| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.727 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1109 | | 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) | |
| 5.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1109 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | word | "down her spine" | | count | 1 |
| | 16 | | | 17 | |
| | highlights | | 0 | "firmly" | | 1 | "sentinels" | | 2 | "silence" | | 3 | "echoed" | | 4 | "pulsed" | | 5 | "traced" | | 6 | "gloom" | | 7 | "weight" | | 8 | "rhythmic" | | 9 | "warmth" | | 10 | "glistening" | | 11 | "flicked" | | 12 | "echoing" | | 13 | "glinting" | | 14 | "chill" | | 15 | "down her spine" | | 16 | "resolve" | | 17 | "shattered" |
| |
| 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 | 94 | | matches | (empty) | |
| 97.26% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | 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 | 2 | | markdownWords | 17 | | totalWords | 1109 | | ratio | 0.015 | | matches | | 0 | "Leave the iron box at the center stone. Do not open the latch." | | 1 | "Drop, slide. Drop, slide." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1073 | | uniqueNames | 12 | | maxNameDensity | 1.4 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Yu-Fei | 2 | | Golden | 1 | | Empress | 1 | | Richmond | 1 | | Park | 1 | | Vibrant | 1 | | London | 2 | | Heartstone | 3 | | Cardiff | 1 | | Hel | 1 | | Rory | 15 | | Evan | 3 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Heartstone" | | 2 | "Rory" | | 3 | "Evan" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 0.801 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1109 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 26.4 | | std | 15.98 | | cv | 0.605 | | sampleLengths | | 0 | 29 | | 1 | 59 | | 2 | 52 | | 3 | 25 | | 4 | 37 | | 5 | 14 | | 6 | 2 | | 7 | 49 | | 8 | 6 | | 9 | 46 | | 10 | 33 | | 11 | 8 | | 12 | 35 | | 13 | 45 | | 14 | 41 | | 15 | 29 | | 16 | 9 | | 17 | 4 | | 18 | 40 | | 19 | 11 | | 20 | 12 | | 21 | 10 | | 22 | 69 | | 23 | 28 | | 24 | 4 | | 25 | 23 | | 26 | 27 | | 27 | 10 | | 28 | 35 | | 29 | 29 | | 30 | 7 | | 31 | 30 | | 32 | 12 | | 33 | 35 | | 34 | 15 | | 35 | 34 | | 36 | 13 | | 37 | 36 | | 38 | 32 | | 39 | 37 | | 40 | 15 | | 41 | 22 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 171 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 98 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 86 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.011627906976744186 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.011627906976744186 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 11.32 | | std | 6.49 | | cv | 0.574 | | sampleLengths | | 0 | 20 | | 1 | 9 | | 2 | 28 | | 3 | 6 | | 4 | 25 | | 5 | 18 | | 6 | 18 | | 7 | 16 | | 8 | 8 | | 9 | 17 | | 10 | 14 | | 11 | 8 | | 12 | 15 | | 13 | 4 | | 14 | 10 | | 15 | 2 | | 16 | 10 | | 17 | 8 | | 18 | 5 | | 19 | 9 | | 20 | 5 | | 21 | 12 | | 22 | 6 | | 23 | 18 | | 24 | 5 | | 25 | 10 | | 26 | 8 | | 27 | 5 | | 28 | 15 | | 29 | 18 | | 30 | 8 | | 31 | 8 | | 32 | 6 | | 33 | 21 | | 34 | 17 | | 35 | 5 | | 36 | 4 | | 37 | 19 | | 38 | 16 | | 39 | 16 | | 40 | 6 | | 41 | 3 | | 42 | 22 | | 43 | 7 | | 44 | 4 | | 45 | 5 | | 46 | 2 | | 47 | 2 | | 48 | 22 | | 49 | 18 |
| |
| 68.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4387755102040816 | | totalSentences | 98 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 89 | | matches | | 0 | "Her leather delivery bag thumped" | | 1 | "They stood like ancient petrified" | | 2 | "Her voice sounded flat, deadened" | | 3 | "She pulled a silver wind-up" | | 4 | "She tapped the glass face." | | 5 | "She refocused on her task." | | 6 | "She stepped toward the center" | | 7 | "She adjusted her grip on" | | 8 | "She kept her pace steady." | | 9 | "Her law tutors back in" | | 10 | "She reached the central stone," | | 11 | "she said into the gloom" | | 12 | "It moved with a sickening," | | 13 | "Its skin possessed the glistening," | | 14 | "Her fingers closed around the" | | 15 | "She flicked the blade open" | | 16 | "It didn't walk." | | 17 | "It slithered forward on four" | | 18 | "She maintained her composure, forcing" | | 19 | "she muttered under her breath" |
| | ratio | 0.27 | |
| 38.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 89 | | matches | | 0 | "Rory forced her shoulder against" | | 1 | "Her leather delivery bag thumped" | | 2 | "The client at Yu-Fei’s Golden" | | 3 | "Yu-Fei grabbed the cash without" | | 4 | "Rory took the night shift" | | 5 | "Rory paused, her bright blue" | | 6 | "They stood like ancient petrified" | | 7 | "Silence swallowed the distant hum" | | 8 | "One second the faint rumble" | | 9 | "Rory reached up, her fingers" | | 10 | "The thumbnail-sized crimson gemstone pulsed" | | 11 | "A steady beat of heat" | | 12 | "Rory called out" | | 13 | "Her voice sounded flat, deadened" | | 14 | "She pulled a silver wind-up" | | 15 | "Silas gave her the old" | | 16 | "The second hand jumped erratically." | | 17 | "Tick-tock, tick-tock, then three rapid" | | 18 | "She tapped the glass face." | | 19 | "The thin hand spun backward" |
| | ratio | 0.843 | |
| 56.18% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 89 | | matches | | 0 | "Yet, the tall grass near" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "Yet, the tall grass near the eastern stone flattened, bending downward as if an invisible heavy weight pressed into the stems." | | 1 | "It moved with a sickening, fluid grace, bending at joints that sat entirely wrong on a frame that roughly resembled a human torso." |
| |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "Rory said, her tone dripping with ice" |
| |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.25 | | effectiveRatio | 0.182 | |