| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.089 | | leniency | 0.178 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1358 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 63.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1358 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flicked" | | 1 | "perfect" | | 2 | "etched" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "silence" |
| |
| 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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 190 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1360 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 1 | | matches | | 0 | "Behind her, Eva whispered." |
| |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 936 | | uniqueNames | 8 | | maxNameDensity | 2.24 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 21 | | Camden | 2 | | Market | 1 | | Davies | 11 | | Kowalski | 1 | | Eva | 11 | | Morris | 1 | | Tube | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Davies" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.378 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 52.94% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.471 | | wordCount | 1360 | | matches | | 0 | "not burned, but depressed" | | 1 | "not north, but directly down the tracks into the black tunnel mouth" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 190 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 88 | | mean | 15.45 | | std | 13.16 | | cv | 0.852 | | sampleLengths | | 0 | 17 | | 1 | 35 | | 2 | 37 | | 3 | 13 | | 4 | 1 | | 5 | 13 | | 6 | 3 | | 7 | 21 | | 8 | 39 | | 9 | 16 | | 10 | 12 | | 11 | 7 | | 12 | 14 | | 13 | 6 | | 14 | 18 | | 15 | 35 | | 16 | 3 | | 17 | 5 | | 18 | 7 | | 19 | 35 | | 20 | 27 | | 21 | 31 | | 22 | 2 | | 23 | 4 | | 24 | 4 | | 25 | 28 | | 26 | 3 | | 27 | 16 | | 28 | 38 | | 29 | 7 | | 30 | 41 | | 31 | 5 | | 32 | 18 | | 33 | 14 | | 34 | 13 | | 35 | 18 | | 36 | 29 | | 37 | 7 | | 38 | 6 | | 39 | 10 | | 40 | 16 | | 41 | 4 | | 42 | 8 | | 43 | 2 | | 44 | 25 | | 45 | 3 | | 46 | 6 | | 47 | 5 | | 48 | 3 | | 49 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 149 | | matches | | 0 | "been bricked" | | 1 | "been pushed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 190 | | ratio | 0.005 | | matches | | 0 | "\"— you can't be down here, this is a restricted — ma'am, ma'am!\"" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 715 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.01818181818181818 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0013986013986013986 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 190 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 190 | | mean | 7.16 | | std | 6.19 | | cv | 0.865 | | sampleLengths | | 0 | 11 | | 1 | 2 | | 2 | 2 | | 3 | 2 | | 4 | 18 | | 5 | 5 | | 6 | 12 | | 7 | 20 | | 8 | 4 | | 9 | 3 | | 10 | 4 | | 11 | 6 | | 12 | 7 | | 13 | 6 | | 14 | 1 | | 15 | 13 | | 16 | 3 | | 17 | 21 | | 18 | 7 | | 19 | 7 | | 20 | 2 | | 21 | 2 | | 22 | 5 | | 23 | 3 | | 24 | 6 | | 25 | 7 | | 26 | 6 | | 27 | 10 | | 28 | 2 | | 29 | 6 | | 30 | 4 | | 31 | 7 | | 32 | 14 | | 33 | 4 | | 34 | 2 | | 35 | 18 | | 36 | 3 | | 37 | 6 | | 38 | 10 | | 39 | 5 | | 40 | 8 | | 41 | 3 | | 42 | 3 | | 43 | 5 | | 44 | 7 | | 45 | 5 | | 46 | 11 | | 47 | 6 | | 48 | 13 | | 49 | 3 |
| |
| 51.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.35789473684210527 | | totalSentences | 190 | | uniqueOpeners | 68 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 120 | | matches | | 0 | "Then at the empty, clean" | | 1 | "Then at the tunnel mouth" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 120 | | matches | | 0 | "Her boots hit iron dust." | | 1 | "She knew the rumours." | | 2 | "She did not believe in" | | 3 | "Her left wrist caught the" | | 4 | "She did not look at" | | 5 | "She did not touch the" | | 6 | "She hovered her hand a" | | 7 | "She leaned in." | | 8 | "She let the sleeve drop." | | 9 | "He patted the coat." | | 10 | "He pulled out a small" | | 11 | "Its face was not north-south." | | 12 | "It snapped hard, pointing not" | | 13 | "She dropped the compass in." | | 14 | "She clutched a worn leather" | | 15 | "Her jaw tightened." | | 16 | "Her eyes jumped from Quinn" | | 17 | "It was not a question." | | 18 | "She did not touch." | | 19 | "She dropped to the track" |
| | ratio | 0.217 | |
| 51.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 120 | | matches | | 0 | "The call came as a" | | 1 | "Harlow Quinn ducked under the" | | 2 | "Her boots hit iron dust." | | 3 | "The air down here ran" | | 4 | "The skeleton of the station" | | 5 | "Sodium emergency lights buzzed." | | 6 | "The Market's bones." | | 7 | "She knew the rumours." | | 8 | "She did not believe in" | | 9 | "A uniformed constable stood near" | | 10 | "PC Davies, twenty-four, notebook too" | | 11 | "Quinn flicked her chin at" | | 12 | "The body lay centre-platform, as" | | 13 | "Hands folded over his chest." | | 14 | "Skin grey, but not from" | | 15 | "Davies flipped his notebook" | | 16 | "Her left wrist caught the" | | 17 | "The worn leather watch." | | 18 | "She did not look at" | | 19 | "Davies knelt beside her." |
| | ratio | 0.817 | |
| 41.67% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 120 | | matches | | 0 | "As if someone had laid" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 1 | | matches | | 0 | "Whispers of an underground market that sold enchanted goods, banned alchemical substances, information." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Davies muttered (mutter)" |
| | dialogueSentences | 45 | | tagDensity | 0.044 | | leniency | 0.089 | | rawRatio | 0.5 | | effectiveRatio | 0.044 | |