| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.219 | | leniency | 0.438 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1645 | | 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) | |
| 93.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1645 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1645 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1019 | | uniqueNames | 17 | | maxNameDensity | 1.77 | | worstName | "Bethan" | | maxWindowNameDensity | 4 | | worstWindowName | "Bethan" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Rory | 16 | | Dean | 1 | | Street | 1 | | Silas | 6 | | Prague | 1 | | Vienna | 1 | | Berlin | 1 | | Pryce | 1 | | Valleys | 1 | | One | 2 | | Bethan | 18 | | Chinese | 1 | | French | 1 | | Evan | 2 | | Nest | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Pryce" | | 3 | "Valleys" | | 4 | "One" | | 5 | "Bethan" | | 6 | "Evan" | | 7 | "Nest" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Prague" | | 3 | "Vienna" | | 4 | "Berlin" |
| | globalScore | 0.617 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1645 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 136 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 21.93 | | std | 23.22 | | cv | 1.058 | | sampleLengths | | 0 | 69 | | 1 | 22 | | 2 | 1 | | 3 | 15 | | 4 | 3 | | 5 | 92 | | 6 | 64 | | 7 | 40 | | 8 | 6 | | 9 | 5 | | 10 | 4 | | 11 | 30 | | 12 | 2 | | 13 | 41 | | 14 | 2 | | 15 | 37 | | 16 | 5 | | 17 | 53 | | 18 | 5 | | 19 | 3 | | 20 | 5 | | 21 | 35 | | 22 | 3 | | 23 | 3 | | 24 | 7 | | 25 | 17 | | 26 | 3 | | 27 | 3 | | 28 | 3 | | 29 | 59 | | 30 | 65 | | 31 | 4 | | 32 | 5 | | 33 | 5 | | 34 | 7 | | 35 | 28 | | 36 | 26 | | 37 | 5 | | 38 | 71 | | 39 | 4 | | 40 | 2 | | 41 | 35 | | 42 | 63 | | 43 | 7 | | 44 | 14 | | 45 | 1 | | 46 | 1 | | 47 | 54 | | 48 | 6 | | 49 | 27 |
| |
| 93.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 86 | | matches | | 0 | "being asked" | | 1 | "been booked" | | 2 | "was gone" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 155 | | matches | | 0 | "was coming" | | 1 | "was keeping" | | 2 | "was ordering" | | 3 | "was listening" | | 4 | "was trying" | | 5 | "was pretending" | | 6 | "was reading" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1024 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.021484375 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001953125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 12.1 | | std | 11.18 | | cv | 0.924 | | sampleLengths | | 0 | 23 | | 1 | 29 | | 2 | 17 | | 3 | 11 | | 4 | 11 | | 5 | 1 | | 6 | 15 | | 7 | 3 | | 8 | 18 | | 9 | 13 | | 10 | 13 | | 11 | 2 | | 12 | 10 | | 13 | 15 | | 14 | 21 | | 15 | 3 | | 16 | 25 | | 17 | 1 | | 18 | 21 | | 19 | 4 | | 20 | 10 | | 21 | 40 | | 22 | 6 | | 23 | 5 | | 24 | 4 | | 25 | 8 | | 26 | 2 | | 27 | 20 | | 28 | 2 | | 29 | 23 | | 30 | 9 | | 31 | 9 | | 32 | 2 | | 33 | 20 | | 34 | 17 | | 35 | 5 | | 36 | 15 | | 37 | 21 | | 38 | 17 | | 39 | 5 | | 40 | 3 | | 41 | 3 | | 42 | 2 | | 43 | 12 | | 44 | 23 | | 45 | 3 | | 46 | 3 | | 47 | 7 | | 48 | 10 | | 49 | 3 |
| |
| 45.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.33088235294117646 | | totalSentences | 136 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 95.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 77 | | matches | | 0 | "She had delivered the last" | | 1 | "He limped off toward the" | | 2 | "It was a habit Silas" | | 3 | "She knew that voice." | | 4 | "She had grown up across" | | 5 | "It lasted about ninety seconds." | | 6 | "She pulled the second glove" | | 7 | "She put a hand flat" | | 8 | "She waved a hand over" | | 9 | "She eyed the thermal bag" | | 10 | "He looked at Bethan for" | | 11 | "She leaned in." | | 12 | "She wrapped both hands round" | | 13 | "She didn't need to say" | | 14 | "It came up in a" | | 15 | "She tore the curl of" | | 16 | "Her eyes had gone bright." | | 17 | "She wiped her mouth with" | | 18 | "She searched Rory's face as" | | 19 | "She glanced at the mirror" |
| | ratio | 0.312 | |
| 44.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 77 | | matches | | 0 | "The Golden Empress thermal bag" | | 1 | "She had delivered the last" | | 2 | "Silas set the glass in" | | 3 | "The silver ring on his" | | 4 | "He limped off toward the" | | 5 | "Rory drank and watched the" | | 6 | "It was a habit Silas" | | 7 | "A couple on a first" | | 8 | "The old maps on the" | | 9 | "The door opened." | | 10 | "Rory could tell from the" | | 11 | "She knew that voice." | | 12 | "She had grown up across" | | 13 | "The third one in wore" | | 14 | "Rory looked back at her" | | 15 | "It lasted about ninety seconds." | | 16 | "The voice cut straight through" | | 17 | "Bethan Pryce stood three feet" | | 18 | "Bethan laughed, a big Valleys" | | 19 | "She pulled the second glove" |
| | ratio | 0.831 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | 0 | "Now she wanted a pint," |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "Bethan laughed (laugh)" | | 1 | "Bethan pressed (press)" |
| | dialogueSentences | 64 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0.5 | | effectiveRatio | 0.063 | |