| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1146 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 30.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1146 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "electric" | | 2 | "flicker" | | 3 | "tracing" | | 4 | "scanned" | | 5 | "crystal" | | 6 | "familiar" | | 7 | "silence" | | 8 | "calculated" | | 9 | "weight" | | 10 | "efficient" | | 11 | "pulsed" | | 12 | "measured" | | 13 | "echoing" | | 14 | "methodical" |
| |
| 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 | 90 | | matches | (empty) | |
| 31.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 90 | | filterMatches | | 0 | "look" | | 1 | "look see" | | 2 | "look realize" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1146 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1146 | | uniqueNames | 9 | | maxNameDensity | 0.87 | | worstName | "You" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 7 | | Rory | 6 | | Cardiff | 2 | | Evan | 2 | | London | 1 | | Prague | 1 | | You | 10 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Prague" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | 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 | 1146 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 66.22% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 40.93 | | std | 15.62 | | cv | 0.382 | | sampleLengths | | 0 | 67 | | 1 | 53 | | 2 | 45 | | 3 | 3 | | 4 | 43 | | 5 | 15 | | 6 | 56 | | 7 | 21 | | 8 | 44 | | 9 | 44 | | 10 | 53 | | 11 | 24 | | 12 | 43 | | 13 | 28 | | 14 | 50 | | 15 | 35 | | 16 | 41 | | 17 | 58 | | 18 | 33 | | 19 | 46 | | 20 | 21 | | 21 | 66 | | 22 | 49 | | 23 | 42 | | 24 | 42 | | 25 | 15 | | 26 | 54 | | 27 | 55 |
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| 97.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 90 | | matches | | 0 | "were concerned" | | 1 | "surprised" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 203 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 90 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1151 | | adjectiveStacks | 1 | | stackExamples | | 0 | "old, leather-bound history" |
| | adverbCount | 18 | | adverbRatio | 0.015638575152041704 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0026064291920069507 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.73 | | std | 6.8 | | cv | 0.534 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 23 | | 3 | 16 | | 4 | 5 | | 5 | 14 | | 6 | 22 | | 7 | 12 | | 8 | 13 | | 9 | 11 | | 10 | 21 | | 11 | 3 | | 12 | 6 | | 13 | 25 | | 14 | 12 | | 15 | 15 | | 16 | 10 | | 17 | 12 | | 18 | 14 | | 19 | 20 | | 20 | 4 | | 21 | 17 | | 22 | 6 | | 23 | 7 | | 24 | 4 | | 25 | 22 | | 26 | 5 | | 27 | 17 | | 28 | 5 | | 29 | 22 | | 30 | 4 | | 31 | 19 | | 32 | 5 | | 33 | 12 | | 34 | 13 | | 35 | 17 | | 36 | 7 | | 37 | 15 | | 38 | 13 | | 39 | 15 | | 40 | 15 | | 41 | 3 | | 42 | 10 | | 43 | 17 | | 44 | 6 | | 45 | 27 | | 46 | 22 | | 47 | 13 | | 48 | 5 | | 49 | 6 |
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| 32.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.3111111111111111 | | totalSentences | 90 | | uniqueOpeners | 28 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Suddenly, doors unlocked that shouldn't" |
| | ratio | 0.011 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 88 | | matches | | 0 | "He adjusted his silver signet" | | 1 | "She shook off a heavy" | | 2 | "She scanned the room, her" | | 3 | "She caught sight of the" | | 4 | "Her shoulders slumped an inch," | | 5 | "He stopped wiping a crystal" | | 6 | "His limp slowed his movement" | | 7 | "He stopped a few feet" | | 8 | "She pressed her scarred wrist" | | 9 | "She looked at the old" | | 10 | "You kept the place." | | 11 | "I thought you would have" | | 12 | "You look tired, Rory." | | 13 | "She didn't look at him." | | 14 | "She stared at her own" | | 15 | "I stopped being a student" | | 16 | "I’m a delivery person now." | | 17 | "I bring cold takeout to" | | 18 | "It pays the rent, and" | | 19 | "It stopped an inch from" |
| | ratio | 0.636 | |
| 16.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 88 | | matches | | 0 | "Rain lashed against the green" | | 1 | "Silas stood behind the scarred" | | 2 | "He adjusted his silver signet" | | 3 | "The front door groaned open." | | 4 | "A woman stepped out of" | | 5 | "She shook off a heavy" | | 6 | "She scanned the room, her" | | 7 | "Rory walked toward the counter," | | 8 | "She caught sight of the" | | 9 | "Her shoulders slumped an inch," | | 10 | "He stopped wiping a crystal" | | 11 | "His limp slowed his movement" | | 12 | "He stopped a few feet" | | 13 | "The city has a way" | | 14 | "Rory hooked a stool with" | | 15 | "She pressed her scarred wrist" | | 16 | "The movement was sharp, calculated," | | 17 | "She looked at the old" | | 18 | "You kept the place." | | 19 | "I thought you would have" |
| | ratio | 0.886 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 1 | | matches | | 0 | "You have a signet ring that opens locks across the continent." |
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| 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 | |