| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.314 | | leniency | 0.629 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 953 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 94.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 953 | | 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 | 45 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 45 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 953 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 657 | | uniqueNames | 12 | | maxNameDensity | 1.98 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 13 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Eva | 11 | | Welsh | 1 | | London | 1 | | Silas | 4 | | Pontcanna | 1 | | Lisbon | 1 | | Caledonian | 1 | | Road | 1 |
| | persons | | 0 | "Rory" | | 1 | "Carter" | | 2 | "Eva" | | 3 | "Welsh" | | 4 | "Silas" |
| | places | | 0 | "Golden" | | 1 | "London" | | 2 | "Pontcanna" | | 3 | "Lisbon" | | 4 | "Caledonian" | | 5 | "Road" |
| | globalScore | 0.511 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | 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 | 953 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 69 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 25.76 | | std | 23.15 | | cv | 0.899 | | sampleLengths | | 0 | 92 | | 1 | 12 | | 2 | 61 | | 3 | 30 | | 4 | 5 | | 5 | 77 | | 6 | 30 | | 7 | 31 | | 8 | 53 | | 9 | 21 | | 10 | 3 | | 11 | 50 | | 12 | 2 | | 13 | 4 | | 14 | 58 | | 15 | 5 | | 16 | 8 | | 17 | 2 | | 18 | 20 | | 19 | 43 | | 20 | 13 | | 21 | 27 | | 22 | 3 | | 23 | 3 | | 24 | 5 | | 25 | 41 | | 26 | 50 | | 27 | 2 | | 28 | 8 | | 29 | 28 | | 30 | 5 | | 31 | 37 | | 32 | 4 | | 33 | 12 | | 34 | 51 | | 35 | 22 | | 36 | 35 |
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| 81.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 45 | | matches | | 0 | "been told" | | 1 | "been sanded" | | 2 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 105 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 657 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.0350076103500761 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0030441400304414 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 13.81 | | std | 11.57 | | cv | 0.838 | | sampleLengths | | 0 | 37 | | 1 | 20 | | 2 | 35 | | 3 | 12 | | 4 | 6 | | 5 | 31 | | 6 | 17 | | 7 | 7 | | 8 | 23 | | 9 | 7 | | 10 | 5 | | 11 | 3 | | 12 | 17 | | 13 | 29 | | 14 | 6 | | 15 | 22 | | 16 | 3 | | 17 | 8 | | 18 | 11 | | 19 | 8 | | 20 | 12 | | 21 | 19 | | 22 | 7 | | 23 | 46 | | 24 | 7 | | 25 | 14 | | 26 | 3 | | 27 | 7 | | 28 | 37 | | 29 | 6 | | 30 | 2 | | 31 | 4 | | 32 | 8 | | 33 | 5 | | 34 | 45 | | 35 | 5 | | 36 | 8 | | 37 | 2 | | 38 | 20 | | 39 | 17 | | 40 | 4 | | 41 | 22 | | 42 | 13 | | 43 | 17 | | 44 | 10 | | 45 | 3 | | 46 | 3 | | 47 | 3 | | 48 | 2 | | 49 | 2 |
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| 71.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.463768115942029 | | totalSentences | 69 | | uniqueOpeners | 32 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 37 | | matches | | 0 | "Instead, she found a woman" | | 1 | "Somewhere in four years of" | | 2 | "Then it was gone." |
| | ratio | 0.081 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 37 | | matches | | 0 | "She had come in for" | | 1 | "He said nothing, but his" | | 2 | "She didn't ask him what" | | 3 | "She crossed the floor past" | | 4 | "Her hair, once a wild" | | 5 | "Her wrist was bare, and" | | 6 | "She could feel the old" |
| | ratio | 0.189 | |
| 68.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 37 | | matches | | 0 | "The green neon above the" | | 1 | "She had come in for" | | 2 | "Rory knew the set of" | | 3 | "Silas glanced up from the" | | 4 | "He said nothing, but his" | | 5 | "Rory had seen him do" | | 6 | "She didn't ask him what" | | 7 | "She crossed the floor past" | | 8 | "The leather was cold through" | | 9 | "Eva looked up." | | 10 | "Her hair, once a wild" | | 11 | "The Welsh had gone out" | | 12 | "Rory tapped the bar" | | 13 | "Silas set the glass down" | | 14 | "The limp made his walk" | | 15 | "Eva lifted her drink" | | 16 | "Her wrist was bare, and" | | 17 | "The rain picked up against" | | 18 | "A cab hissed past outside." | | 19 | "Rory waited, because she had" |
| | ratio | 0.784 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "Instead, she found a woman in a camel coat sitting at the far end of the bar, turning a tumbler in slow circles, as if she had been waiting there for someone wh…" | | 1 | "She could feel the old anger pulling at the edges of her ribs, the same anger that had kept her awake in a bedsit off the Caledonian Road for two winters, draft…" |
| |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva set, the base squared to a beer mat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |