| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 915 | | totalAiIsmAdverbs | 2 | | found | | | 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) | |
| 28.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 915 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "shattered" | | 3 | "echoing" | | 4 | "perfect" | | 5 | "systematic" | | 6 | "pulse" | | 7 | "etched" | | 8 | "intricate" | | 9 | "unwavering" | | 10 | "methodical" | | 11 | "stark" |
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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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 913 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 742 | | uniqueNames | 8 | | maxNameDensity | 0.94 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 7 | | Tube | 1 | | Davies | 5 | | Fixer | 2 | | Veil | 3 | | Market | 2 | | Compass | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Davies" | | 2 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 62.28% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it had been carved from worn" | | 1 | "looked like silver ingots, now melted int" |
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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 | 913 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 39.7 | | std | 28.13 | | cv | 0.709 | | sampleLengths | | 0 | 91 | | 1 | 15 | | 2 | 27 | | 3 | 20 | | 4 | 89 | | 5 | 53 | | 6 | 21 | | 7 | 46 | | 8 | 83 | | 9 | 29 | | 10 | 29 | | 11 | 3 | | 12 | 88 | | 13 | 32 | | 14 | 7 | | 15 | 23 | | 16 | 4 | | 17 | 44 | | 18 | 28 | | 19 | 57 | | 20 | 82 | | 21 | 19 | | 22 | 23 |
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| 80.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 72 | | matches | | 0 | "been carved" | | 1 | "was dressed" | | 2 | "been carved" | | 3 | "was snapped" | | 4 | "been broken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 113 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 744 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.024193548387096774 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008064516129032258 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 11.13 | | std | 6.91 | | cv | 0.62 | | sampleLengths | | 0 | 20 | | 1 | 5 | | 2 | 11 | | 3 | 1 | | 4 | 9 | | 5 | 21 | | 6 | 24 | | 7 | 15 | | 8 | 14 | | 9 | 13 | | 10 | 10 | | 11 | 10 | | 12 | 13 | | 13 | 29 | | 14 | 10 | | 15 | 18 | | 16 | 10 | | 17 | 9 | | 18 | 13 | | 19 | 2 | | 20 | 1 | | 21 | 18 | | 22 | 18 | | 23 | 1 | | 24 | 15 | | 25 | 6 | | 26 | 6 | | 27 | 7 | | 28 | 33 | | 29 | 12 | | 30 | 11 | | 31 | 10 | | 32 | 13 | | 33 | 10 | | 34 | 3 | | 35 | 5 | | 36 | 19 | | 37 | 21 | | 38 | 8 | | 39 | 6 | | 40 | 8 | | 41 | 15 | | 42 | 3 | | 43 | 11 | | 44 | 15 | | 45 | 16 | | 46 | 11 | | 47 | 3 | | 48 | 6 | | 49 | 26 |
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| 47.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.34146341463414637 | | totalSentences | 82 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 44.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 66 | | matches | | 0 | "She descended the last few" | | 1 | "she said, her voice low" | | 2 | "She moved deeper into the" | | 3 | "He was a Fixer, a" | | 4 | "She’d seen his file, a" | | 5 | "He was dressed in an" | | 6 | "She leaned closer, not touching," | | 7 | "She stood and walked a" | | 8 | "It wasn't vandalism." | | 9 | "It was a systematic neutering." | | 10 | "She’d clocked them the moment" | | 11 | "Her attention snagged on something" | | 12 | "She knelt again, this time" | | 13 | "It was a compass, its" | | 14 | "She’d only ever seen one" | | 15 | "She picked it up, her" | | 16 | "It whirled with a frantic," | | 17 | "It pointed, unwavering, directly at" | | 18 | "She stood up, the compass" | | 19 | "She looked from the compass" |
| | ratio | 0.439 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "The coppery tang of blood" | | 1 | "A sharp, clean smell, like" | | 2 | "The two smells didn't belong" | | 3 | "She descended the last few" | | 4 | "The abandoned Tube station was" | | 5 | "DS Davies gestured with a" | | 6 | "Harlow’s gaze swept past him," | | 7 | "A fine, shimmering dust covered" | | 8 | "she said, her voice low" | | 9 | "She moved deeper into the" | | 10 | "The victim lay in the" | | 11 | "He was a Fixer, a" | | 12 | "She’d seen his file, a" | | 13 | "He was dressed in an" | | 14 | "A perfect circle, three inches" | | 15 | "The edges of the hole" | | 16 | "Davies offered, coming up beside" | | 17 | "Harlow crouched, her sharp jaw" | | 18 | "She leaned closer, not touching," | | 19 | "She stood and walked a" |
| | ratio | 0.909 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 66 | | matches | | 0 | "To the ozone in the" | | 1 | "To the drained magic in" |
| | ratio | 0.03 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 3 | | matches | | 0 | "The coppery tang of blood hit Harlow first, a familiar metallic scent that clung to the back of her throat." | | 1 | "She’d seen his file, a ghost in the system who made problems disappear for the city’s supernatural underbelly." | | 2 | "She’d only ever seen one in a confiscated evidence box from a case three years ago, a case that had cost her DS Morris his life." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 4 | | matches | | 0 | "she said, her voice low" | | 1 | "Harlow said, a cold certainty settling in her gut" | | 2 | "She stood up, the compass still held tight in her hand" | | 3 | "she said, her voice flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "Harlow muttered (mutter)" |
| | dialogueSentences | 22 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.125 | | effectiveRatio | 0.091 | |