| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.169 | | leniency | 0.339 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2000 | | 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) | |
| 87.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2000 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "traced" | | 1 | "measured" | | 2 | "quivered" | | 3 | "silence" | | 4 | "echoes" |
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| 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 | 168 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 218 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2000 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1566 | | uniqueNames | 11 | | maxNameDensity | 1.15 | | worstName | "Reeves" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Camden | 1 | | Harlow | 17 | | Reeves | 18 | | Limehouse | 1 | | Morris | 2 | | Museum | 1 | | Archives | 1 | | Eva | 1 | | Kowalski | 1 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Harlow" | | 2 | "Reeves" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.925 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.5 | | wordCount | 2000 | | matches | | 0 | "not burnt by flame licking up from below but marked in a band, as if something hot" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 218 | | matches | | 0 | "thrown that arc" | | 1 | "shut that door" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 108 | | mean | 18.52 | | std | 20.93 | | cv | 1.13 | | sampleLengths | | 0 | 16 | | 1 | 1 | | 2 | 33 | | 3 | 2 | | 4 | 5 | | 5 | 72 | | 6 | 12 | | 7 | 79 | | 8 | 55 | | 9 | 8 | | 10 | 11 | | 11 | 4 | | 12 | 7 | | 13 | 5 | | 14 | 11 | | 15 | 69 | | 16 | 13 | | 17 | 63 | | 18 | 6 | | 19 | 47 | | 20 | 6 | | 21 | 5 | | 22 | 93 | | 23 | 16 | | 24 | 9 | | 25 | 18 | | 26 | 52 | | 27 | 5 | | 28 | 8 | | 29 | 8 | | 30 | 29 | | 31 | 4 | | 32 | 5 | | 33 | 1 | | 34 | 3 | | 35 | 87 | | 36 | 19 | | 37 | 3 | | 38 | 4 | | 39 | 4 | | 40 | 11 | | 41 | 1 | | 42 | 28 | | 43 | 12 | | 44 | 40 | | 45 | 3 | | 46 | 14 | | 47 | 53 | | 48 | 13 | | 49 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 168 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 252 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 218 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1398 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same sweet-burnt scent" |
| | adverbCount | 35 | | adverbRatio | 0.02503576537911302 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001430615164520744 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 218 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 218 | | mean | 9.17 | | std | 6.96 | | cv | 0.759 | | sampleLengths | | 0 | 16 | | 1 | 1 | | 2 | 17 | | 3 | 16 | | 4 | 2 | | 5 | 5 | | 6 | 17 | | 7 | 4 | | 8 | 2 | | 9 | 32 | | 10 | 17 | | 11 | 4 | | 12 | 1 | | 13 | 7 | | 14 | 19 | | 15 | 11 | | 16 | 24 | | 17 | 4 | | 18 | 5 | | 19 | 16 | | 20 | 19 | | 21 | 19 | | 22 | 17 | | 23 | 8 | | 24 | 11 | | 25 | 4 | | 26 | 7 | | 27 | 5 | | 28 | 5 | | 29 | 6 | | 30 | 14 | | 31 | 6 | | 32 | 6 | | 33 | 6 | | 34 | 11 | | 35 | 14 | | 36 | 12 | | 37 | 13 | | 38 | 16 | | 39 | 5 | | 40 | 3 | | 41 | 1 | | 42 | 11 | | 43 | 12 | | 44 | 15 | | 45 | 6 | | 46 | 19 | | 47 | 28 | | 48 | 6 | | 49 | 5 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.42201834862385323 | | totalSentences | 218 | | uniqueOpeners | 92 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 146 | | matches | | 0 | "Even the forensic lamps gave" | | 1 | "Then something inside clicked." |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 146 | | matches | | 0 | "He flushed and stepped where" | | 1 | "She crouched, leather watch strap" | | 2 | "He had the heavy face" | | 3 | "His back touched the stained" | | 4 | "His chin sagged onto his" | | 5 | "His hands rested on his" | | 6 | "It had struck from left" | | 7 | "She saw dozens of overlapping" | | 8 | "Their toes shone." | | 9 | "Their soles held no station" | | 10 | "She looked up at Reeves." | | 11 | "He wore that faint smirk" | | 12 | "She held the torch low" | | 13 | "She pressed a gloved finger" | | 14 | "She pointed with her torch" | | 15 | "She shut that door at" | | 16 | "He gestured upward." | | 17 | "She walked three paces left." | | 18 | "She turned her back to" | | 19 | "She handed the pass back." |
| | ratio | 0.178 | |
| 96.99% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 146 | | matches | | 0 | "Harlow Quinn caught the constable" | | 1 | "The young man froze on" | | 2 | "He flushed and stepped where" | | 3 | "She crouched, leather watch strap" | | 4 | "A narrow sole with a" | | 5 | "Someone had turned here." | | 6 | "Someone who had not expected" | | 7 | "The platform had once sold" | | 8 | "The place smelt wrong." | | 9 | "Reeves waited beside the body" | | 10 | "He had the heavy face" | | 11 | "A SOCO photographed the victim’s" | | 12 | "Harlow descended the rest of" | | 13 | "Reeves huffed through his nose." | | 14 | "That counted as amusement from" | | 15 | "The victim sat on an" | | 16 | "Someone had arranged him with" | | 17 | "His back touched the stained" | | 18 | "His chin sagged onto his" | | 19 | "A dark bloom covered the" |
| | ratio | 0.726 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 146 | | matches | | 0 | "Now flaked enamel signs peeled" | | 1 | "If his wound had thrown" | | 2 | "Whoever had brought him to" | | 3 | "If it points wrong, you’re" |
| | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 2 | | matches | | 0 | "She saw dozens of overlapping prints, boot soles, narrow heels, a child’s trainer, a tiny pointed shoe with a split toe, and wheel ruts from small carts that ha…" | | 1 | "The wool around the cuff had browned in a sharp ring, not burnt by flame licking up from below but marked in a band, as if something hot and narrow had clasped …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.119 | | leniency | 0.237 | | rawRatio | 0 | | effectiveRatio | 0 | |