| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.298 | | leniency | 0.596 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1301 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "reluctantly" | | count | 1 |
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| | highlights | | |
| 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) | |
| 19.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1301 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "chill" | | 1 | "velvet" | | 2 | "methodical" | | 3 | "silk" | | 4 | "shimmered" | | 5 | "unreadable" | | 6 | "stark" | | 7 | "chaotic" | | 8 | "resonance" | | 9 | "aligned" | | 10 | "etched" | | 11 | "scanned" | | 12 | "tracing" | | 13 | "potential" | | 14 | "stomach" | | 15 | "calculated" | | 16 | "beacon" | | 17 | "gloom" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 97 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1298 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 863 | | uniqueNames | 18 | | maxNameDensity | 1.74 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Davies" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Town | 1 | | North | 1 | | Veil | 4 | | Market | 4 | | Davies | 9 | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 15 | | Seraphina | 3 | | Silas | 6 | | Compass | 1 | | Eva | 4 | | Kowalski | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tube" | | 2 | "Davies" | | 3 | "Harlow" | | 4 | "Seraphina" | | 5 | "Silas" | | 6 | "Compass" | | 7 | "Eva" | | 8 | "Kowalski" | | 9 | "Museum" |
| | places | | 0 | "Town" | | 1 | "North" | | 2 | "Market" | | 3 | "Camden" | | 4 | "High" | | 5 | "Street" | | 6 | "British" |
| | globalScore | 0.631 | | windowScore | 0.667 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 2 | | matches | | 0 | "something like dust motes and forgotten pray" | | 1 | "smelled like trouble even before the call" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1298 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 23.18 | | std | 17.26 | | cv | 0.745 | | sampleLengths | | 0 | 76 | | 1 | 35 | | 2 | 53 | | 3 | 37 | | 4 | 36 | | 5 | 8 | | 6 | 15 | | 7 | 4 | | 8 | 34 | | 9 | 4 | | 10 | 20 | | 11 | 58 | | 12 | 16 | | 13 | 14 | | 14 | 16 | | 15 | 29 | | 16 | 30 | | 17 | 17 | | 18 | 13 | | 19 | 28 | | 20 | 17 | | 21 | 13 | | 22 | 4 | | 23 | 18 | | 24 | 2 | | 25 | 20 | | 26 | 24 | | 27 | 8 | | 28 | 13 | | 29 | 53 | | 30 | 4 | | 31 | 15 | | 32 | 15 | | 33 | 17 | | 34 | 45 | | 35 | 4 | | 36 | 35 | | 37 | 4 | | 38 | 4 | | 39 | 13 | | 40 | 33 | | 41 | 6 | | 42 | 43 | | 43 | 4 | | 44 | 12 | | 45 | 45 | | 46 | 59 | | 47 | 14 | | 48 | 34 | | 49 | 23 |
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| 98.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 97 | | matches | | 0 | "was marked" | | 1 | "was meant" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 150 | | matches | | 0 | "were watching" | | 1 | "was clutching" |
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| 76.92% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 130 | | ratio | 0.023 | | matches | | 0 | "He was young, mid-twenties maybe, dressed in the sort of flamboyant clothes you saw on the fringes of Camden High Street—velvet jacket, too many rings." | | 1 | "A compass tuned to supernatural energy; the needle pointed towards rifts and portals." | | 2 | "The puncture wound, the surgical precision, the knowledge of the Veil – it all pointed to a calculated act." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 730 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.038356164383561646 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0136986301369863 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 9.98 | | std | 7.42 | | cv | 0.743 | | sampleLengths | | 0 | 16 | | 1 | 23 | | 2 | 19 | | 3 | 18 | | 4 | 11 | | 5 | 15 | | 6 | 6 | | 7 | 3 | | 8 | 18 | | 9 | 25 | | 10 | 10 | | 11 | 15 | | 12 | 2 | | 13 | 12 | | 14 | 8 | | 15 | 10 | | 16 | 6 | | 17 | 3 | | 18 | 9 | | 19 | 4 | | 20 | 4 | | 21 | 8 | | 22 | 5 | | 23 | 10 | | 24 | 4 | | 25 | 34 | | 26 | 2 | | 27 | 2 | | 28 | 20 | | 29 | 11 | | 30 | 7 | | 31 | 11 | | 32 | 9 | | 33 | 14 | | 34 | 6 | | 35 | 9 | | 36 | 7 | | 37 | 12 | | 38 | 2 | | 39 | 9 | | 40 | 7 | | 41 | 6 | | 42 | 16 | | 43 | 1 | | 44 | 6 | | 45 | 30 | | 46 | 13 | | 47 | 4 | | 48 | 4 | | 49 | 9 |
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| 57.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.38461538461538464 | | totalSentences | 130 | | uniqueOpeners | 50 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 90 | | matches | | 0 | "Just the single, precise puncture" | | 1 | "Too clean, too small." | | 2 | "Just seven hours until the" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 90 | | matches | | 0 | "He saw things at face" | | 1 | "He was young, mid-twenties maybe," | | 2 | "She needed quiet, and she" | | 3 | "It wasn’t a knife." | | 4 | "She glanced around the platform," | | 5 | "They were watching, but not" | | 6 | "It was small, almost insignificant," | | 7 | "She stood and walked towards" | | 8 | "They never stayed in one" | | 9 | "She passed a stall selling" | | 10 | "She stopped at a stall" | | 11 | "she greeted him" | | 12 | "He didn’t bother with a" | | 13 | "She’d only encountered a handful" | | 14 | "They were expensive, rare, and" | | 15 | "She noticed one was missing" | | 16 | "He reluctantly produced a leather-bound" | | 17 | "she called out, her voice" | | 18 | "She examined the wound again," | | 19 | "She directed a uniformed officer" |
| | ratio | 0.244 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 90 | | matches | | 0 | "The air tasted of wet" | | 1 | "Harlow Quinn adjusted the collar" | | 2 | "Camden Town North, the sign" | | 3 | "This was the Veil Market’s" | | 4 | "DS Davies said, gesturing with" | | 5 | "Davies was good people, solid," | | 6 | "He saw things at face" | | 7 | "A weakness, sometimes." | | 8 | "He was young, mid-twenties maybe," | | 9 | "A tourist, probably, drawn in" | | 10 | "Harlow knelt, her knees protesting" | | 11 | "Davies busied himself with the" | | 12 | "She needed quiet, and she" | | 13 | "The victim’s eyes were wide," | | 14 | "It wasn’t a knife." | | 15 | "Davies commented, joining her" | | 16 | "Harlow didn’t bother looking up." | | 17 | "She glanced around the platform," | | 18 | "Silk scarves shimmered under flickering" | | 19 | "Jars filled with murky liquids" |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow Quinn adjusted the collar of her trench coat, the fabric doing little against the chill that clung to the abandoned Tube station." |
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| 17.86% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 3 | | matches | | 0 | "Harlow said, her voice flat and devoid of pleasantries" | | 1 | "she called out, her voice cutting through the murmur of the market" | | 2 | "Harlow said, her tone grim" |
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| 64.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 4 | | fancyTags | | 0 | "Davies commented (comment)" | | 1 | "Davies observed (observe)" | | 2 | "she called out (call out)" | | 3 | "Eva called out (call out)" |
| | dialogueSentences | 47 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0.5 | | effectiveRatio | 0.17 | |