| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1180 | | 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) | |
| 61.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1180 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "flickered" | | 2 | "electric" | | 3 | "scanned" | | 4 | "crystal" | | 5 | "pulsed" | | 6 | "weight" | | 7 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 239 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 239 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 15 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1179 | | uniqueNames | 13 | | maxNameDensity | 1.61 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Morris | 5 | | Herrera | 10 | | Town | 1 | | Saint | 1 | | Christopher | 1 | | Detective | 1 | | Veil | 1 | | Metropolitan | 1 | | Police | 1 | | Quinn | 19 |
| | persons | | 0 | "Morris" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Veil" | | 5 | "Quinn" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Town" |
| | globalScore | 0.694 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | 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 | 1179 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 19.98 | | std | 17.18 | | cv | 0.86 | | sampleLengths | | 0 | 74 | | 1 | 41 | | 2 | 65 | | 3 | 53 | | 4 | 32 | | 5 | 29 | | 6 | 20 | | 7 | 5 | | 8 | 26 | | 9 | 5 | | 10 | 28 | | 11 | 3 | | 12 | 24 | | 13 | 8 | | 14 | 8 | | 15 | 54 | | 16 | 4 | | 17 | 39 | | 18 | 4 | | 19 | 3 | | 20 | 12 | | 21 | 14 | | 22 | 44 | | 23 | 6 | | 24 | 6 | | 25 | 37 | | 26 | 44 | | 27 | 23 | | 28 | 13 | | 29 | 3 | | 30 | 3 | | 31 | 7 | | 32 | 7 | | 33 | 24 | | 34 | 5 | | 35 | 15 | | 36 | 32 | | 37 | 35 | | 38 | 23 | | 39 | 4 | | 40 | 8 | | 41 | 5 | | 42 | 38 | | 43 | 7 | | 44 | 6 | | 45 | 28 | | 46 | 21 | | 47 | 6 | | 48 | 4 | | 49 | 28 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 224 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1180 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.017796610169491526 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00423728813559322 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 4.93 | | std | 2.56 | | cv | 0.519 | | sampleLengths | | 0 | 9 | | 1 | 2 | | 2 | 7 | | 3 | 7 | | 4 | 10 | | 5 | 15 | | 6 | 7 | | 7 | 6 | | 8 | 11 | | 9 | 4 | | 10 | 5 | | 11 | 7 | | 12 | 5 | | 13 | 7 | | 14 | 5 | | 15 | 4 | | 16 | 4 | | 17 | 6 | | 18 | 10 | | 19 | 2 | | 20 | 9 | | 21 | 8 | | 22 | 4 | | 23 | 6 | | 24 | 8 | | 25 | 6 | | 26 | 6 | | 27 | 10 | | 28 | 3 | | 29 | 6 | | 30 | 3 | | 31 | 5 | | 32 | 3 | | 33 | 10 | | 34 | 6 | | 35 | 6 | | 36 | 1 | | 37 | 2 | | 38 | 7 | | 39 | 3 | | 40 | 3 | | 41 | 6 | | 42 | 6 | | 43 | 1 | | 44 | 2 | | 45 | 2 | | 46 | 5 | | 47 | 3 | | 48 | 7 | | 49 | 5 |
| |
| 50.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.3598326359832636 | | totalSentences | 239 | | uniqueOpeners | 86 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 200 | | matches | | 0 | "Once you cross, there is" | | 1 | "Then you are here to" | | 2 | "Only the murmur of the" | | 3 | "Then you cannot pass." |
| | ratio | 0.02 | |
| 92.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 64 | | totalSentences | 200 | | matches | | 0 | "Her boots slapped against the" | | 1 | "She checked her wrist." | | 2 | "She pushed the memory down." | | 3 | "She recognised the curl of" | | 4 | "She recognised the limp favouring" | | 5 | "Her breath came in sharp" | | 6 | "Her jacket soaked through at" | | 7 | "Her hips cleared the top" | | 8 | "She landed hard." | | 9 | "She ignored it." | | 10 | "She approached the gap in" | | 11 | "She stepped onto the tracks." | | 12 | "He reached into his pocket." | | 13 | "You should not be here." | | 14 | "She remembered the report." | | 15 | "He slid it into a" | | 16 | "She aimed at his centre" | | 17 | "Her stance widened." | | 18 | "You do not understand what" | | 19 | "I understand you are obstructing" |
| | ratio | 0.32 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 170 | | totalSentences | 200 | | matches | | 0 | "Rain lashed against the pavement" | | 1 | "Her boots slapped against the" | | 2 | "The sound echoed off the" | | 3 | "Shadows stretched long under the" | | 4 | "Quinn tightened her grip on" | | 5 | "The beam cut through the" | | 6 | "Dust motes danced in the" | | 7 | "She checked her wrist." | | 8 | "The worn leather watch ticked." | | 9 | "The second hand swept past" | | 10 | "She pushed the memory down." | | 11 | "The suspect wore a dark" | | 12 | "Olive skin flashed under the" | | 13 | "She recognised the curl of" | | 14 | "She recognised the limp favouring" | | 15 | "Quinn increased her pace." | | 16 | "Her breath came in sharp" | | 17 | "The air tasted of ozone" | | 18 | "Her jacket soaked through at" | | 19 | "The cold bit into her" |
| | ratio | 0.85 | |
| 50.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 200 | | matches | | 0 | "If she retreated, the trail" | | 1 | "If she advanced, she entered" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |