| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 81.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1353 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | adverb | "barely above a whisper" | | count | 1 |
| | 3 | | | 4 | |
| | highlights | | 0 | "utterly" | | 1 | "carefully" | | 2 | "barely above a whisper" | | 3 | "softly" | | 4 | "truly" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 33.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1353 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "mechanical" | | 1 | "weight" | | 2 | "familiar" | | 3 | "silence" | | 4 | "calculating" | | 5 | "navigated" | | 6 | "pulse" | | 7 | "charged" | | 8 | "affection" | | 9 | "echo" | | 10 | "flickered" | | 11 | "wavered" | | 12 | "constructed" | | 13 | "traced" | | 14 | "whisper" | | 15 | "footsteps" |
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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 | 129 | | matches | (empty) | |
| 76.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1353 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 8 | | matches | | 0 | "You are still wearing that sweater, he said." | | 1 | "You know the rules, she said, voice steady despite the quickening pulse at her throat." | | 2 | "Always the barrister daughter, he murmured, stepping over the threshold before she could protest." | | 3 | "I needed to see you, he said, unbuttoning his suit jacket and draping it over the back of a chair buried under case law …" | | 4 | "You always were too stubborn to run, he whispered." | | 5 | "And you are too reckless to stop, she replied, the words slipping out before she could cage them." | | 6 | "Luc, she said, the name tasting like ash and honey on her tongue." | | 7 | "I know, he said softly." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1353 | | uniqueNames | 13 | | maxNameDensity | 0.52 | | worstName | "You" | | maxWindowNameDensity | 1.5 | | worstWindowName | "You" | | discoveredNames | | Brick | 2 | | Lane | 2 | | Marseille | 1 | | Luc | 2 | | Moreau | 1 | | Avaros | 1 | | Rory | 3 | | London | 1 | | Aurora | 5 | | Lucien | 4 | | Three | 3 | | Ptolemy | 4 | | You | 7 |
| | persons | | 0 | "Luc" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Aurora" | | 4 | "Lucien" | | 5 | "Ptolemy" | | 6 | "You" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1353 | | matches | (empty) | |
| 89.15% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 129 | | matches | | 0 | "wearing that sweater" | | 1 | "like that, I" | | 2 | "know that I" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 43.65 | | std | 27.52 | | cv | 0.631 | | sampleLengths | | 0 | 78 | | 1 | 74 | | 2 | 57 | | 3 | 27 | | 4 | 32 | | 5 | 25 | | 6 | 93 | | 7 | 33 | | 8 | 27 | | 9 | 101 | | 10 | 42 | | 11 | 11 | | 12 | 44 | | 13 | 33 | | 14 | 88 | | 15 | 30 | | 16 | 48 | | 17 | 64 | | 18 | 9 | | 19 | 18 | | 20 | 24 | | 21 | 5 | | 22 | 51 | | 23 | 29 | | 24 | 106 | | 25 | 13 | | 26 | 20 | | 27 | 20 | | 28 | 37 | | 29 | 47 | | 30 | 67 |
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| 99.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 129 | | matches | | 0 | "was slicked" | | 1 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 250 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 129 | | ratio | 0 | | matches | (empty) | |
| 99.59% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1359 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 55 | | adverbRatio | 0.04047093451066961 | | lyAdverbCount | 23 | | lyAdverbRatio | 0.01692420897718911 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 10.49 | | std | 6.92 | | cv | 0.66 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 18 | | 3 | 4 | | 4 | 1 | | 5 | 2 | | 6 | 28 | | 7 | 4 | | 8 | 3 | | 9 | 15 | | 10 | 23 | | 11 | 1 | | 12 | 12 | | 13 | 16 | | 14 | 29 | | 15 | 15 | | 16 | 8 | | 17 | 5 | | 18 | 8 | | 19 | 19 | | 20 | 16 | | 21 | 9 | | 22 | 7 | | 23 | 13 | | 24 | 5 | | 25 | 7 | | 26 | 8 | | 27 | 31 | | 28 | 19 | | 29 | 6 | | 30 | 6 | | 31 | 3 | | 32 | 3 | | 33 | 17 | | 34 | 15 | | 35 | 18 | | 36 | 14 | | 37 | 13 | | 38 | 13 | | 39 | 14 | | 40 | 22 | | 41 | 17 | | 42 | 10 | | 43 | 15 | | 44 | 6 | | 45 | 4 | | 46 | 25 | | 47 | 5 | | 48 | 4 | | 49 | 8 |
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| 34.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.3023255813953488 | | totalSentences | 129 | | uniqueOpeners | 39 | |
| 83.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 120 | | matches | | 0 | "Then he was there." | | 1 | "Always the barrister daughter, he" | | 2 | "Instead, she stayed rooted, her" |
| | ratio | 0.025 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 60 | | totalSentences | 120 | | matches | | 0 | "He had not changed." | | 1 | "He never did." | | 2 | "His platinum hair was slicked" | | 3 | "They looked exactly as she" | | 4 | "He did not look down" | | 5 | "He only looked at her." | | 6 | "You are still wearing that" | | 7 | "His voice was low, threaded" | | 8 | "I told you about the" | | 9 | "He offered a smile that" | | 10 | "I will sweep it up." | | 11 | "He was not here by" | | 12 | "He did precision." | | 13 | "He did leverage." | | 14 | "He did things that kept" | | 15 | "You know the rules, she" | | 16 | "You do not cross my" | | 17 | "He stood just inside the" | | 18 | "He smelled of cold rain," | | 19 | "She had learned to ignore" |
| | ratio | 0.5 | |
| 68.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 120 | | matches | | 0 | "The brass deadbolt turned with" | | 1 | "Aurora pulled the door open," | | 2 | "The air smelled of cardamom," | | 3 | "He had not changed." | | 4 | "He never did." | | 5 | "His platinum hair was slicked" | | 6 | "The tailored charcoal suit fit" | | 7 | "They looked exactly as she" | | 8 | "Ptolemy, her ginger tabby, slipped" | | 9 | "Lucien’s gloved hand moved almost" | | 10 | "He did not look down" | | 11 | "He only looked at her." | | 12 | "You are still wearing that" | | 13 | "His voice was low, threaded" | | 14 | "Aurora crossed her arms, feeling" | | 15 | "I told you about the" | | 16 | "He offered a smile that" | | 17 | "I will sweep it up." | | 18 | "The old instinct warred with" | | 19 | "Part of her wanted to" |
| | ratio | 0.783 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 120 | | matches | | 0 | "If you let me in," | | 1 | "Because it is easier to" | | 2 | "Because I thought I had" | | 3 | "Because I thought the line" | | 4 | "If you keep looking at" |
| | ratio | 0.042 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "The tailored charcoal suit fit his broad shoulders with military precision, but it was his eyes that pinned her to the scuffed linoleum." | | 1 | "His voice was low, threaded with a Marseille accent that softened the consonants, though the underlying edge remained unbroken." | | 2 | "The other part, the cool, calculating part that navigated legal briefs and midnight delivery routes alike, recognized the trap." | | 3 | "She traced the crescent scar on her wrist, feeling the ghost of his touch there, the memory of hands that had once known every fault line in her armor." |
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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 | |