| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 74 | | tagDensity | 0.162 | | leniency | 0.324 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1529 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 96.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1529 | | totalAiIsms | 1 | | 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 | 116 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 116 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 178 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1529 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 7.69% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1054 | | uniqueNames | 9 | | maxNameDensity | 2.85 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 30 | | Tube | 1 | | Detective | 2 | | Sergeant | 1 | | Nikhil | 1 | | Patel | 15 | | Kowalski | 1 | | Eva | 16 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Nikhil" | | 4 | "Patel" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0.077 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | 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 | 1529 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 178 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 96 | | mean | 15.93 | | std | 15.97 | | cv | 1.003 | | sampleLengths | | 0 | 43 | | 1 | 30 | | 2 | 5 | | 3 | 12 | | 4 | 28 | | 5 | 26 | | 6 | 4 | | 7 | 18 | | 8 | 7 | | 9 | 79 | | 10 | 4 | | 11 | 29 | | 12 | 4 | | 13 | 21 | | 14 | 10 | | 15 | 5 | | 16 | 61 | | 17 | 9 | | 18 | 2 | | 19 | 4 | | 20 | 8 | | 21 | 13 | | 22 | 39 | | 23 | 16 | | 24 | 32 | | 25 | 3 | | 26 | 11 | | 27 | 2 | | 28 | 7 | | 29 | 47 | | 30 | 3 | | 31 | 36 | | 32 | 10 | | 33 | 8 | | 34 | 3 | | 35 | 1 | | 36 | 18 | | 37 | 17 | | 38 | 40 | | 39 | 5 | | 40 | 20 | | 41 | 3 | | 42 | 8 | | 43 | 5 | | 44 | 27 | | 45 | 11 | | 46 | 28 | | 47 | 7 | | 48 | 9 | | 49 | 2 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 178 | | ratio | 0.006 | | matches | | 0 | "The man’s left arm lay beneath his chest; his right hand curled against the tiles." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1055 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.014218009478672985 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009478672985781991 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 178 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 178 | | mean | 8.59 | | std | 5.21 | | cv | 0.607 | | sampleLengths | | 0 | 13 | | 1 | 11 | | 2 | 19 | | 3 | 15 | | 4 | 15 | | 5 | 5 | | 6 | 12 | | 7 | 11 | | 8 | 4 | | 9 | 8 | | 10 | 5 | | 11 | 11 | | 12 | 15 | | 13 | 4 | | 14 | 18 | | 15 | 5 | | 16 | 2 | | 17 | 7 | | 18 | 16 | | 19 | 11 | | 20 | 15 | | 21 | 11 | | 22 | 19 | | 23 | 4 | | 24 | 21 | | 25 | 8 | | 26 | 4 | | 27 | 21 | | 28 | 10 | | 29 | 5 | | 30 | 18 | | 31 | 6 | | 32 | 12 | | 33 | 10 | | 34 | 15 | | 35 | 6 | | 36 | 3 | | 37 | 2 | | 38 | 4 | | 39 | 3 | | 40 | 5 | | 41 | 13 | | 42 | 7 | | 43 | 2 | | 44 | 15 | | 45 | 10 | | 46 | 5 | | 47 | 10 | | 48 | 6 | | 49 | 8 |
| |
| 63.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.38764044943820225 | | totalSentences | 178 | | uniqueOpeners | 69 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 106 | | matches | | 0 | "Its owner had left the" | | 1 | "He pushed the bone into" | | 2 | "Her colleague, Detective Sergeant Nikhil" | | 3 | "His shoes stood in a" | | 4 | "Their counters stood under striped" | | 5 | "She lifted the coat’s edge" | | 6 | "His lips had turned a" | | 7 | "she told Patel" | | 8 | "Her round glasses caught the" | | 9 | "Her shoes tapped over tiles" | | 10 | "She stopped at a bare" | | 11 | "Its fingers held a trace" | | 12 | "She drew out a folded" | | 13 | "Its needle pointed across the" | | 14 | "She walked three paces towards" | | 15 | "She retraced her steps and" | | 16 | "It turned again." | | 17 | "It ran from the spot" | | 18 | "She reached towards it, then" | | 19 | "He moved to call the" |
| | ratio | 0.189 | |
| 35.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 106 | | matches | | 0 | "Detective Harlow Quinn found the" | | 1 | "Its owner had left the" | | 2 | "A constable stood beside it" | | 3 | "He pushed the bone into" | | 4 | "The gate clicked open." | | 5 | "Quinn glanced at the rust" | | 6 | "Her colleague, Detective Sergeant Nikhil" | | 7 | "His shoes stood in a" | | 8 | "Patel pointed down the platform." | | 9 | "Quinn passed a row of" | | 10 | "Their counters stood under striped" | | 11 | "Someone had left a cup" | | 12 | "Steam no longer rose from" | | 13 | "A bowl of black fruit" | | 14 | "Patel kept pace with her" | | 15 | "The body lay between a" | | 16 | "A man in his forties," | | 17 | "A narrow slit marked the" | | 18 | "Someone had drawn a chalk" | | 19 | "Quinn stopped short of the" |
| | ratio | 0.849 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn found the entrance beneath a shop that sold second-hand records." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 74 | | tagDensity | 0.122 | | leniency | 0.243 | | rawRatio | 0 | | effectiveRatio | 0 | |