| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said softly [softly]" | | 1 | "she said finally [finally]" |
| | dialogueSentences | 26 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.154 | | effectiveRatio | 0.154 | |
| 77.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1102 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "softly" | | 1 | "deliberately" | | 2 | "slightly" | | 3 | "really" |
| |
| 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) | |
| 50.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1102 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "jaw clenched" | | 1 | "tension" | | 2 | "silk" | | 3 | "flicker" | | 4 | "wavering" | | 5 | "eyebrow" | | 6 | "scanned" | | 7 | "fractured" | | 8 | "etched" | | 9 | "weight" | | 10 | "shattered" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1101 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 870 | | uniqueNames | 10 | | maxNameDensity | 2.18 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Veil | 3 | | Market | 1 | | Harlow | 19 | | Kowalski | 2 | | British | 1 | | Museum | 1 | | Eva | 18 | | Confused | 1 | | Compass | 1 |
| | persons | | 0 | "Veil" | | 1 | "Market" | | 2 | "Harlow" | | 3 | "Kowalski" | | 4 | "Museum" | | 5 | "Eva" | | 6 | "Compass" |
| | places | (empty) | | globalScore | 0.408 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 18.35% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.817 | | wordCount | 1101 | | matches | | 0 | "not so much in indifference but resignation" | | 1 | "Not the conclusion I would have jumped to,\" she said, \"but you carry the weight, detective" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 32.38 | | std | 19.77 | | cv | 0.611 | | sampleLengths | | 0 | 87 | | 1 | 59 | | 2 | 47 | | 3 | 74 | | 4 | 55 | | 5 | 52 | | 6 | 22 | | 7 | 35 | | 8 | 15 | | 9 | 9 | | 10 | 13 | | 11 | 17 | | 12 | 17 | | 13 | 38 | | 14 | 52 | | 15 | 12 | | 16 | 13 | | 17 | 16 | | 18 | 48 | | 19 | 11 | | 20 | 13 | | 21 | 56 | | 22 | 26 | | 23 | 29 | | 24 | 28 | | 25 | 13 | | 26 | 29 | | 27 | 23 | | 28 | 20 | | 29 | 30 | | 30 | 48 | | 31 | 15 | | 32 | 55 | | 33 | 24 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 156 | | matches | | |
| 81.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 93 | | ratio | 0.022 | | matches | | 0 | "She recognized the satchel slung across the woman's freckled shoulder; she recognized the red frizz of disorderly curls." | | 1 | "The evidence told a different story, and she needed to listen—really listen this time." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 872 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.034403669724770644 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.014908256880733946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 11.84 | | std | 6.23 | | cv | 0.526 | | sampleLengths | | 0 | 16 | | 1 | 16 | | 2 | 16 | | 3 | 25 | | 4 | 14 | | 5 | 15 | | 6 | 3 | | 7 | 7 | | 8 | 22 | | 9 | 4 | | 10 | 8 | | 11 | 20 | | 12 | 14 | | 13 | 13 | | 14 | 20 | | 15 | 18 | | 16 | 16 | | 17 | 20 | | 18 | 21 | | 19 | 17 | | 20 | 2 | | 21 | 15 | | 22 | 20 | | 23 | 12 | | 24 | 6 | | 25 | 14 | | 26 | 14 | | 27 | 8 | | 28 | 5 | | 29 | 7 | | 30 | 8 | | 31 | 15 | | 32 | 15 | | 33 | 4 | | 34 | 5 | | 35 | 10 | | 36 | 3 | | 37 | 17 | | 38 | 3 | | 39 | 14 | | 40 | 9 | | 41 | 26 | | 42 | 3 | | 43 | 11 | | 44 | 10 | | 45 | 17 | | 46 | 14 | | 47 | 5 | | 48 | 7 | | 49 | 4 |
| |
| 76.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4838709677419355 | | totalSentences | 93 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 76 | | matches | | 0 | "Of course this woman would" | | 1 | "Perhaps there were solutions yet" | | 2 | "perhaps a new way forward." |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 76 | | matches | | 0 | "It leant on itself, a" | | 1 | "She slipped in unseen with" | | 2 | "She recognized the satchel slung" | | 3 | "She stalked through the murk," | | 4 | "she said softly, but her" | | 5 | "She stepped out of the" | | 6 | "she replied, keeping her voice" | | 7 | "She tugged her frizzing hair," | | 8 | "Her nervous habit." | | 9 | "she said, leading Harlow past" | | 10 | "she said, almost apologetic" | | 11 | "She tasted wet pennies, felt" | | 12 | "She forced herself to nod." | | 13 | "she said finally" | | 14 | "she said at last" | | 15 | "It stretched unfamiliar muscles, but" | | 16 | "They stood a moment in" | | 17 | "She tucked the compass deep" |
| | ratio | 0.237 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 76 | | matches | | 0 | "Harlow stepped off the broken" | | 1 | "The air was stale, thick" | | 2 | "A miasma like wet wool" | | 3 | "The Veil Market." | | 4 | "Stalls of mossy brickwork and" | | 5 | "It leant on itself, a" | | 6 | "The Veil had been the" | | 7 | "She slipped in unseen with" | | 8 | "She recognized the satchel slung" | | 9 | "Eva Kowalski, a longtime fixture" | | 10 | "Eva approached a vendor hunkered" | | 11 | "Eva's head tilted, her round" | | 12 | "Harlow decided to follow, and" | | 13 | "She stalked through the murk," | | 14 | "The researcher waited in the" | | 15 | "she said softly, but her" | | 16 | "Harlow bit off a huff." | | 17 | "She stepped out of the" | | 18 | "she replied, keeping her voice" | | 19 | "Eva cocked an eyebrow." |
| | ratio | 0.671 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "If she was asking Harlow" |
| | ratio | 0.013 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "Curious, Harlow kept her distance, pretending to inspect the shimmering bolts of enchanted spider silk displayed on one rotting stall." | | 1 | "Harlow's frown deepened, her eyes flicking over Eva's face, seeking deceit." | | 2 | "Eva Kowalski was notorious for her meticulous research into areas that would make even seasoned veterans balk." |
| |
| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "she said softly, but her voice carried in the quiet" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 26 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0.1 | | effectiveRatio | 0.077 | |