | | 3 | 21 | | 4 | 26 | | 5 | 12 | | 6 | 19 | | 7 | 29 | | 8 | 16 | | 9 | 17 | | 10 | 9 | | 11 | 13 | | 12 | 3 | | 13 | 2 | | 14 | 17 | | 15 | 24 | | 16 | 12 | | 17 | 28 | | 18 | 21 | | 19 | 34 | | 20 | 6 | | 21 | 5 | | 22 | 1 | | 23 | 12 | | 24 | 6 | | 25 | 21 | | 26 | 29 | | 27 | 1 | | 28 | 8 | | 29 | 21 | | 30 | 6 | | 31 | 3 | | 32 | 15 | | 33 | 10 | | 34 | 4 | | 35 | 15 | | 36 | 12 | | 37 | 7 | | 38 | 5 | | 39 | 3 | | 40 | 3 | | 41 | 10 | | 42 | 10 | | 43 | 9 | | 44 | 4 | | 45 | 13 | | 46 | 19 | | 47 | 8 | | 48 | 19 | | 49 | 15 |
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| 38.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.28776978417266186 | | totalSentences | 139 | | uniqueOpeners | 40 | |
| 27.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 123 | | matches | | 0 | "Instead, he raised his glass" |
| | ratio | 0.008 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 74 | | totalSentences | 123 | | matches | | 0 | "She shook rain from her" | | 1 | "She hadn’t been down to" | | 2 | "She was twenty-five now, though" | | 3 | "She told herself she was" | | 4 | "She moved toward the bar," | | 5 | "He was there." | | 6 | "He hadn’t changed so much" | | 7 | "His neatly trimmed beard matched" | | 8 | "He was staring into his" | | 9 | "She had seen that silence" | | 10 | "She had worn it herself." | | 11 | "His voice left his lips" | | 12 | "She hadn’t heard it in" | | 13 | "He’d stopped calling her that" | | 14 | "Her voice came out rougher" | | 15 | "She moved toward the table," | | 16 | "I didn’t think you’d be" | | 17 | "He didn’t stand." | | 18 | "I own the place." | | 19 | "She pulled out the chair" |
| | ratio | 0.602 | |
| 57.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 99 | | totalSentences | 123 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora pushed through the heavy" | | 2 | "The Raven’s Nest smelled of" | | 3 | "She shook rain from her" | | 4 | "The walls were a collage" | | 5 | "She hadn’t been down to" | | 6 | "She was twenty-five now, though" | | 7 | "She told herself she was" | | 8 | "She moved toward the bar," | | 9 | "He was there." | | 10 | "He hadn’t changed so much" | | 11 | "His neatly trimmed beard matched" | | 12 | "He was staring into his" | | 13 | "The slight limp in his" | | 14 | "MI6 hadn’t just left him" | | 15 | "She had seen that silence" | | 16 | "She had worn it herself." | | 17 | "His voice left his lips" | | 18 | "She hadn’t heard it in" | | 19 | "He’d stopped calling her that" |
| | ratio | 0.805 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 123 | | matches | | 0 | "Now, standing in the rain-slicked" | | 1 | "Though it feels less like" | | 2 | "Before you vanished into the" | | 3 | "Now she understood it was" | | 4 | "Before you learned to make" | | 5 | "If the woman sitting across" |
| | ratio | 0.049 | |
| 86.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 5 | | matches | | 0 | "Not since she’d taken the flat above the bar and started delivering takeout for Yu-Fei’s Golden Empress, surviving on boxed meals and part-time shifts that blur…" | | 1 | "He was staring into his drink as if it might offer answers, his right hand resting lightly on the table, a silver signet ring glinting against his skin." | | 2 | "She’d chosen silence, armor, the kind of quick-thinking detachment that kept Evan’s memory from clawing back into her ribs." | | 3 | "He’d spent his life reading the spaces between words, tracing the fractures in a lie before it fully formed." | | 4 | "She caught her reflection in the dark window opposite: bright blue eyes hollowed out by exhaustion, shoulders hunched against a weight that wasn’t there." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |