| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 841 | | totalAiIsmAdverbs | 2 | | 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) | |
| 88.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 841 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 59 | | matches | (empty) | |
| 70.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 841 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 757 | | uniqueNames | 9 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 8 | | Herrera | 4 | | Morris | 2 | | Tube | 1 | | Edwardian | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.972 | | windowScore | 0.833 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | 0 | "Something like burnt sugar and copper" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 841 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 67 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 29 | | std | 25.57 | | cv | 0.882 | | sampleLengths | | 0 | 73 | | 1 | 37 | | 2 | 3 | | 3 | 19 | | 4 | 81 | | 5 | 19 | | 6 | 106 | | 7 | 27 | | 8 | 13 | | 9 | 20 | | 10 | 13 | | 11 | 30 | | 12 | 5 | | 13 | 3 | | 14 | 32 | | 15 | 54 | | 16 | 29 | | 17 | 9 | | 18 | 80 | | 19 | 42 | | 20 | 17 | | 21 | 20 | | 22 | 17 | | 23 | 23 | | 24 | 15 | | 25 | 26 | | 26 | 13 | | 27 | 10 | | 28 | 5 |
| |
| 93.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 59 | | matches | | 0 | "was gone" | | 1 | "being unfolded" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 123 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 757 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.02774108322324967 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0066050198150594455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 12.55 | | std | 9.19 | | cv | 0.732 | | sampleLengths | | 0 | 28 | | 1 | 19 | | 2 | 6 | | 3 | 20 | | 4 | 2 | | 5 | 17 | | 6 | 7 | | 7 | 11 | | 8 | 3 | | 9 | 3 | | 10 | 13 | | 11 | 3 | | 12 | 18 | | 13 | 43 | | 14 | 10 | | 15 | 6 | | 16 | 4 | | 17 | 19 | | 18 | 16 | | 19 | 16 | | 20 | 21 | | 21 | 19 | | 22 | 34 | | 23 | 5 | | 24 | 16 | | 25 | 6 | | 26 | 5 | | 27 | 8 | | 28 | 20 | | 29 | 13 | | 30 | 8 | | 31 | 12 | | 32 | 10 | | 33 | 3 | | 34 | 2 | | 35 | 3 | | 36 | 4 | | 37 | 2 | | 38 | 26 | | 39 | 12 | | 40 | 3 | | 41 | 10 | | 42 | 29 | | 43 | 29 | | 44 | 9 | | 45 | 8 | | 46 | 15 | | 47 | 23 | | 48 | 34 | | 49 | 15 |
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| 81.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5223880597014925 | | totalSentences | 67 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 79.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 54 | | matches | | 0 | "She had watched him for" | | 1 | "She had never once gotten" | | 2 | "He glanced back." | | 3 | "Their eyes met across twenty" | | 4 | "He ran harder." | | 5 | "She checked it on instinct," | | 6 | "She had never found out" | | 7 | "She had stopped asking." | | 8 | "She reached the corner in" | | 9 | "She pulled out her radio." | | 10 | "She looked at the gap" | | 11 | "It seemed to hold depth," | | 12 | "She lowered the radio." | | 13 | "She thought of his face" | | 14 | "She pushed the steel door" | | 15 | "Her torch picked out tiled" | | 16 | "She looked up at Quinn" | | 17 | "She could already tell." | | 18 | "She could wait for it." |
| | ratio | 0.352 | |
| 71.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 54 | | matches | | 0 | "The rain had turned Camden" | | 1 | "A quick, economical stride, the" | | 2 | "She had watched him for" | | 3 | "She had never once gotten" | | 4 | "He glanced back." | | 5 | "Their eyes met across twenty" | | 6 | "He ran harder." | | 7 | "Quinn's left wrist ached with" | | 8 | "She checked it on instinct," | | 9 | "The watch had stopped that" | | 10 | "She had never found out" | | 11 | "She had stopped asking." | | 12 | "Herrera swerved off the main" | | 13 | "She reached the corner in" | | 14 | "The lane was narrow and" | | 15 | "The steel door stood ajar." | | 16 | "Something like burnt sugar and" | | 17 | "She pulled out her radio." | | 18 | "The static answered her before" | | 19 | "She looked at the gap" |
| | ratio | 0.778 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "Former paramedic, struck off, currently a ghost who stitched up people the city's records didn't admit existed." | | 1 | "The lane was narrow and empty, lit by a single sodium lamp that buzzed and flickered." | | 2 | "At the far end, a row of iron railings ran along a stretch of pavement that had no business being there." | | 3 | "She thought of his face in the last clear image she had of him, grinning at something she'd said, his coat dripping in the doorway of an evidence store." | | 4 | "She looked up at Quinn with the flat, unhurried calm of someone who had seen many people arrive here and most of them leave in a hurry." |
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| 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 | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |