| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1225 | | 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) | |
| 95.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1225 | | totalAiIsms | 1 | | 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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1225 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1102 | | uniqueNames | 20 | | maxNameDensity | 1 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Herrera | 7 | | Wardour | 1 | | Street | 1 | | Old | 2 | | Compton | 1 | | Rain | 2 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Silas | 2 | | Town | 1 | | London | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 1 | | Wapping | 1 | | Aldershot | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Rain" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Nest" | | 8 | "Silas" | | 9 | "Police" | | 10 | "Morris" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Compton" | | 3 | "Town" | | 4 | "London" | | 5 | "Wapping" | | 6 | "Aldershot" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like standing water and someone's" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1225 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 26.06 | | std | 24.9 | | cv | 0.955 | | sampleLengths | | 0 | 25 | | 1 | 43 | | 2 | 10 | | 3 | 3 | | 4 | 69 | | 5 | 10 | | 6 | 29 | | 7 | 7 | | 8 | 4 | | 9 | 68 | | 10 | 24 | | 11 | 11 | | 12 | 5 | | 13 | 10 | | 14 | 20 | | 15 | 2 | | 16 | 19 | | 17 | 3 | | 18 | 71 | | 19 | 53 | | 20 | 7 | | 21 | 10 | | 22 | 8 | | 23 | 60 | | 24 | 61 | | 25 | 14 | | 26 | 1 | | 27 | 26 | | 28 | 7 | | 29 | 14 | | 30 | 4 | | 31 | 11 | | 32 | 48 | | 33 | 6 | | 34 | 9 | | 35 | 5 | | 36 | 33 | | 37 | 23 | | 38 | 6 | | 39 | 112 | | 40 | 68 | | 41 | 14 | | 42 | 47 | | 43 | 27 | | 44 | 45 | | 45 | 17 | | 46 | 56 |
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| 85.98% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 91 | | matches | | 0 | "been chiselled" | | 1 | "was built" | | 2 | "been finished" | | 3 | "been taught" | | 4 | "been hung" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1107 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.013550135501355014 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 11.78 | | std | 8.25 | | cv | 0.701 | | sampleLengths | | 0 | 25 | | 1 | 12 | | 2 | 7 | | 3 | 24 | | 4 | 9 | | 5 | 1 | | 6 | 3 | | 7 | 19 | | 8 | 17 | | 9 | 33 | | 10 | 3 | | 11 | 2 | | 12 | 5 | | 13 | 4 | | 14 | 10 | | 15 | 15 | | 16 | 7 | | 17 | 4 | | 18 | 27 | | 19 | 21 | | 20 | 5 | | 21 | 15 | | 22 | 16 | | 23 | 8 | | 24 | 11 | | 25 | 5 | | 26 | 10 | | 27 | 8 | | 28 | 12 | | 29 | 2 | | 30 | 19 | | 31 | 3 | | 32 | 40 | | 33 | 5 | | 34 | 26 | | 35 | 11 | | 36 | 2 | | 37 | 9 | | 38 | 20 | | 39 | 2 | | 40 | 9 | | 41 | 7 | | 42 | 6 | | 43 | 4 | | 44 | 8 | | 45 | 6 | | 46 | 16 | | 47 | 10 | | 48 | 6 | | 49 | 22 |
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| 74.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.47115384615384615 | | totalSentences | 104 | | uniqueOpeners | 49 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 84 | | matches | | 0 | "Somewhere down there, shoes scraped" | | 1 | "Then a gate clanged." |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 84 | | matches | | 0 | "He took Wardour Street at" | | 1 | "He knew these streets better" | | 2 | "She stayed off his shoulder" | | 3 | "He saw her." | | 4 | "Her breath sawed" | | 5 | "He turned and ran the" | | 6 | "They crossed six streets that" | | 7 | "His jacket was soaked through." | | 8 | "His left arm kept moving" | | 9 | "She went left." | | 10 | "Her torch beam found the" | | 11 | "She took the steps two" | | 12 | "His face was wet and" | | 13 | "He pulled his hand out" | | 14 | "He pressed it to the" | | 15 | "He stepped backwards into the" | | 16 | "He turned and walked into" | | 17 | "She thought about the report." | | 18 | "She had never once been" | | 19 | "Her torch found it and" |
| | ratio | 0.274 | |
| 49.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 84 | | matches | | 0 | "Rain came off the awnings" | | 1 | "Tomás Herrera ran forty feet" | | 2 | "He took Wardour Street at" | | 3 | "Quinn slapped the cab's roof" | | 4 | "The cabbie moved." | | 5 | "Herrera swerved left onto Old" | | 6 | "He knew these streets better" | | 7 | "She stayed off his shoulder" | | 8 | "He saw her." | | 9 | "Water sprayed off his heels." | | 10 | "Her breath sawed" | | 11 | "Rain ran into her mouth" | | 12 | "He turned and ran the" | | 13 | "Quinn went after him." | | 14 | "They crossed six streets that" | | 15 | "The Saint Christopher medallion had" | | 16 | "His jacket was soaked through." | | 17 | "His left arm kept moving" | | 18 | "The green neon of the" | | 19 | "Herrera passed under it and" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 4 | | matches | | 0 | "Water fell in sheets off the fence and made its own noise in the stairwell, a hollow, drumming sound that ran deeper than a cellar should." | | 1 | "Real light, warm and moving, thrown by lamps that hung from the ceiling on butcher's hooks." | | 2 | "Stalls under the lamps, hung with copper and glass, strings of dried things, jars of liquid that glowed the way a fish glows in deep water." | | 3 | "Quinn walked into the crowd with her coat dripping on the stone and her eyes on the back of a brown jacket, twenty stalls ahead, moving fast and not looking rou…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |