| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said quietly [quietly]" | | 1 | "She stood slowly [slowly]" |
| | dialogueSentences | 10 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.4 | | effectiveRatio | 0.4 | |
| 87.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 782 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 55.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 782 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "echo" | | 2 | "weight" | | 3 | "pulsed" | | 4 | "could feel" | | 5 | "warmth" | | 6 | "footsteps" |
| |
| 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 | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 70 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 782 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 744 | | uniqueNames | 10 | | maxNameDensity | 0.27 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Four" | | discoveredNames | | Aurora | 2 | | Sheen | 1 | | Road | 1 | | Heartstone | 1 | | Pendant | 1 | | Greenwich | 1 | | Eva | 1 | | October | 1 | | Four | 2 | | Cardiff | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Pendant" | | 2 | "Eva" |
| | places | | 0 | "Sheen" | | 1 | "Road" | | 2 | "Greenwich" | | 3 | "October" | | 4 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite straightened again" |
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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 | 782 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 70 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 34 | | std | 24.46 | | cv | 0.719 | | sampleLengths | | 0 | 66 | | 1 | 73 | | 2 | 8 | | 3 | 61 | | 4 | 59 | | 5 | 3 | | 6 | 45 | | 7 | 13 | | 8 | 6 | | 9 | 52 | | 10 | 17 | | 11 | 1 | | 12 | 11 | | 13 | 65 | | 14 | 68 | | 15 | 16 | | 16 | 19 | | 17 | 49 | | 18 | 6 | | 19 | 56 | | 20 | 46 | | 21 | 20 | | 22 | 22 |
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| 99.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 65 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 123 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 70 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 429 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 12 | | adverbRatio | 0.027972027972027972 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.004662004662004662 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 70 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 70 | | mean | 11.17 | | std | 8.51 | | cv | 0.762 | | sampleLengths | | 0 | 34 | | 1 | 6 | | 2 | 11 | | 3 | 6 | | 4 | 9 | | 5 | 8 | | 6 | 22 | | 7 | 8 | | 8 | 8 | | 9 | 27 | | 10 | 6 | | 11 | 2 | | 12 | 22 | | 13 | 3 | | 14 | 14 | | 15 | 7 | | 16 | 15 | | 17 | 10 | | 18 | 15 | | 19 | 11 | | 20 | 23 | | 21 | 3 | | 22 | 4 | | 23 | 14 | | 24 | 17 | | 25 | 2 | | 26 | 8 | | 27 | 9 | | 28 | 4 | | 29 | 3 | | 30 | 3 | | 31 | 10 | | 32 | 11 | | 33 | 31 | | 34 | 2 | | 35 | 15 | | 36 | 1 | | 37 | 4 | | 38 | 7 | | 39 | 32 | | 40 | 2 | | 41 | 1 | | 42 | 2 | | 43 | 15 | | 44 | 13 | | 45 | 11 | | 46 | 6 | | 47 | 15 | | 48 | 15 | | 49 | 21 |
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| 62.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.44285714285714284 | | totalSentences | 70 | | uniqueOpeners | 31 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Then, from directly behind her," |
| | ratio | 0.018 | |
| 45.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 55 | | matches | | 0 | "Her trainers slipped on wet" | | 1 | "She caught herself on an" | | 2 | "She pulled it out and" | | 3 | "she said quietly" | | 4 | "She followed it." | | 5 | "It stayed still in the" | | 6 | "Their bark had grown over" | | 7 | "She stepped through." | | 8 | "It was softer, sweeter, the" | | 9 | "She crouched and pressed two" | | 10 | "Her phone showed no signal" | | 11 | "She checked again five minutes" | | 12 | "She thumbed the screen, then" | | 13 | "She stood, slowly, and counted" | | 14 | "She turned her head, too" | | 15 | "She saw only the outline" | | 16 | "Her voice came out thin." | | 17 | "She did not turn around." | | 18 | "She had heard her own" | | 19 | "Her left wrist ached along" |
| | ratio | 0.436 | |
| 23.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 55 | | matches | | 0 | "The park gates had long" | | 1 | "Her trainers slipped on wet" | | 2 | "She caught herself on an" | | 3 | "Deer shifted somewhere to the" | | 4 | "A car hissed along Sheen" | | 5 | "The silver chain lay cold" | | 6 | "She pulled it out and" | | 7 | "The crimson stone caught the" | | 8 | "she said quietly" | | 9 | "The path she had memorised" | | 10 | "She followed it." | | 11 | "The park did not seem" | | 12 | "It stayed still in the" | | 13 | "The standing stones came into" | | 14 | "Their bark had grown over" | | 15 | "Aurora stopped at the boundary," | | 16 | "She stepped through." | | 17 | "The air changed first." | | 18 | "It was softer, sweeter, the" | | 19 | "Wildflowers covered the ground, foxgloves" |
| | ratio | 0.873 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "She could feel it through her shirt, a pressure that felt less like warmth and more like a question." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.25 | | effectiveRatio | 0.2 | |