| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1073 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "carefully" | | 2 | "slightly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 39.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1073 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "echoing" | | 1 | "silk" | | 2 | "warmth" | | 3 | "flickered" | | 4 | "flicker" | | 5 | "shattered" | | 6 | "standard" | | 7 | "pulse" | | 8 | "glinting" | | 9 | "flicked" |
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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 | 127 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 66.29% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 20 | | markdownWords | 71 | | totalWords | 1062 | | ratio | 0.067 | | matches | | 0 | "Sloppy. Or deliberate." | | 1 | "Not a robbery." | | 2 | "Eva Kowalski" | | 3 | "Fresh." | | 4 | "Directed" | | 5 | "Willingly." | | 6 | "professional." | | 7 | "Rat. Or worse." | | 8 | "You’re late. The girl’s already here. Don’t let her leave with the Compass." | | 9 | "Girl." | | 10 | "Compass" | | 11 | "GHB derivatives. Cl 340" | | 12 | "Truth serum base." | | 13 | "Aurora saw something she shouldn’t have. Docility chemicals. The Veil Market trades in more than antiques—they traffic in influence." | | 14 | "vague-out" | | 15 | "nervous habit" | | 16 | "Someone’s there." | | 17 | "Embedded in her skin." | | 18 | "still spinning" | | 19 | "The Veil & Thorn." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 789 | | uniqueNames | 17 | | maxNameDensity | 1.77 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Morris | 2 | | Personal | 1 | | Callum | 10 | | Davies | 1 | | Union | 1 | | Jack | 1 | | Quinn | 14 | | Bone | 1 | | Eva | 9 | | Veil | 3 | | Compass | 3 | | Market | 3 | | Standard | 1 | | Camden | 2 | | High | 1 | | Street | 1 | | Quint | 1 |
| | persons | | 0 | "Morris" | | 1 | "Callum" | | 2 | "Davies" | | 3 | "Union" | | 4 | "Jack" | | 5 | "Quinn" | | 6 | "Eva" | | 7 | "Compass" | | 8 | "Market" | | 9 | "Quint" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.613 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1062 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 17.41 | | std | 13.73 | | cv | 0.789 | | sampleLengths | | 0 | 26 | | 1 | 33 | | 2 | 5 | | 3 | 61 | | 4 | 15 | | 5 | 23 | | 6 | 45 | | 7 | 30 | | 8 | 9 | | 9 | 6 | | 10 | 51 | | 11 | 7 | | 12 | 41 | | 13 | 6 | | 14 | 31 | | 15 | 14 | | 16 | 23 | | 17 | 9 | | 18 | 13 | | 19 | 25 | | 20 | 11 | | 21 | 9 | | 22 | 38 | | 23 | 11 | | 24 | 41 | | 25 | 26 | | 26 | 9 | | 27 | 6 | | 28 | 2 | | 29 | 5 | | 30 | 26 | | 31 | 9 | | 32 | 12 | | 33 | 32 | | 34 | 9 | | 35 | 12 | | 36 | 21 | | 37 | 41 | | 38 | 37 | | 39 | 10 | | 40 | 8 | | 41 | 26 | | 42 | 7 | | 43 | 30 | | 44 | 22 | | 45 | 3 | | 46 | 5 | | 47 | 18 | | 48 | 13 | | 49 | 5 |
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| 96.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 127 | | matches | | 0 | "been prised" | | 1 | "was littered" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 153 | | ratio | 0.085 | | matches | | 0 | "Cracked ceramic tiles glistened under her torchlight—same place, different corpse." | | 1 | "Inside, miniature portraits—both smiling." | | 2 | "She knew that voice—the dry, resigned warmth that had covered her back for eight years." | | 3 | "The tunnel was littered with old graffiti—names, dates, sigils." | | 4 | "A flicker of movement—something small, skittering through the shadows." | | 5 | "Quinn stepped past the body, her boots crunching on shattered glass—old test tubes, shattered." | | 6 | "Cl 340*—recreational?" | | 7 | "The Veil Market trades in more than antiques—they traffic in influence.*" | | 8 | "The exit opened into a narrow service corridor, walls lined with peeling posters—lost pets, gigs, one for a philosophy lecture at UCL." | | 9 | "A leather satchel hung from her shoulder—bulging, like books inside." | | 10 | "She tucked a curl behind her left ear—*nervous habit*." | | 11 | "Quinn saw it—the same sigil Quint had seen in the tunnel." | | 12 | "And the compass—*still spinning*—lay on the ground near Quinn’s boot." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 801 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.02746566791510612 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00749063670411985 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 6.94 | | std | 5.43 | | cv | 0.782 | | sampleLengths | | 0 | 16 | | 1 | 10 | | 2 | 14 | | 3 | 16 | | 4 | 1 | | 5 | 2 | | 6 | 5 | | 7 | 22 | | 8 | 4 | | 9 | 2 | | 10 | 6 | | 11 | 5 | | 12 | 4 | | 13 | 7 | | 14 | 11 | | 15 | 6 | | 16 | 9 | | 17 | 3 | | 18 | 15 | | 19 | 3 | | 20 | 2 | | 21 | 20 | | 22 | 2 | | 23 | 2 | | 24 | 21 | | 25 | 10 | | 26 | 20 | | 27 | 5 | | 28 | 4 | | 29 | 6 | | 30 | 28 | | 31 | 1 | | 32 | 3 | | 33 | 5 | | 34 | 9 | | 35 | 4 | | 36 | 1 | | 37 | 2 | | 38 | 5 | | 39 | 14 | | 40 | 6 | | 41 | 2 | | 42 | 2 | | 43 | 11 | | 44 | 6 | | 45 | 3 | | 46 | 3 | | 47 | 3 | | 48 | 28 | | 49 | 4 |
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| 71.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.43137254901960786 | | totalSentences | 153 | | uniqueOpeners | 66 | |
| 34.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 98 | | matches | | | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 98 | | matches | | 0 | "she called out, the word" | | 1 | "She knelt, fingers brushing cold" | | 2 | "She slipped it into an" | | 3 | "She knew that voice—the dry," | | 4 | "She stepped closer, her boot" | | 5 | "She crouched, using her pen" | | 6 | "She ignored him, turning her" | | 7 | "Her watch buzzed." | | 8 | "She turned to Callum." | | 9 | "He shook his head." | | 10 | "She watched his face carefully" | | 11 | "She knelt, sifting through the" | | 12 | "She looked up at Callum." | | 13 | "She stood abruptly." | | 14 | "Her voice was low, controlled" | | 15 | "She was already walking toward" | | 16 | "She clutched something small in" | | 17 | "Her freckled face tensed." | | 18 | "She tucked a curl behind" | | 19 | "Her eyes flicked to the" |
| | ratio | 0.214 | |
| 56.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 98 | | matches | | 0 | "Quinn ducked under the yellow" | | 1 | "she called out, the word" | | 2 | "The nameplates near the ticket" | | 3 | "DS Morris would’ve known why." | | 4 | "The torch beam stumbled over" | | 5 | "She knelt, fingers brushing cold" | | 6 | "A locket, its clasp broken." | | 7 | "She slipped it into an" | | 8 | "A laugh cut through the" | | 9 | "Quinn didn’t turn." | | 10 | "She knew that voice—the dry," | | 11 | "DS Callum Davies stood over" | | 12 | "Callum said, adjusting his glasses" | | 13 | "Quinn exhaled through her nose." | | 14 | "She stepped closer, her boot" | | 15 | "A finger bone." | | 16 | "Part of a larger skeleton?" | | 17 | "The tunnel was littered with" | | 18 | "She crouched, using her pen" | | 19 | "The young man’s face was" |
| | ratio | 0.806 | |
| 51.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 98 | | matches | | 0 | "Either he was clean, or" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "The same man who’d sealed Morris’s case file with a smile." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "she called out, the word echoing through the abandoned station like a challenge" | | 1 | "She stepped, her boot crunching on something brittle" | | 2 | "Quinn stepped, boots scuffing pavement" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 38 | | tagDensity | 0.105 | | leniency | 0.211 | | rawRatio | 0.25 | | effectiveRatio | 0.053 | |