| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 83.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1556 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "truly" | | 2 | "suddenly" | | 3 | "slightly" |
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| 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) | |
| 61.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1556 | | totalAiIsms | 12 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "carried the weight" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | |
| | highlights | | 0 | "weight" | | 1 | "tracing" | | 2 | "scanning" | | 3 | "scanned" | | 4 | "stomach" | | 5 | "carried the weight" | | 6 | "flickered" | | 7 | "traced" | | 8 | "sentinel" | | 9 | "unreadable" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "air was thick with" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 208 | | matches | (empty) | |
| 94.78% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 1 | | narrationSentences | 208 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 208 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1556 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 10 | | matches | | 0 | "Rory, Eva said." | | 1 | "I went back to Cardiff, Eva said." | | 2 | "You left everything, Eva said." | | 3 | "I am not the same person who left Cardiff, Rory said." | | 4 | "You should go, Rory said." | | 5 | "I miss my friend, Eva said softly." | | 6 | "I will not tell them you are here, Eva said." | | 7 | "She knows too much, Silas said." | | 8 | "Then I will have to be someone else again, she said." | | 9 | "Rest tonight, he said." |
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| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 94 | | wordCount | 1556 | | uniqueNames | 20 | | maxNameDensity | 1.8 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Soho | 1 | | Carter | 3 | | Raven | 1 | | Nest | 1 | | London | 2 | | Silas | 9 | | Blackwood | 2 | | Prague | 1 | | Evan | 3 | | Cardiff | 4 | | Pre-Law | 1 | | Yu-Fei | 1 | | Cheung | 1 | | Rory | 28 | | Eva | 21 | | Golden | 1 | | Empress | 1 | | Aurora | 2 | | Brendan | 1 | | You | 10 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Evan" | | 5 | "Yu-Fei" | | 6 | "Cheung" | | 7 | "Rory" | | 8 | "Eva" | | 9 | "Aurora" | | 10 | "Brendan" | | 11 | "You" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Prague" | | 3 | "Cardiff" | | 4 | "Golden" |
| | globalScore | 0.6 | | windowScore | 0.167 | |
| 79.91% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like they had secrets buried in th" | | 1 | "felt like it belonged to a different sp" | | 2 | "shadows that seemed to stretch too long in the corners" |
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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 | 1556 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 208 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 25.1 | | std | 23.84 | | cv | 0.95 | | sampleLengths | | 0 | 99 | | 1 | 56 | | 2 | 90 | | 3 | 51 | | 4 | 57 | | 5 | 20 | | 6 | 48 | | 7 | 10 | | 8 | 1 | | 9 | 48 | | 10 | 34 | | 11 | 16 | | 12 | 21 | | 13 | 24 | | 14 | 2 | | 15 | 30 | | 16 | 2 | | 17 | 60 | | 18 | 16 | | 19 | 17 | | 20 | 3 | | 21 | 5 | | 22 | 2 | | 23 | 68 | | 24 | 8 | | 25 | 3 | | 26 | 4 | | 27 | 67 | | 28 | 22 | | 29 | 41 | | 30 | 4 | | 31 | 31 | | 32 | 12 | | 33 | 15 | | 34 | 5 | | 35 | 56 | | 36 | 30 | | 37 | 3 | | 38 | 22 | | 39 | 28 | | 40 | 5 | | 41 | 4 | | 42 | 16 | | 43 | 56 | | 44 | 7 | | 45 | 3 | | 46 | 4 | | 47 | 3 | | 48 | 43 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 208 | | matches | | 0 | "was supposed" | | 1 | "was gone" | | 2 | "is gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 306 | | matches | | 0 | "was polishing" | | 1 | "was wearing" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 208 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1563 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.03134996801023673 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.008317338451695458 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 208 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 208 | | mean | 7.48 | | std | 5.46 | | cv | 0.73 | | sampleLengths | | 0 | 24 | | 1 | 17 | | 2 | 21 | | 3 | 14 | | 4 | 23 | | 5 | 7 | | 6 | 21 | | 7 | 13 | | 8 | 15 | | 9 | 9 | | 10 | 15 | | 11 | 20 | | 12 | 8 | | 13 | 23 | | 14 | 4 | | 15 | 11 | | 16 | 21 | | 17 | 12 | | 18 | 3 | | 19 | 6 | | 20 | 9 | | 21 | 5 | | 22 | 9 | | 23 | 14 | | 24 | 4 | | 25 | 4 | | 26 | 21 | | 27 | 5 | | 28 | 15 | | 29 | 4 | | 30 | 12 | | 31 | 14 | | 32 | 18 | | 33 | 2 | | 34 | 8 | | 35 | 1 | | 36 | 5 | | 37 | 25 | | 38 | 18 | | 39 | 3 | | 40 | 7 | | 41 | 12 | | 42 | 12 | | 43 | 3 | | 44 | 13 | | 45 | 5 | | 46 | 14 | | 47 | 2 | | 48 | 5 | | 49 | 4 |
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| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 52 | | diversityRatio | 0.19230769230769232 | | totalSentences | 208 | | uniqueOpeners | 40 | |
| 35.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 187 | | matches | | 0 | "Then the door opened again." | | 1 | "Then I will have to" |
| | ratio | 0.011 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 117 | | totalSentences | 187 | | matches | | 0 | "She paused on the threshold," | | 1 | "She was not here for" | | 2 | "She was here because the" | | 3 | "She moved toward the bar," | | 4 | "She wore a dark jacket" | | 5 | "He moved with a deliberate," | | 6 | "He wore his silver signet" | | 7 | "He looked up, his hazel" | | 8 | "He did not smile." | | 9 | "He simply nodded, acknowledging her" | | 10 | "She rubbed her left wrist," | | 11 | "It was easier than the" | | 12 | "She ordered a tonic water." | | 13 | "He knew better than to" | | 14 | "He knew about Evan." | | 15 | "He knew about Cardiff." | | 16 | "He knew she was supposed" | | 17 | "She shook a umbrella, sending" | | 18 | "She was wearing a coat" | | 19 | "She turned, scanning the room," |
| | ratio | 0.626 | |
| 8.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 169 | | totalSentences | 187 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora Carter pushed open the" | | 2 | "She paused on the threshold," | | 3 | "The walls were cluttered with" | | 4 | "She was not here for" | | 5 | "She was here because the" | | 6 | "She moved toward the bar," | | 7 | "She wore a dark jacket" | | 8 | "He moved with a deliberate," | | 9 | "He wore his silver signet" | | 10 | "He looked up, his hazel" | | 11 | "He did not smile." | | 12 | "He simply nodded, acknowledging her" | | 13 | "Rory took a stool at" | | 14 | "She rubbed her left wrist," | | 15 | "A childhood accident." | | 16 | "That was the story she" | | 17 | "It was easier than the" | | 18 | "She ordered a tonic water." | | 19 | "Silas poured it without a" |
| | ratio | 0.904 | |
| 53.48% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 187 | | matches | | 0 | "Because of him." | | 1 | "Now you need to let" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "The walls were cluttered with faded maps and black-and-white photographs of people who looked like they had secrets buried in their shoe heels." | | 1 | "In her place was a woman who delivered food in the rain and lived above a bar owned by a retired spymaster." |
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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 | |