| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1613 | | 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) | |
| 56.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1613 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "pulse" | | 1 | "warmth" | | 2 | "scanned" | | 3 | "weight" | | 4 | "throbbed" | | 5 | "echoes" | | 6 | "echo" | | 7 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 156 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 156 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1612 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.86% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1570 | | uniqueNames | 15 | | maxNameDensity | 1.08 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Cheung | 1 | | Thames | 1 | | Tonight | 1 | | Richmond | 2 | | Park | 2 | | Eva | 3 | | Cardiff | 4 | | University | 1 | | Jennifer | 1 | | Rory | 17 | | Cold | 4 | | Heat | 3 | | Plink | 3 |
| | persons | | 0 | "Cheung" | | 1 | "Eva" | | 2 | "Jennifer" | | 3 | "Rory" | | 4 | "Heat" |
| | places | | 0 | "Thames" | | 1 | "Richmond" | | 2 | "Park" | | 3 | "Cardiff" | | 4 | "Cold" |
| | globalScore | 0.959 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | 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 | 1612 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 164 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 62 | | std | 43.88 | | cv | 0.708 | | sampleLengths | | 0 | 120 | | 1 | 126 | | 2 | 2 | | 3 | 110 | | 4 | 111 | | 5 | 90 | | 6 | 121 | | 7 | 6 | | 8 | 74 | | 9 | 7 | | 10 | 94 | | 11 | 93 | | 12 | 4 | | 13 | 60 | | 14 | 90 | | 15 | 93 | | 16 | 6 | | 17 | 85 | | 18 | 63 | | 19 | 3 | | 20 | 34 | | 21 | 90 | | 22 | 3 | | 23 | 83 | | 24 | 2 | | 25 | 42 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 156 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 228 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 164 | | ratio | 0.006 | | matches | | 0 | "Wildflowers covered the ground in clumps—poppies and bluebells and white cow parsley thick for late season." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 123 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.008130081300813009 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 9.83 | | std | 5.61 | | cv | 0.571 | | sampleLengths | | 0 | 14 | | 1 | 21 | | 2 | 9 | | 3 | 7 | | 4 | 8 | | 5 | 17 | | 6 | 11 | | 7 | 27 | | 8 | 6 | | 9 | 21 | | 10 | 2 | | 11 | 6 | | 12 | 8 | | 13 | 25 | | 14 | 8 | | 15 | 14 | | 16 | 7 | | 17 | 22 | | 18 | 13 | | 19 | 2 | | 20 | 11 | | 21 | 15 | | 22 | 7 | | 23 | 19 | | 24 | 27 | | 25 | 13 | | 26 | 8 | | 27 | 10 | | 28 | 7 | | 29 | 16 | | 30 | 16 | | 31 | 8 | | 32 | 15 | | 33 | 10 | | 34 | 4 | | 35 | 3 | | 36 | 15 | | 37 | 8 | | 38 | 9 | | 39 | 12 | | 40 | 6 | | 41 | 5 | | 42 | 16 | | 43 | 6 | | 44 | 10 | | 45 | 9 | | 46 | 4 | | 47 | 10 | | 48 | 6 | | 49 | 6 |
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| 43.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.32926829268292684 | | totalSentences | 164 | | uniqueOpeners | 54 | |
| 45.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 145 | | matches | | 0 | "Then came grass." | | 1 | "Then came the line of" |
| | ratio | 0.014 | |
| 87.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 145 | | matches | | 0 | "She pushed her staff keys" | | 1 | "She had solved puzzles with" | | 2 | "She kept the pendant off" | | 3 | "She crossed the road at" | | 4 | "She thought of the childhood" | | 5 | "She pushed the memory aside" | | 6 | "She heard tires on wet" | | 7 | "She slipped through a side" | | 8 | "Her bright blue eyes adjusted" | | 9 | "She stepped between two stones" | | 10 | "She withdrew her hand and" | | 11 | "She wiped it on her" | | 12 | "She pulled her phone from" | | 13 | "She locked the phone and" | | 14 | "She shifted it to her" | | 15 | "She lifted the pendant clear" | | 16 | "She thought of the habit" | | 17 | "Her voice sounded loud in" | | 18 | "She held the pendant up" | | 19 | "She turned in a slow" |
| | ratio | 0.331 | |
| 42.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 145 | | matches | | 0 | "Rory left the Golden Empress" | | 1 | "Yu-Fei Cheung had locked the" | | 2 | "Rain had soaked the pavement" | | 3 | "Bus fumes hung low over" | | 4 | "She pushed her staff keys" | | 5 | "Heat spread through the crimson" | | 6 | "Heat had spread through it" | | 7 | "Tonight heat pulled toward Richmond" | | 8 | "Someone had left the pendant" | | 9 | "Stone the size of a" | | 10 | "Rory had carried the box" | | 11 | "Rory had kept it in" | | 12 | "Eva had warned her about" | | 13 | "Rory had nodded and kept" | | 14 | "She had solved puzzles with" | | 15 | "She kept the pendant off" | | 16 | "Rory shouldered the delivery bag" | | 17 | "She crossed the road at" | | 18 | "The pendant warmed with each" | | 19 | "The crescent-shaped scar on her" |
| | ratio | 0.834 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 2 | | matches | | 0 | "Eva had warned her about men who left gifts and men who left traps." | | 1 | "She thought of the childhood accident that left the scar and the fence at home in Cardiff and her mother, Jennifer, pressing a cloth to her wrist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |