| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.479 | | leniency | 0.958 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1403 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "completely" |
| |
| 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) | |
| 50.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1403 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "shimmered" | | 1 | "warmth" | | 2 | "etched" | | 3 | "velvet" | | 4 | "standard" | | 5 | "traced" | | 6 | "resonated" | | 7 | "racing" | | 8 | "silence" | | 9 | "weight" | | 10 | "echoing" | | 11 | "gloom" | | 12 | "pulse" |
| |
| 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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1403 | | 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 | 52 | | wordCount | 999 | | uniqueNames | 11 | | maxNameDensity | 2.3 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Tube | 1 | | Harlow | 23 | | Quinn | 2 | | Metropolitan | 1 | | Police | 1 | | Morris | 2 | | Eva | 15 | | Kowalski | 1 | | Veil | 1 | | Compass | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Compass" |
| | places | (empty) | | globalScore | 0.349 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like oil, but it didn't reflect th" |
| |
| 57.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.426 | | wordCount | 1403 | | matches | | 0 | "not with water, but with a viscous residue" | | 1 | "Not toward the tunnel, but toward the wall behind Harlow" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 33.4 | | std | 22.66 | | cv | 0.678 | | sampleLengths | | 0 | 87 | | 1 | 57 | | 2 | 14 | | 3 | 72 | | 4 | 73 | | 5 | 18 | | 6 | 23 | | 7 | 27 | | 8 | 46 | | 9 | 3 | | 10 | 33 | | 11 | 51 | | 12 | 35 | | 13 | 50 | | 14 | 4 | | 15 | 30 | | 16 | 20 | | 17 | 43 | | 18 | 12 | | 19 | 4 | | 20 | 54 | | 21 | 1 | | 22 | 18 | | 23 | 40 | | 24 | 37 | | 25 | 2 | | 26 | 38 | | 27 | 4 | | 28 | 73 | | 29 | 3 | | 30 | 45 | | 31 | 24 | | 32 | 61 | | 33 | 17 | | 34 | 32 | | 35 | 29 | | 36 | 68 | | 37 | 18 | | 38 | 23 | | 39 | 30 | | 40 | 19 | | 41 | 65 |
| |
| 95.61% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 109 | | matches | | 0 | "was frayed" | | 1 | "was tied" | | 2 | "was closed" |
| |
| 49.34% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 177 | | matches | | 0 | "were whispering" | | 1 | "wasn't pointing" | | 2 | "was spinning" | | 3 | "was vibrating" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 134 | | ratio | 0.007 | | matches | | 0 | "It looked like oil, but it didn't reflect the light correctly; it swallowed it." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 999 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.025025025025025027 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.012012012012012012 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.47 | | std | 6.93 | | cv | 0.662 | | sampleLengths | | 0 | 28 | | 1 | 23 | | 2 | 22 | | 3 | 14 | | 4 | 4 | | 5 | 28 | | 6 | 4 | | 7 | 2 | | 8 | 19 | | 9 | 8 | | 10 | 6 | | 11 | 24 | | 12 | 16 | | 13 | 16 | | 14 | 3 | | 15 | 13 | | 16 | 17 | | 17 | 17 | | 18 | 19 | | 19 | 20 | | 20 | 8 | | 21 | 10 | | 22 | 3 | | 23 | 7 | | 24 | 10 | | 25 | 3 | | 26 | 6 | | 27 | 11 | | 28 | 10 | | 29 | 8 | | 30 | 16 | | 31 | 4 | | 32 | 3 | | 33 | 2 | | 34 | 13 | | 35 | 3 | | 36 | 10 | | 37 | 23 | | 38 | 8 | | 39 | 12 | | 40 | 9 | | 41 | 6 | | 42 | 7 | | 43 | 9 | | 44 | 16 | | 45 | 15 | | 46 | 4 | | 47 | 11 | | 48 | 3 | | 49 | 17 |
| |
| 49.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.34328358208955223 | | totalSentences | 134 | | uniqueOpeners | 46 | |
| 73.26% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 91 | | matches | | 0 | "Too late for a normal" | | 1 | "Just the outline of a" |
| | ratio | 0.022 | |
| 74.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 91 | | matches | | 0 | "She adjusted the collar of" | | 1 | "It looked like oil, but" | | 2 | "She tapped the face." | | 3 | "They were whispering, eyes darting" | | 4 | "She needed order." | | 5 | "She found Eva Kowalski crouched" | | 6 | "She adjusted her round glasses," | | 7 | "Her voice was tight, stripped" | | 8 | "She stopped a few feet" | | 9 | "She finally looked up, her" | | 10 | "She pulled a latex glove" | | 11 | "She reached out, hovering her" | | 12 | "She reached into her worn" | | 13 | "She had confiscated it from" | | 14 | "It was spinning slowly, erratically," | | 15 | "She turned to the wall." | | 16 | "It was standard brick, covered" | | 17 | "She looked at the spinning" | | 18 | "It was vibrating now, a" | | 19 | "She ran her gloved hand" |
| | ratio | 0.363 | |
| 47.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 91 | | matches | | 0 | "The air in the abandoned" | | 1 | "She adjusted the collar of" | | 2 | "The concrete floor here was" | | 3 | "It looked like oil, but" | | 4 | "Harlow checked her watch." | | 5 | "The worn leather strap was" | | 6 | "She tapped the face." | | 7 | "They were whispering, eyes darting" | | 8 | "Harlow pushed past them, her" | | 9 | "She needed order." | | 10 | "Order was what kept the" | | 11 | "She found Eva Kowalski crouched" | | 12 | "Eva’s curly red hair was" | | 13 | "She adjusted her round glasses," | | 14 | "The nervous habit was there," | | 15 | "Eva said, not looking up" | | 16 | "Her voice was tight, stripped" | | 17 | "She stopped a few feet" | | 18 | "She finally looked up, her" | | 19 | "Harlow frowned, stepping closer to" |
| | ratio | 0.824 | |
| 54.95% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 91 | | matches | | 0 | "Now, standing in the dark" |
| | ratio | 0.011 | |
| 17.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 7 | | matches | | 0 | "The air in the abandoned Camden Tube station tasted of copper and old rain, a flavor that coated the back of Harlow Quinn’s throat like a bad memory." | | 1 | "She adjusted the collar of her trench coat, the wool scratchy against her neck, and stepped over a threshold that shouldn't have existed." | | 2 | "The concrete floor here was slick, not with water, but with a viscous residue that shimmered under the beam of her flashlight." | | 3 | "The worn leather strap was frayed at the edges, a relic from a different life before the Metropolitan Police, before the job that had eaten DS Morris alive." | | 4 | "Harlow pushed past them, her boots clicking on the tiles with a rhythm that demanded order." | | 5 | "Brass casing, verdigris patina, etched with sigils that made her eyes water if she stared too long." | | 6 | "It was vibrating now, a low hum that resonated in her teeth." |
| |
| 59.78% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 3 | | matches | | 0 | "Eva said, not looking up" | | 1 | "Harlow corrected, her voice low" | | 2 | "Eva called out, her voice echoing off the tiles" |
| |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 4 | | fancyTags | | 0 | "Harlow lied (lie)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow corrected (correct)" | | 3 | "Eva called out (call out)" |
| | dialogueSentences | 48 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.25 | | effectiveRatio | 0.167 | |