| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.563 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1117 | | totalAiIsmAdverbs | 1 | | 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) | |
| 68.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1117 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silence" | | 1 | "pulse" | | 2 | "warmth" | | 3 | "echo" | | 4 | "weight" | | 5 | "footsteps" |
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| 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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | 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 | 5 | | markdownWords | 22 | | totalWords | 1117 | | ratio | 0.02 | | matches | | 0 | "Richmond Park, Pen Ponds car park, 11:40 p.m." | | 1 | "Follow the path to the stones. Customer will meet you." | | 2 | "...take it." | | 3 | "take" | | 4 | "...funny." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 1014 | | uniqueNames | 7 | | maxNameDensity | 0.39 | | worstName | "Yu-Fei" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Yu-Fei" | | discoveredNames | | Park | 1 | | Pen | 1 | | Ponds | 1 | | Yu-Fei | 4 | | Rory | 4 | | November | 1 | | Closer | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1117 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 111 | | matches | | 0 | "felt that warmth" | | 1 | "understood that running" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 26.6 | | std | 20.54 | | cv | 0.772 | | sampleLengths | | 0 | 39 | | 1 | 11 | | 2 | 48 | | 3 | 12 | | 4 | 19 | | 5 | 12 | | 6 | 56 | | 7 | 7 | | 8 | 47 | | 9 | 19 | | 10 | 8 | | 11 | 44 | | 12 | 37 | | 13 | 31 | | 14 | 37 | | 15 | 5 | | 16 | 67 | | 17 | 24 | | 18 | 15 | | 19 | 20 | | 20 | 34 | | 21 | 5 | | 22 | 18 | | 23 | 5 | | 24 | 55 | | 25 | 26 | | 26 | 44 | | 27 | 13 | | 28 | 2 | | 29 | 53 | | 30 | 25 | | 31 | 5 | | 32 | 1 | | 33 | 1 | | 34 | 90 | | 35 | 31 | | 36 | 13 | | 37 | 25 | | 38 | 62 | | 39 | 8 | | 40 | 12 | | 41 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 80.24% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 167 | | matches | | 0 | "was itching" | | 1 | "was nodding" | | 2 | "was not smoking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1020 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.025490196078431372 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0058823529411764705 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 10.06 | | std | 7.44 | | cv | 0.739 | | sampleLengths | | 0 | 27 | | 1 | 12 | | 2 | 5 | | 3 | 6 | | 4 | 27 | | 5 | 21 | | 6 | 3 | | 7 | 9 | | 8 | 15 | | 9 | 4 | | 10 | 4 | | 11 | 8 | | 12 | 16 | | 13 | 17 | | 14 | 3 | | 15 | 20 | | 16 | 4 | | 17 | 3 | | 18 | 6 | | 19 | 24 | | 20 | 15 | | 21 | 2 | | 22 | 3 | | 23 | 11 | | 24 | 5 | | 25 | 8 | | 26 | 27 | | 27 | 17 | | 28 | 4 | | 29 | 12 | | 30 | 8 | | 31 | 13 | | 32 | 3 | | 33 | 6 | | 34 | 22 | | 35 | 17 | | 36 | 4 | | 37 | 7 | | 38 | 9 | | 39 | 5 | | 40 | 3 | | 41 | 15 | | 42 | 10 | | 43 | 4 | | 44 | 21 | | 45 | 14 | | 46 | 6 | | 47 | 8 | | 48 | 6 | | 49 | 4 |
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| 48.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.38181818181818183 | | totalSentences | 110 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 55.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 90 | | matches | | 0 | "She pulled out her phone." | | 1 | "She turned a slow circle," | | 2 | "She hung up." | | 3 | "She slung the insulated bag" | | 4 | "Her breath smoked." | | 5 | "She stopped at the tree" | | 6 | "Her voice sounded thin, as" | | 7 | "She pressed two fingers to" | | 8 | "She had felt that warmth" | | 9 | "She could go back." | | 10 | "She could see the pale" | | 11 | "She looked back." | | 12 | "Her thumb found the crescent" | | 13 | "It had not itched in" | | 14 | "She stepped through the ring." | | 15 | "It did not fade, it" | | 16 | "She lowered the torch." | | 17 | "She held up the bag" | | 18 | "She checked the slip" | | 19 | "She swung the torch toward" |
| | ratio | 0.411 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 90 | | matches | | 0 | "The order slip had said" | | 1 | "Rory killed the bike's engine" | | 2 | "She pulled out her phone." | | 3 | "She turned a slow circle," | | 4 | "She hung up." | | 5 | "The voicemail would reach Yu-Fei" | | 6 | "The slip had a second" | | 7 | "Customer will meet you.*" | | 8 | "Rory read it twice." | | 9 | "Yu-Fei never sent her past" | | 10 | "She slung the insulated bag" | | 11 | "Gravel gave way to packed" | | 12 | "Her breath smoked." | | 13 | "Frost had silvered the ferns" | | 14 | "Those stayed green." | | 15 | "She stopped at the tree" | | 16 | "Wildflowers crowded the ground between" | | 17 | "Her voice sounded thin, as" | | 18 | "The pendant against her breastbone" | | 19 | "She pressed two fingers to" |
| | ratio | 0.867 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "Now she counted eleven, and" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | |
| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.2 | | effectiveRatio | 0.125 | |