| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 72 | | tagDensity | 0.097 | | leniency | 0.194 | | rawRatio | 0.143 | | effectiveRatio | 0.028 | |
| 97.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1940 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 84.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1940 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "measured" | | 1 | "warmth" | | 2 | "pulse" | | 3 | "weight" | | 4 | "flicked" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 179 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 179 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 243 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1940 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1448 | | uniqueNames | 9 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Mercer" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Camden | 1 | | Underground | 1 | | Lyle | 1 | | Mercer | 19 | | Morris | 2 | | Vale | 6 | | Gareth | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Lyle" | | 3 | "Mercer" | | 4 | "Morris" | | 5 | "Vale" | | 6 | "Gareth" |
| | places | (empty) | | globalScore | 0.775 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | 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 | 1940 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 243 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 158 | | mean | 12.28 | | std | 13.08 | | cv | 1.065 | | sampleLengths | | 0 | 12 | | 1 | 4 | | 2 | 40 | | 3 | 12 | | 4 | 6 | | 5 | 7 | | 6 | 59 | | 7 | 7 | | 8 | 51 | | 9 | 7 | | 10 | 13 | | 11 | 21 | | 12 | 1 | | 13 | 37 | | 14 | 35 | | 15 | 3 | | 16 | 24 | | 17 | 3 | | 18 | 4 | | 19 | 35 | | 20 | 5 | | 21 | 9 | | 22 | 4 | | 23 | 6 | | 24 | 27 | | 25 | 2 | | 26 | 9 | | 27 | 4 | | 28 | 1 | | 29 | 42 | | 30 | 5 | | 31 | 2 | | 32 | 28 | | 33 | 3 | | 34 | 10 | | 35 | 5 | | 36 | 11 | | 37 | 12 | | 38 | 2 | | 39 | 18 | | 40 | 5 | | 41 | 16 | | 42 | 43 | | 43 | 5 | | 44 | 1 | | 45 | 51 | | 46 | 13 | | 47 | 5 | | 48 | 1 | | 49 | 56 |
| |
| 99.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 179 | | matches | | 0 | "been scuffed" | | 1 | "been swept" | | 2 | "been rubbed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 226 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 243 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1455 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.01993127147766323 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003436426116838488 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 243 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 243 | | mean | 7.98 | | std | 5.82 | | cv | 0.729 | | sampleLengths | | 0 | 12 | | 1 | 4 | | 2 | 10 | | 3 | 16 | | 4 | 2 | | 5 | 2 | | 6 | 3 | | 7 | 7 | | 8 | 12 | | 9 | 6 | | 10 | 7 | | 11 | 17 | | 12 | 16 | | 13 | 26 | | 14 | 7 | | 15 | 24 | | 16 | 6 | | 17 | 21 | | 18 | 2 | | 19 | 5 | | 20 | 13 | | 21 | 4 | | 22 | 17 | | 23 | 1 | | 24 | 6 | | 25 | 31 | | 26 | 12 | | 27 | 23 | | 28 | 3 | | 29 | 24 | | 30 | 3 | | 31 | 4 | | 32 | 9 | | 33 | 8 | | 34 | 8 | | 35 | 10 | | 36 | 5 | | 37 | 9 | | 38 | 4 | | 39 | 6 | | 40 | 27 | | 41 | 2 | | 42 | 9 | | 43 | 4 | | 44 | 1 | | 45 | 3 | | 46 | 11 | | 47 | 11 | | 48 | 2 | | 49 | 2 |
| |
| 48.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.345679012345679 | | totalSentences | 243 | | uniqueOpeners | 84 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 156 | | matches | | 0 | "Only a faint grit beneath" | | 1 | "Then the lights had burst." | | 2 | "Then it stopped." |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 156 | | matches | | 0 | "Her boot had already settled" | | 1 | "Their light caught the dead" | | 2 | "He had died on his" | | 3 | "His throat bore a narrow" | | 4 | "Her leather watch pressed against" | | 5 | "He was a narrow man" | | 6 | "His fingernails carried dark material," | | 7 | "It glittered in the forensic" | | 8 | "She held out her hand." | | 9 | "It clung to the plastic" | | 10 | "She watched the fleck tremble" | | 11 | "It slid across the bag," | | 12 | "She folded the bag shut." | | 13 | "He had turned to the" | | 14 | "It lay thick along the" | | 15 | "She ignored Mercer." | | 16 | "It started at the bench" | | 17 | "It had not been swept." | | 18 | "They resembled letters, but no" | | 19 | "She reached towards the nearest" |
| | ratio | 0.205 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 156 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "The warning came from behind" | | 2 | "Her boot had already settled" | | 3 | "Quinn looked down at it," | | 4 | "The constable swallowed and lifted" | | 5 | "The disused platform lay beneath" | | 6 | "A line of empty globe" | | 7 | "Their light caught the dead" | | 8 | "He had died on his" | | 9 | "A silver coin covered each" | | 10 | "His throat bore a narrow" | | 11 | "Quinn crouched beside him." | | 12 | "Her leather watch pressed against" | | 13 | "Mercer stood near the tracks" | | 14 | "He was a narrow man" | | 15 | "Quinn glanced towards the far" | | 16 | "A green-painted iron gate blocked" | | 17 | "A cheap brass padlock hung" | | 18 | "Mercer watched her study the" | | 19 | "Quinn leaned closer." |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 2 | | matches | | 0 | "Between them, a narrow drainage channel held clotted leaves, cigarette filters, a child’s plastic dinosaur, and water that did not flow downhill." | | 1 | "A woman singing something in a language that made Quinn’s scalp tighten." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 72 | | tagDensity | 0.097 | | leniency | 0.194 | | rawRatio | 0 | | effectiveRatio | 0 | |