| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1773 | | totalAiIsmAdverbs | 3 | | 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) | |
| 46.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1773 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "weight" | | 1 | "whisper" | | 2 | "echoing" | | 3 | "tracing" | | 4 | "pulsed" | | 5 | "rhythmic" | | 6 | "warmth" | | 7 | "pulse" | | 8 | "vibrated" | | 9 | "resonated" | | 10 | "tension" | | 11 | "flickered" | | 12 | "scanned" | | 13 | "echoed" | | 14 | "stark" | | 15 | "searing" | | 16 | "perfect" | | 17 | "silence" |
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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 | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 217 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 217 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 217 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1773 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 108 | | wordCount | 1773 | | uniqueNames | 21 | | maxNameDensity | 1.69 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | London | 2 | | Park | 1 | | Veil | 3 | | Fae-Forged | 2 | | Blade | 2 | | Heartstone | 7 | | Pendant | 1 | | Seer | 4 | | Hel | 6 | | Earth | 1 | | Fae | 9 | | Courts | 1 | | Aurora | 30 | | Rory | 2 | | Isolde | 4 | | Pre-Law | 1 | | Hell | 1 | | Dymas | 2 | | Nyx | 22 | | Do | 4 | | You | 3 |
| | persons | | 0 | "Blade" | | 1 | "Seer" | | 2 | "Aurora" | | 3 | "Rory" | | 4 | "Isolde" | | 5 | "Hell" | | 6 | "Nyx" | | 7 | "You" |
| | places | | 0 | "London" | | 1 | "Park" | | 2 | "Veil" | | 3 | "Hel" | | 4 | "Earth" | | 5 | "Fae" | | 6 | "Dymas" |
| | globalScore | 0.654 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 141 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a thick mattress, springing b" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1773 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 73.13% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 26.86 | | std | 10.91 | | cv | 0.406 | | sampleLengths | | 0 | 31 | | 1 | 23 | | 2 | 19 | | 3 | 39 | | 4 | 16 | | 5 | 22 | | 6 | 20 | | 7 | 53 | | 8 | 18 | | 9 | 24 | | 10 | 32 | | 11 | 4 | | 12 | 26 | | 13 | 34 | | 14 | 37 | | 15 | 16 | | 16 | 21 | | 17 | 48 | | 18 | 32 | | 19 | 31 | | 20 | 21 | | 21 | 11 | | 22 | 25 | | 23 | 36 | | 24 | 8 | | 25 | 46 | | 26 | 23 | | 27 | 29 | | 28 | 10 | | 29 | 13 | | 30 | 33 | | 31 | 39 | | 32 | 10 | | 33 | 7 | | 34 | 20 | | 35 | 27 | | 36 | 29 | | 37 | 34 | | 38 | 35 | | 39 | 26 | | 40 | 38 | | 41 | 26 | | 42 | 45 | | 43 | 17 | | 44 | 24 | | 45 | 39 | | 46 | 25 | | 47 | 22 | | 48 | 26 | | 49 | 12 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 217 | | matches | | 0 | "was gone" | | 1 | "being tested" | | 2 | "is tied" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 302 | | matches | | 0 | "was freezing" | | 1 | "were slowly curling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 217 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1788 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar." |
| | adverbCount | 38 | | adverbRatio | 0.021252796420581657 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.00727069351230425 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 217 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 217 | | mean | 8.17 | | std | 4.6 | | cv | 0.563 | | sampleLengths | | 0 | 8 | | 1 | 9 | | 2 | 8 | | 3 | 3 | | 4 | 3 | | 5 | 5 | | 6 | 18 | | 7 | 13 | | 8 | 2 | | 9 | 4 | | 10 | 6 | | 11 | 18 | | 12 | 15 | | 13 | 8 | | 14 | 8 | | 15 | 5 | | 16 | 17 | | 17 | 12 | | 18 | 3 | | 19 | 5 | | 20 | 4 | | 21 | 10 | | 22 | 13 | | 23 | 12 | | 24 | 14 | | 25 | 5 | | 26 | 4 | | 27 | 6 | | 28 | 3 | | 29 | 12 | | 30 | 5 | | 31 | 7 | | 32 | 3 | | 33 | 29 | | 34 | 4 | | 35 | 5 | | 36 | 6 | | 37 | 8 | | 38 | 7 | | 39 | 4 | | 40 | 7 | | 41 | 13 | | 42 | 10 | | 43 | 8 | | 44 | 9 | | 45 | 20 | | 46 | 7 | | 47 | 4 | | 48 | 5 | | 49 | 7 |
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| 41.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.24423963133640553 | | totalSentences | 217 | | uniqueOpeners | 53 | |
| 64.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 206 | | matches | | 0 | "Only her boot prints marked" | | 1 | "Instead, the liquid mirrored a" | | 2 | "Then we do it ourselves." | | 3 | "Even the hum of the" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 206 | | matches | | 0 | "She pushed forward." | | 1 | "Their humanoid silhouette of living" | | 2 | "It feels heavy." | | 3 | "She adjusted her jacket." | | 4 | "They do not align with" | | 5 | "Her boots sank into the" | | 6 | "She paused, looking back at" | | 7 | "It only does this near" | | 8 | "They walked along the printless" | | 9 | "I feel it." | | 10 | "She exhaled, the tension leaving" | | 11 | "Your mind is resilient, but" | | 12 | "They broke through the tree" | | 13 | "Their reflection did not show" | | 14 | "Your human face." | | 15 | "I am trapped between." | | 16 | "It will show you the" | | 17 | "We need to find Isolde." | | 18 | "She offered the blade." | | 19 | "She knows about the rift" |
| | ratio | 0.204 | |
| 30.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 177 | | totalSentences | 206 | | matches | | 0 | "Aurora stepped between the ancient" | | 1 | "The air thickened, turning to" | | 2 | "A faint shimmering distortion rippled" | | 3 | "She pushed forward." | | 4 | "The resistance snapped." | | 5 | "Nyx glided through the barrier." | | 6 | "Their humanoid silhouette of living" | | 7 | "The scent of damp London" | | 8 | "Richmond Park was gone." | | 9 | "A sprawling clearing stretched before" | | 10 | "Wildflowers in violent shades of" | | 11 | "Nyx drifted over a patch" | | 12 | "The flowers did not bend" | | 13 | "The Veil is thicker here." | | 14 | "Nyx drifted like a whisper" | | 15 | "Aurora rubbed her left wrist," | | 16 | "It feels heavy." | | 17 | "She adjusted her jacket." | | 18 | "The Fae-Forged Blade rested in" | | 19 | "The moonsilver hilt radiated a" |
| | ratio | 0.859 | |
| 72.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 206 | | matches | | 0 | "If a rift is tearing," | | 1 | "If it is burning, something" | | 2 | "If I can channel the" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 1 | | matches | | 0 | "The petals were thick and velvety, weeping a sweet, cloying nectar that attracted insects with iridescent, glass-like wings." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |