| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1130 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1130 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1130 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 37.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 624 | | uniqueNames | 7 | | maxNameDensity | 2.24 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Hughes | 1 | | Llandaff | 1 | | Rory | 12 | | Eva | 14 | | Silas | 5 | | Blackwood | 1 | | Soho | 1 |
| | persons | | 0 | "Hughes" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Blackwood" |
| | places | | | globalScore | 0.378 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 1130 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 89 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 21.73 | | std | 21.32 | | cv | 0.981 | | sampleLengths | | 0 | 23 | | 1 | 8 | | 2 | 92 | | 3 | 1 | | 4 | 35 | | 5 | 6 | | 6 | 68 | | 7 | 4 | | 8 | 23 | | 9 | 19 | | 10 | 4 | | 11 | 16 | | 12 | 36 | | 13 | 3 | | 14 | 4 | | 15 | 26 | | 16 | 27 | | 17 | 42 | | 18 | 2 | | 19 | 4 | | 20 | 32 | | 21 | 37 | | 22 | 39 | | 23 | 9 | | 24 | 3 | | 25 | 11 | | 26 | 35 | | 27 | 9 | | 28 | 1 | | 29 | 1 | | 30 | 18 | | 31 | 40 | | 32 | 7 | | 33 | 9 | | 34 | 2 | | 35 | 58 | | 36 | 44 | | 37 | 62 | | 38 | 5 | | 39 | 4 | | 40 | 34 | | 41 | 43 | | 42 | 7 | | 43 | 6 | | 44 | 3 | | 45 | 48 | | 46 | 64 | | 47 | 5 | | 48 | 3 | | 49 | 2 |
| |
| 84.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 50 | | matches | | 0 | "was cropped" | | 1 | "being asked" | | 2 | "been photographed" |
| |
| 79.88% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 111 | | matches | | 0 | "was slicing" | | 1 | "was already reaching" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 89 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 626 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.038338658146964855 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009584664536741214 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 12.7 | | std | 10.58 | | cv | 0.833 | | sampleLengths | | 0 | 23 | | 1 | 8 | | 2 | 5 | | 3 | 32 | | 4 | 23 | | 5 | 16 | | 6 | 16 | | 7 | 1 | | 8 | 31 | | 9 | 4 | | 10 | 6 | | 11 | 2 | | 12 | 21 | | 13 | 18 | | 14 | 10 | | 15 | 7 | | 16 | 10 | | 17 | 4 | | 18 | 23 | | 19 | 6 | | 20 | 13 | | 21 | 4 | | 22 | 16 | | 23 | 2 | | 24 | 18 | | 25 | 16 | | 26 | 3 | | 27 | 4 | | 28 | 12 | | 29 | 14 | | 30 | 4 | | 31 | 17 | | 32 | 6 | | 33 | 30 | | 34 | 12 | | 35 | 2 | | 36 | 4 | | 37 | 17 | | 38 | 15 | | 39 | 20 | | 40 | 17 | | 41 | 33 | | 42 | 6 | | 43 | 9 | | 44 | 3 | | 45 | 11 | | 46 | 10 | | 47 | 2 | | 48 | 23 | | 49 | 9 |
| |
| 54.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.38202247191011235 | | totalSentences | 89 | | uniqueOpeners | 34 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 42 | | matches | | 0 | "Then the laugh faded, and" |
| | ratio | 0.024 | |
| 67.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 42 | | matches | | 0 | "She turned on the stool." | | 1 | "Her hair had been long" | | 2 | "She wore a navy wool" | | 3 | "She set the keys on" | | 4 | "He didn't look up from" | | 5 | "He never looked up when" | | 6 | "He only ever looked up" | | 7 | "It was the same laugh," | | 8 | "It was the good stuff," | | 9 | "He had the sense to" | | 10 | "She had been doing that" | | 11 | "Her mother used to call" | | 12 | "She pressed her thumb against" | | 13 | "She looked at the keys," | | 14 | "She saw the woman she" | | 15 | "She slid the keys into" |
| | ratio | 0.381 | |
| 7.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 42 | | matches | | 0 | "Rory had her back to" | | 1 | "She turned on the stool." | | 2 | "Eva Hughes stood under the" | | 3 | "Her hair had been long" | | 4 | "She wore a navy wool" | | 5 | "Eva crossed the floorboards, stopped" | | 6 | "She set the keys on" | | 7 | "He didn't look up from" | | 8 | "He never looked up when" | | 9 | "He only ever looked up" | | 10 | "Silas asked, and Rory caught" | | 11 | "It was the same laugh," | | 12 | "Eva lifted her eyes" | | 13 | "Rory drank her gin." | | 14 | "It was the good stuff," | | 15 | "Eva gave a short, tight" | | 16 | "Rory set the glass down" | | 17 | "Silas moved to the far" | | 18 | "He had the sense to" | | 19 | "Eva looked at the rows" |
| | ratio | 0.905 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 42 | | matches | | 0 | "Now it was cropped close" |
| | ratio | 0.024 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "Her hair had been long and wild at seventeen, a cheap copper dye that bled onto every pillowcase at every sleepover in Llandaff." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.115 | | leniency | 0.231 | | rawRatio | 0 | | effectiveRatio | 0 | |