| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1500 | | totalAiIsmAdverbs | 2 | | 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) | |
| 53.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1500 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "measured" | | 2 | "weight" | | 3 | "tracing" | | 4 | "treacherous" | | 5 | "scanned" | | 6 | "gloom" | | 7 | "calculated" | | 8 | "unreadable" | | 9 | "perfect" | | 10 | "silence" | | 11 | "flickered" | | 12 | "resolve" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1499 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1085 | | uniqueNames | 16 | | maxNameDensity | 1.57 | | worstName | "Gwyn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Gwyn" | | discoveredNames | | October | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Silas | 7 | | Blackwood | 1 | | Carter | 1 | | Golden | 2 | | Empress | 2 | | Soho | 2 | | Savile | 1 | | Row | 1 | | Thomas | 2 | | Rory | 14 | | Cardiff | 1 | | Gwyn | 17 |
| | persons | | 0 | "Nest" | | 1 | "Silas" | | 2 | "Blackwood" | | 3 | "Carter" | | 4 | "Row" | | 5 | "Thomas" | | 6 | "Rory" | | 7 | "Gwyn" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Golden" | | 3 | "Soho" | | 4 | "Cardiff" |
| | globalScore | 0.717 | | windowScore | 0.5 | |
| 48.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 3 | | matches | | 0 | "sounded like a laugh, though his face rema" | | 1 | "sounded like snapping dry tinder" | | 2 | "as if searching for one final argument, one piece of legal jargon that could resolve the division between who they used to be and what they had become" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.667 | | wordCount | 1499 | | matches | | 0 | "not flinch, but her grip on her glass tightened until her knuckles turned th" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 20.82 | | std | 21.62 | | cv | 1.038 | | sampleLengths | | 0 | 94 | | 1 | 80 | | 2 | 9 | | 3 | 68 | | 4 | 18 | | 5 | 1 | | 6 | 15 | | 7 | 1 | | 8 | 71 | | 9 | 14 | | 10 | 1 | | 11 | 2 | | 12 | 8 | | 13 | 2 | | 14 | 9 | | 15 | 33 | | 16 | 27 | | 17 | 6 | | 18 | 12 | | 19 | 6 | | 20 | 21 | | 21 | 4 | | 22 | 27 | | 23 | 12 | | 24 | 5 | | 25 | 2 | | 26 | 1 | | 27 | 47 | | 28 | 6 | | 29 | 15 | | 30 | 20 | | 31 | 2 | | 32 | 8 | | 33 | 4 | | 34 | 21 | | 35 | 24 | | 36 | 3 | | 37 | 5 | | 38 | 16 | | 39 | 40 | | 40 | 4 | | 41 | 72 | | 42 | 14 | | 43 | 5 | | 44 | 29 | | 45 | 3 | | 46 | 19 | | 47 | 25 | | 48 | 8 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 81 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 168 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1092 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small, crescent-shaped scar" | | 1 | "sleek, matte-black business" |
| | adverbCount | 25 | | adverbRatio | 0.022893772893772892 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00641025641025641 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 12.7 | | std | 9.03 | | cv | 0.711 | | sampleLengths | | 0 | 22 | | 1 | 12 | | 2 | 12 | | 3 | 13 | | 4 | 15 | | 5 | 20 | | 6 | 16 | | 7 | 18 | | 8 | 24 | | 9 | 22 | | 10 | 9 | | 11 | 8 | | 12 | 6 | | 13 | 15 | | 14 | 19 | | 15 | 20 | | 16 | 3 | | 17 | 15 | | 18 | 1 | | 19 | 15 | | 20 | 1 | | 21 | 16 | | 22 | 28 | | 23 | 9 | | 24 | 18 | | 25 | 14 | | 26 | 1 | | 27 | 2 | | 28 | 8 | | 29 | 2 | | 30 | 9 | | 31 | 20 | | 32 | 13 | | 33 | 21 | | 34 | 6 | | 35 | 6 | | 36 | 12 | | 37 | 6 | | 38 | 21 | | 39 | 4 | | 40 | 15 | | 41 | 12 | | 42 | 12 | | 43 | 5 | | 44 | 2 | | 45 | 1 | | 46 | 10 | | 47 | 8 | | 48 | 14 | | 49 | 6 |
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| 44.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3135593220338983 | | totalSentences | 118 | | uniqueOpeners | 37 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Then in half again, until" |
| | ratio | 0.012 | |
| 76.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 81 | | matches | | 0 | "His silver signet ring caught" | | 1 | "He favored his right leg," | | 2 | "Her straight, shoulder-length black hair" | | 3 | "She had three more deliveries" | | 4 | "He wore a charcoal wool" | | 5 | "His leather shoes clicked sharply" | | 6 | "He scanned the room, his" | | 7 | "He let out a breath" | | 8 | "He looked her up and" | | 9 | "He looked exactly as she" | | 10 | "He placed his leather briefcase" | | 11 | "He set a clean coaster" | | 12 | "It was softer now, coated" | | 13 | "She picked up her glass" | | 14 | "Her left sleeve slipped back," | | 15 | "His silver signet ring tapped" | | 16 | "He lingered for a second," | | 17 | "He was offering a silent" | | 18 | "He watched the carbonation rise." | | 19 | "She turned her head fully" |
| | ratio | 0.358 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 81 | | matches | | 0 | "The green neon sign outside" | | 1 | "His silver signet ring caught" | | 2 | "He favored his right leg," | | 3 | "Rory Carter sat on the" | | 4 | "Her straight, shoulder-length black hair" | | 5 | "She had three more deliveries" | | 6 | "The brass bell above the" | | 7 | "A man stepped inside, shaking" | | 8 | "Droplets scattered across the entryway" | | 9 | "He wore a charcoal wool" | | 10 | "His leather shoes clicked sharply" | | 11 | "He scanned the room, his" | | 12 | "The man froze." | | 13 | "He let out a breath" | | 14 | "Rory kept her gaze on" | | 15 | "Gwyn Thomas took three steps" | | 16 | "He looked her up and" | | 17 | "He looked exactly as she" | | 18 | "The soft edges of his" | | 19 | "Gwyn gestured to the empty" |
| | ratio | 0.926 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "He favored his right leg, leaning his weight away from the old knee injury that gave him his trademark hitch." | | 1 | "He wore a charcoal wool overcoat that screamed Savile Row, draped over a tailored suit." | | 2 | "Silas limped over, his hazel eyes assessing the newcomer with the cool vigilance of a man who spent decades reading rooms." | | 3 | "The green neon light outside flickered, casting a brief shadow across Gwyn's face that made him look tired, compromised, and old." | | 4 | "Gwyn lingered for a second, his mouth twitching as if searching for one final argument, one piece of legal jargon that could resolve the division between who th…" | | 5 | "His leather shoes clicked against the floorboards, a retreating rhythm that faded as the brass bell chimed once more, letting in a gust of cold, wet Soho air be…" |
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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 | |