| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 826 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 826 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | word | "down her spine" | | count | 1 |
| | 17 | |
| | highlights | | 0 | "pulse" | | 1 | "silence" | | 2 | "navigated" | | 3 | "cacophony" | | 4 | "etched" | | 5 | "glinting" | | 6 | "stark" | | 7 | "eyebrow" | | 8 | "navigate" | | 9 | "scanning" | | 10 | "chaotic" | | 11 | "pulsed" | | 12 | "racing" | | 13 | "furrowing" | | 14 | "could feel" | | 15 | "tangible" | | 16 | "down her spine" | | 17 | "tension" |
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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 | 0 | | narrationSentences | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 829 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 41.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 643 | | uniqueNames | 11 | | maxNameDensity | 2.18 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Tube | 1 | | Detective | 1 | | Harlow | 14 | | Quinn | 2 | | Veil | 3 | | Market | 1 | | Camden | 1 | | Davies | 9 | | Met | 1 | | Compass | 2 |
| | persons | | 0 | "Tube" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Davies" | | 4 | "Met" | | 5 | "Compass" |
| | places | | | globalScore | 0.411 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 829 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 34.54 | | std | 20.85 | | cv | 0.604 | | sampleLengths | | 0 | 82 | | 1 | 52 | | 2 | 58 | | 3 | 37 | | 4 | 18 | | 5 | 16 | | 6 | 21 | | 7 | 78 | | 8 | 6 | | 9 | 42 | | 10 | 57 | | 11 | 25 | | 12 | 17 | | 13 | 39 | | 14 | 14 | | 15 | 30 | | 16 | 10 | | 17 | 37 | | 18 | 15 | | 19 | 20 | | 20 | 52 | | 21 | 45 | | 22 | 48 | | 23 | 10 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 67 | | matches | | |
| 86.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 117 | | matches | | 0 | "were watching" | | 1 | "were waiting" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 79 | | ratio | 0.013 | | matches | | 0 | "The victim was dressed in the usual market fare - a mishmash of styles and eras, designed to blend in with the eclectic crowd." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 642 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03582554517133956 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010903426791277258 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 10.49 | | std | 6.31 | | cv | 0.602 | | sampleLengths | | 0 | 25 | | 1 | 16 | | 2 | 22 | | 3 | 19 | | 4 | 16 | | 5 | 11 | | 6 | 18 | | 7 | 7 | | 8 | 7 | | 9 | 16 | | 10 | 22 | | 11 | 13 | | 12 | 1 | | 13 | 19 | | 14 | 17 | | 15 | 14 | | 16 | 4 | | 17 | 4 | | 18 | 12 | | 19 | 5 | | 20 | 16 | | 21 | 8 | | 22 | 24 | | 23 | 14 | | 24 | 14 | | 25 | 7 | | 26 | 3 | | 27 | 8 | | 28 | 6 | | 29 | 4 | | 30 | 17 | | 31 | 21 | | 32 | 7 | | 33 | 19 | | 34 | 13 | | 35 | 11 | | 36 | 7 | | 37 | 12 | | 38 | 13 | | 39 | 2 | | 40 | 15 | | 41 | 4 | | 42 | 23 | | 43 | 12 | | 44 | 9 | | 45 | 5 | | 46 | 2 | | 47 | 28 | | 48 | 6 | | 49 | 4 |
| |
| 62.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43037974683544306 | | totalSentences | 79 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 56.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 61 | | matches | | 0 | "Her worn leather watch glinted" | | 1 | "She was here on business," | | 2 | "She looked up as Harlow" | | 3 | "His hands were clean, nails" | | 4 | "she asked, standing up" | | 5 | "She pointed to a nearby" | | 6 | "It was a chaotic mess" | | 7 | "She picked up a small" | | 8 | "It pulsed with a faint" | | 9 | "She put it back, her" | | 10 | "she said, turning back to" | | 11 | "She pointed to the congealing" | | 12 | "They were all watching her," | | 13 | "She could feel it, like" | | 14 | "She turned back to Davies." | | 15 | "She walked back to the" | | 16 | "She picked up the Veil" | | 17 | "It was warm, its needle" | | 18 | "She slipped it into her" | | 19 | "She stood up, her eyes" |
| | ratio | 0.41 | |
| 41.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 61 | | matches | | 0 | "The Veil Market, an underground" | | 1 | "Her worn leather watch glinted" | | 2 | "The bone token clutched in" | | 3 | "The market was a sprawling" | | 4 | "Stalls peddled enchanted trinkets, banned" | | 5 | "Harlow navigated the labyrinthine stalls," | | 6 | "She was here on business," | | 7 | "A commotion up ahead drew" | | 8 | "A circle of onlookers had" | | 9 | "Harlow pushed through the crowd," | | 10 | "Amelia Davies, the Met's forensic" | | 11 | "She looked up as Harlow" | | 12 | "Davies acknowledged, her voice a" | | 13 | "Harlow raised an eyebrow." | | 14 | "Davies gestured to the body." | | 15 | "Harlow crouched down, her eyes" | | 16 | "The victim was dressed in" | | 17 | "His hands were clean, nails" | | 18 | "A small brass compass hung" | | 19 | "A Veil Compass, attuned to" |
| | ratio | 0.836 | |
| 81.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 61 | | matches | | 0 | "Because Harlow Quinn was here." |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "It was warm, its needle twitching slightly, pointing towards something unseen." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Davies acknowledged (acknowledge)" |
| | dialogueSentences | 17 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0.333 | | effectiveRatio | 0.118 | |