| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1162 | | 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) | |
| 52.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1162 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "stark" | | 1 | "gloom" | | 2 | "echoed" | | 3 | "intricate" | | 4 | "glint" | | 5 | "dancing" | | 6 | "weight" | | 7 | "crystal" | | 8 | "chill" | | 9 | "footsteps" | | 10 | "shattered" |
| |
| 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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | 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 | 1162 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 958 | | uniqueNames | 14 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | London | 1 | | West | 1 | | End | 1 | | Quinn | 14 | | Miller | 8 | | Underground | 1 | | Whitechapel | 1 | | Morris | 1 | | Chalk | 1 | | Bovril | 1 | | Victorian | 1 | | Aurora | 1 | | Shade | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Miller" | | 2 | "Morris" | | 3 | "Aurora" |
| | places | | | globalScore | 0.769 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1162 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 23.71 | | std | 18.96 | | cv | 0.8 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 54 | | 3 | 64 | | 4 | 26 | | 5 | 4 | | 6 | 2 | | 7 | 44 | | 8 | 50 | | 9 | 7 | | 10 | 7 | | 11 | 8 | | 12 | 6 | | 13 | 8 | | 14 | 7 | | 15 | 45 | | 16 | 63 | | 17 | 54 | | 18 | 9 | | 19 | 5 | | 20 | 3 | | 21 | 26 | | 22 | 3 | | 23 | 12 | | 24 | 46 | | 25 | 46 | | 26 | 3 | | 27 | 63 | | 28 | 6 | | 29 | 46 | | 30 | 3 | | 31 | 36 | | 32 | 44 | | 33 | 36 | | 34 | 7 | | 35 | 12 | | 36 | 5 | | 37 | 18 | | 38 | 39 | | 39 | 14 | | 40 | 45 | | 41 | 22 | | 42 | 21 | | 43 | 13 | | 44 | 24 | | 45 | 12 | | 46 | 11 | | 47 | 23 | | 48 | 23 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 970 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.020618556701030927 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002061855670103093 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 11.07 | | std | 6.87 | | cv | 0.621 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 11 | | 3 | 15 | | 4 | 6 | | 5 | 22 | | 6 | 10 | | 7 | 18 | | 8 | 21 | | 9 | 15 | | 10 | 15 | | 11 | 11 | | 12 | 4 | | 13 | 2 | | 14 | 13 | | 15 | 31 | | 16 | 8 | | 17 | 10 | | 18 | 7 | | 19 | 1 | | 20 | 16 | | 21 | 8 | | 22 | 7 | | 23 | 7 | | 24 | 8 | | 25 | 6 | | 26 | 8 | | 27 | 7 | | 28 | 10 | | 29 | 1 | | 30 | 19 | | 31 | 15 | | 32 | 5 | | 33 | 7 | | 34 | 22 | | 35 | 8 | | 36 | 21 | | 37 | 5 | | 38 | 6 | | 39 | 6 | | 40 | 21 | | 41 | 4 | | 42 | 12 | | 43 | 9 | | 44 | 5 | | 45 | 3 | | 46 | 17 | | 47 | 9 | | 48 | 3 | | 49 | 12 |
| |
| 97.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.7047619047619048 | | totalSentences | 105 | | uniqueOpeners | 74 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Away from the platform edge," |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 74 | | matches | | 0 | "She marched down the disused" | | 1 | "Her heavy boots echoed against" | | 2 | "He stood as she approached," | | 3 | "She clasped her hands behind" | | 4 | "She dropped to one haunch." | | 5 | "Her knees popped in the" | | 6 | "It dragged a phantom ache" | | 7 | "She leaned over the corpse." | | 8 | "He yanked his hand back," | | 9 | "She bagged it and slipped" | | 10 | "She turned back to the" | | 11 | "She wedged the flat steel" | | 12 | "She applied steady leverage until" |
| | ratio | 0.176 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 74 | | matches | | 0 | "Quinn snapped a pair of" | | 1 | "A forensic camera flashed ahead," | | 2 | "She marched down the disused" | | 3 | "Her heavy boots echoed against" | | 4 | "Scuff marks marred the grime" | | 5 | "Generators hummed near the tunnel" | | 6 | "DS Miller crouched beside the" | | 7 | "He stood as she approached," | | 8 | "Miller pointed his pen toward" | | 9 | "Quinn halted at the edge" | | 10 | "She clasped her hands behind" | | 11 | "The victim lay twisted on" | | 12 | "Bespoke charcoal overcoat, tailored wool" | | 13 | "Quinn checked the worn leather" | | 14 | "The call had come in" | | 15 | "She dropped to one haunch." | | 16 | "Her knees popped in the" | | 17 | "It dragged a phantom ache" | | 18 | "She leaned over the corpse." | | 19 | "The man's eyes bulged, capillaries" |
| | ratio | 0.689 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Miller leaned, breath misting in the chill surrounding the corpse" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.074 | | leniency | 0.148 | | rawRatio | 0 | | effectiveRatio | 0 | |