| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2218 | | totalAiIsmAdverbs | 3 | | 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) | |
| 84.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2218 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "could feel" | | 1 | "flicked" | | 2 | "weight" | | 3 | "flicker" | | 4 | "sense of" | | 5 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 274 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 96 | | wordCount | 1323 | | uniqueNames | 12 | | maxNameDensity | 3.02 | | worstName | "Aurora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | London | 1 | | Morgan | 1 | | Cardiff | 2 | | Aurora | 40 | | Eva | 37 | | Prague | 1 | | Tom | 1 | | Silas | 9 |
| | persons | | 0 | "Raven" | | 1 | "Morgan" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Tom" | | 5 | "Silas" |
| | places | | 0 | "London" | | 1 | "Cardiff" | | 2 | "Prague" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 64.74% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 1.353 | | wordCount | 2218 | | matches | | 0 | "not the shoes, not the haircut, not the careful voice from some polished office, but the raw streak underneath, the one" | | 1 | "not the haircut, not the careful voice from some polished office, but the raw streak underneath, the one" | | 2 | "not the careful voice from some polished office, but the raw streak underneath, the one" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 274 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 221 | | mean | 10.04 | | std | 13.26 | | cv | 1.321 | | sampleLengths | | 0 | 56 | | 1 | 75 | | 2 | 24 | | 3 | 10 | | 4 | 8 | | 5 | 10 | | 6 | 1 | | 7 | 6 | | 8 | 5 | | 9 | 4 | | 10 | 61 | | 11 | 3 | | 12 | 5 | | 13 | 9 | | 14 | 7 | | 15 | 14 | | 16 | 6 | | 17 | 22 | | 18 | 1 | | 19 | 8 | | 20 | 1 | | 21 | 121 | | 22 | 12 | | 23 | 4 | | 24 | 5 | | 25 | 3 | | 26 | 10 | | 27 | 9 | | 28 | 8 | | 29 | 9 | | 30 | 24 | | 31 | 22 | | 32 | 2 | | 33 | 4 | | 34 | 1 | | 35 | 3 | | 36 | 4 | | 37 | 1 | | 38 | 11 | | 39 | 13 | | 40 | 5 | | 41 | 15 | | 42 | 2 | | 43 | 29 | | 44 | 16 | | 45 | 1 | | 46 | 8 | | 47 | 4 | | 48 | 4 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 224 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 274 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1328 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.021837349397590362 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002259036144578313 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 274 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 274 | | mean | 8.09 | | std | 7.4 | | cv | 0.914 | | sampleLengths | | 0 | 20 | | 1 | 7 | | 2 | 29 | | 3 | 31 | | 4 | 10 | | 5 | 34 | | 6 | 24 | | 7 | 10 | | 8 | 8 | | 9 | 10 | | 10 | 1 | | 11 | 6 | | 12 | 5 | | 13 | 4 | | 14 | 3 | | 15 | 9 | | 16 | 31 | | 17 | 3 | | 18 | 6 | | 19 | 7 | | 20 | 2 | | 21 | 3 | | 22 | 5 | | 23 | 5 | | 24 | 4 | | 25 | 7 | | 26 | 6 | | 27 | 8 | | 28 | 6 | | 29 | 22 | | 30 | 1 | | 31 | 8 | | 32 | 1 | | 33 | 13 | | 34 | 36 | | 35 | 6 | | 36 | 10 | | 37 | 4 | | 38 | 4 | | 39 | 7 | | 40 | 16 | | 41 | 25 | | 42 | 12 | | 43 | 4 | | 44 | 5 | | 45 | 3 | | 46 | 10 | | 47 | 9 | | 48 | 8 | | 49 | 9 |
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| 44.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.22992700729927007 | | totalSentences | 274 | | uniqueOpeners | 63 | |
| 25.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 130 | | matches | | 0 | "Even the way she stood" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 130 | | matches | | 0 | "Her hair clung in dark" | | 1 | "He set a bowl of" | | 2 | "Her hair had gone back" | | 3 | "Her fingers were bare." | | 4 | "Her face had sharpened." | | 5 | "She took the stool beside" | | 6 | "His hazel eyes flicked to" | | 7 | "He knew when a room" | | 8 | "Her own face stared back" | | 9 | "She looked at it as" | | 10 | "Her throat moved when she" | | 11 | "They looked at each other" | | 12 | "She pressed her tongue against" |
| | ratio | 0.1 | |
| 29.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 130 | | matches | | 0 | "Rain needled the green neon" | | 1 | "Aurora sat at the far" | | 2 | "Her hair clung in dark" | | 3 | "Silas moved along the bar" | | 4 | "He set a bowl of" | | 5 | "Aurora tipped one into her" | | 6 | "The door opened." | | 7 | "A gust of cold air" | | 8 | "A woman stepped in, pausing" | | 9 | "Hair cut blunt at the" | | 10 | "A leather case tucked under" | | 11 | "Silas looked up." | | 12 | "The woman unbuttoned her coat." | | 13 | "The voice came first." | | 14 | "Aurora’s hand tightened round the" | | 15 | "The whisky shifted and tapped" | | 16 | "The woman turned at the" | | 17 | "Aurora let out a breath" | | 18 | "Eva Morgan stood in the" | | 19 | "This woman had none of" |
| | ratio | 0.862 | |
| 38.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 130 | | matches | | 0 | "Now she kept her elbows" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "A woman stepped in, pausing under the green spill from the sign as if she had walked through the wrong doorway and needed a second to decide whether to admit it…" | | 1 | "The room seemed to narrow, the old photographs and maps drawing in, every line in the wood beneath their hands growing visible." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |