| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 848 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 11.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 848 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "tracing" | | 1 | "loomed" | | 2 | "oppressive" | | 3 | "flickered" | | 4 | "pulse" | | 5 | "rhythmic" | | 6 | "silence" | | 7 | "warmth" | | 8 | "scanning" | | 9 | "gloom" | | 10 | "vibrated" | | 11 | "could feel" | | 12 | "radiant" |
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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 | "knuckles turned white" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "knuckles turned white" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 69 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 843 | | ratio | 0.005 | | matches | | 0 | "Clack." | | 1 | "Scritch. Scritch. Scritch." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 824 | | uniqueNames | 12 | | maxNameDensity | 1.33 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 1 | | Park | 1 | | London | 1 | | Carter | 1 | | Fae | 1 | | Grove | 1 | | Yu-Fei | 1 | | Heartstone | 3 | | Pendant | 1 | | Hel | 1 | | Rory | 11 | | Cardiff | 1 |
| | persons | | 0 | "Carter" | | 1 | "Yu-Fei" | | 2 | "Heartstone" | | 3 | "Rory" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Fae" | | 4 | "Grove" | | 5 | "Hel" | | 6 | "Cardiff" |
| | globalScore | 0.833 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 81.38% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.186 | | wordCount | 843 | | matches | | 0 | "Not the rustle of fox paws or the wind in the leaves, but a dry, rhythmic scraping" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 24.09 | | std | 14.12 | | cv | 0.586 | | sampleLengths | | 0 | 38 | | 1 | 19 | | 2 | 40 | | 3 | 38 | | 4 | 19 | | 5 | 2 | | 6 | 28 | | 7 | 50 | | 8 | 2 | | 9 | 29 | | 10 | 10 | | 11 | 3 | | 12 | 29 | | 13 | 42 | | 14 | 23 | | 15 | 31 | | 16 | 11 | | 17 | 1 | | 18 | 22 | | 19 | 33 | | 20 | 25 | | 21 | 26 | | 22 | 24 | | 23 | 19 | | 24 | 46 | | 25 | 14 | | 26 | 28 | | 27 | 53 | | 28 | 4 | | 29 | 22 | | 30 | 47 | | 31 | 25 | | 32 | 17 | | 33 | 9 | | 34 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 69 | | matches | | |
| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 141 | | matches | | 0 | "was tearing" | | 1 | "were dying" | | 2 | "wasn't looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 72 | | ratio | 0.056 | | matches | | 0 | "She had come for the drop—a sealed envelope Yu-Fei had insisted needed personal delivery before dawn." | | 1 | "It wasn't supposed to glow like this—not unless a portal to Hel was tearing open nearby." | | 2 | "Her Heartstone didn't just pulse now—it vibrated, burning her skin, casting a harsh crimson glare across the dying grove." | | 3 | "It wasn't looking at her—it lacked eyes—but its head tilted, trailing the scent of her sweat, twitching at the sound of her ragged breathing." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 106 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 1 | | adverbRatio | 0.009433962264150943 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 11.71 | | std | 6.1 | | cv | 0.521 | | sampleLengths | | 0 | 15 | | 1 | 23 | | 2 | 19 | | 3 | 7 | | 4 | 16 | | 5 | 17 | | 6 | 10 | | 7 | 19 | | 8 | 9 | | 9 | 4 | | 10 | 15 | | 11 | 2 | | 12 | 11 | | 13 | 17 | | 14 | 11 | | 15 | 23 | | 16 | 16 | | 17 | 2 | | 18 | 6 | | 19 | 17 | | 20 | 6 | | 21 | 9 | | 22 | 1 | | 23 | 3 | | 24 | 17 | | 25 | 12 | | 26 | 9 | | 27 | 18 | | 28 | 15 | | 29 | 17 | | 30 | 6 | | 31 | 16 | | 32 | 15 | | 33 | 4 | | 34 | 7 | | 35 | 1 | | 36 | 8 | | 37 | 14 | | 38 | 17 | | 39 | 16 | | 40 | 7 | | 41 | 18 | | 42 | 19 | | 43 | 7 | | 44 | 8 | | 45 | 16 | | 46 | 4 | | 47 | 15 | | 48 | 8 | | 49 | 21 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.375 | | totalSentences | 72 | | uniqueOpeners | 27 | |
| 51.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 65 | | matches | | | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 65 | | matches | | 0 | "She had come for the" | | 1 | "She checked her phone." | | 2 | "Her voice sounded muffled, swallowed" | | 3 | "It wasn't supposed to glow" | | 4 | "She took three steps toward" | | 5 | "she muttered, though her bright" | | 6 | "She reached the designated drop" | | 7 | "Her breath bloomed into pale" | | 8 | "Her hand dropped to her" | | 9 | "She kept her voice even," | | 10 | "It parted and re-formed in" | | 11 | "It moved, but the air" | | 12 | "Her Heartstone didn't just pulse" | | 13 | "It had no face, only" | | 14 | "It crouched on all fours," | | 15 | "It wasn't looking at her—it" | | 16 | "It rose, unfolding like an" | | 17 | "It leaned down, dropping its" | | 18 | "She could feel the radiant," |
| | ratio | 0.292 | |
| 29.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 65 | | matches | | 0 | "midnight fog hung thick over" | | 1 | "Aurora Carter adjusted the strap" | | 2 | "The oak standing stones loomed" | | 3 | "Rory swallowed hard, stepping past" | | 4 | "She had come for the" | | 5 | "The pay was triple her" | | 6 | "Earth gave way to vibrant" | | 7 | "The air smelled of crushed" | | 8 | "She checked her phone." | | 9 | "The screen flickered, the digital" | | 10 | "Her voice sounded muffled, swallowed" | | 11 | "Time moved wrong in this" | | 12 | "Rory reached into her jacket" | | 13 | "The thumbnail-sized crimson gemstone flared" | | 14 | "It wasn't supposed to glow" | | 15 | "A sound rippled through the" | | 16 | "Rory whirled around, black hair" | | 17 | "Silence answered her." | | 18 | "She took three steps toward" | | 19 | "The warmth from the pendant" |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 51.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 5 | | matches | | 0 | "Earth gave way to vibrant blue wildflowers that bloomed unnaturally in the dark, their petals brushing against her jeans." | | 1 | "The head of the shadow was misshapen, elongated, bristling with spindly projections that twitched like insect legs." | | 2 | "Her Heartstone didn't just pulse now—it vibrated, burning her skin, casting a harsh crimson glare across the dying grove." | | 3 | "It crouched on all fours, its joints bending backward with sickening fluidity." | | 4 | "It wasn't looking at her—it lacked eyes—but its head tilted, trailing the scent of her sweat, twitching at the sound of her ragged breathing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |