| 78.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 2 | | adverbTags | | 0 | "She stepped around [around]" | | 1 | "Thomas said suddenly [suddenly]" |
| | dialogueSentences | 33 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.133 | | effectiveRatio | 0.121 | |
| 87.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1569 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "completely" | | 2 | "really" | | 3 | "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) | |
| 33.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1569 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "pulsed" | | 3 | "calculated" | | 4 | "rhythmic" | | 5 | "measured" | | 6 | "sentinel" | | 7 | "glinting" | | 8 | "electric" | | 9 | "reminder" | | 10 | "unspoken" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes widened" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 130 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 130 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1569 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1372 | | uniqueNames | 10 | | maxNameDensity | 0.95 | | worstName | "Thomas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Thomas" | | discoveredNames | | Raven | 3 | | Nest | 4 | | Silas | 7 | | Spymaster | 1 | | Thomas | 13 | | Cardiff | 2 | | Evan | 1 | | Rory | 10 | | Prague | 1 | | Soho | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Thomas" | | 4 | "Evan" | | 5 | "Rory" |
| | places | | 0 | "Spymaster" | | 1 | "Cardiff" | | 2 | "Prague" | | 3 | "Soho" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 93.82% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 2 | | matches | | 0 | "photographs that seemed to watch the patrons with detached, historical eyes" | | 1 | "quite recognition of the tenant" |
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| 72.53% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.275 | | wordCount | 1569 | | matches | | 0 | "Not just the mark on the skin, but the weight of the decisions" | | 1 | "not by a fight, but by the quiet, terrifying necessity of leaving a room" |
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| 99.10% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 148 | | matches | | 0 | "knew that profile" | | 1 | "hiding that scar" | | 2 | "knew that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 38.27 | | std | 25.68 | | cv | 0.671 | | sampleLengths | | 0 | 113 | | 1 | 59 | | 2 | 96 | | 3 | 29 | | 4 | 53 | | 5 | 39 | | 6 | 79 | | 7 | 45 | | 8 | 42 | | 9 | 23 | | 10 | 15 | | 11 | 63 | | 12 | 23 | | 13 | 22 | | 14 | 22 | | 15 | 53 | | 16 | 2 | | 17 | 6 | | 18 | 38 | | 19 | 33 | | 20 | 40 | | 21 | 15 | | 22 | 16 | | 23 | 22 | | 24 | 76 | | 25 | 10 | | 26 | 13 | | 27 | 7 | | 28 | 48 | | 29 | 12 | | 30 | 8 | | 31 | 35 | | 32 | 58 | | 33 | 22 | | 34 | 17 | | 35 | 27 | | 36 | 57 | | 37 | 50 | | 38 | 37 | | 39 | 66 | | 40 | 78 |
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| 97.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 130 | | matches | | 0 | "was clouded" | | 1 | "been severed" | | 2 | "were hidden" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 230 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 148 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1375 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.027636363636363636 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.010181818181818183 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 10.6 | | std | 6.97 | | cv | 0.657 | | sampleLengths | | 0 | 25 | | 1 | 11 | | 2 | 25 | | 3 | 12 | | 4 | 17 | | 5 | 23 | | 6 | 9 | | 7 | 18 | | 8 | 32 | | 9 | 16 | | 10 | 14 | | 11 | 11 | | 12 | 23 | | 13 | 5 | | 14 | 20 | | 15 | 7 | | 16 | 8 | | 17 | 21 | | 18 | 5 | | 19 | 8 | | 20 | 4 | | 21 | 21 | | 22 | 3 | | 23 | 3 | | 24 | 9 | | 25 | 6 | | 26 | 12 | | 27 | 17 | | 28 | 4 | | 29 | 16 | | 30 | 18 | | 31 | 16 | | 32 | 12 | | 33 | 17 | | 34 | 5 | | 35 | 6 | | 36 | 2 | | 37 | 32 | | 38 | 1 | | 39 | 7 | | 40 | 12 | | 41 | 22 | | 42 | 8 | | 43 | 10 | | 44 | 3 | | 45 | 2 | | 46 | 8 | | 47 | 6 | | 48 | 1 | | 49 | 10 |
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| 35.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.22972972972972974 | | totalSentences | 148 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 68 | | totalSentences | 118 | | matches | | 0 | "She was still in her" | | 1 | "Her shoulders ached from the" | | 2 | "She stepped inside and blinked" | | 3 | "She headed for the counter," | | 4 | "He looked exactly as he" | | 5 | "It belonged to the Spymaster." | | 6 | "He looked up, and for" | | 7 | "It was a deeper sort" | | 8 | "It was the man sitting" | | 9 | "She knew that profile." | | 10 | "It was Thomas." | | 11 | "Her Cardiff friend." | | 12 | "She hadn't intended to come" | | 13 | "She'd come to hide, to" | | 14 | "She had spent months hiding" | | 15 | "She forced herself to move." | | 16 | "His eyes widened, the polite" | | 17 | "He pushed his chair back" | | 18 | "He wore a suit, tailored" | | 19 | "His voice was the same," |
| | ratio | 0.576 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 118 | | matches | | 0 | "The neon green sign of" | | 1 | "Aurora pushed open the door," | | 2 | "She was still in her" | | 3 | "Her shoulders ached from the" | | 4 | "She stepped inside and blinked" | | 5 | "The bar was sparse, populated" | | 6 | "She headed for the counter," | | 7 | "Silas stood behind the wood," | | 8 | "He looked exactly as he" | | 9 | "The grey-streaked auburn hair was" | | 10 | "It belonged to the Spymaster." | | 11 | "He looked up, and for" | | 12 | "It was a deeper sort" | | 13 | "It was the man sitting" | | 14 | "Rory felt the air shift." | | 15 | "The hum of the neon" | | 16 | "She knew that profile." | | 17 | "The sharp jawline, the way" | | 18 | "It was Thomas." | | 19 | "Her Cardiff friend." |
| | ratio | 0.924 | |
| 42.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 118 | | matches | | 0 | "Now, in the dim light," |
| | ratio | 0.008 | |
| 60.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 7 | | matches | | 0 | "Old maps draped the walls like tapestries, their edges yellowed and brittle, alongside black-and-white photographs that seemed to watch the patrons with detache…" | | 1 | "The bar was sparse, populated mostly by the kind of night shift regulars who preferred silence to conversation." | | 2 | "His eyes widened, the polite smile freezing into something harder, something that calculated the distance between the woman in the delivery jacket and the girl …" | | 3 | "She saw the lines around his mouth that weren't there three years ago." | | 4 | "It was a heavy thing, the history of a friendship that had been severed not by a fight, but by the quiet, terrifying necessity of leaving a room that was on fir…" | | 5 | "The neon sign buzzed louder now, a final electric reminder of the city that had swallowed her whole." | | 6 | "The Raven's Nest stood behind her, a dark shape against the Soho streetlights, a place where old things were hidden, and where the only thing that mattered was …" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, her voice rough" | | 1 | "She stepped around, the chair scraping against the wood" | | 2 | "she said, her voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "she corrected (correct)" |
| | dialogueSentences | 33 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.1 | | effectiveRatio | 0.061 | |