| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 32 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.78 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 966 | | 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) | |
| 74.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 966 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "etched" | | 1 | "familiar" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "flicked" |
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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 | 143 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 143 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | 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 | 966 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 32 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 824 | | uniqueNames | 18 | | maxNameDensity | 3.16 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Eva | 19 | | Kowalski | 2 | | Quinn | 26 | | Camden | 1 | | Town | 1 | | Veil | 2 | | Market | 2 | | Morris | 2 | | Three | 2 | | Tube | 1 | | British | 1 | | Museum | 1 | | History | 1 | | Greek | 1 | | Sigma | 1 | | Footsteps | 1 | | Met | 1 | | Compass | 1 |
| | persons | | 0 | "Eva" | | 1 | "Kowalski" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Three" | | 6 | "Museum" | | 7 | "Footsteps" | | 8 | "Compass" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "British" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 966 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 14.42 | | std | 12.78 | | cv | 0.886 | | sampleLengths | | 0 | 4 | | 1 | 22 | | 2 | 5 | | 3 | 26 | | 4 | 6 | | 5 | 58 | | 6 | 35 | | 7 | 17 | | 8 | 42 | | 9 | 7 | | 10 | 8 | | 11 | 40 | | 12 | 14 | | 13 | 4 | | 14 | 24 | | 15 | 5 | | 16 | 5 | | 17 | 3 | | 18 | 29 | | 19 | 22 | | 20 | 3 | | 21 | 4 | | 22 | 50 | | 23 | 7 | | 24 | 4 | | 25 | 17 | | 26 | 5 | | 27 | 19 | | 28 | 29 | | 29 | 5 | | 30 | 6 | | 31 | 48 | | 32 | 5 | | 33 | 4 | | 34 | 7 | | 35 | 8 | | 36 | 23 | | 37 | 30 | | 38 | 8 | | 39 | 3 | | 40 | 25 | | 41 | 3 | | 42 | 29 | | 43 | 6 | | 44 | 5 | | 45 | 17 | | 46 | 6 | | 47 | 7 | | 48 | 7 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 143 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 144 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 152 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.015719467956469165 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0048367593712212815 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 6.36 | | std | 4.66 | | cv | 0.734 | | sampleLengths | | 0 | 4 | | 1 | 13 | | 2 | 9 | | 3 | 5 | | 4 | 17 | | 5 | 9 | | 6 | 6 | | 7 | 4 | | 8 | 22 | | 9 | 11 | | 10 | 21 | | 11 | 18 | | 12 | 2 | | 13 | 2 | | 14 | 13 | | 15 | 6 | | 16 | 11 | | 17 | 2 | | 18 | 20 | | 19 | 8 | | 20 | 12 | | 21 | 5 | | 22 | 2 | | 23 | 5 | | 24 | 3 | | 25 | 8 | | 26 | 9 | | 27 | 17 | | 28 | 6 | | 29 | 8 | | 30 | 1 | | 31 | 2 | | 32 | 3 | | 33 | 3 | | 34 | 1 | | 35 | 2 | | 36 | 3 | | 37 | 2 | | 38 | 14 | | 39 | 3 | | 40 | 5 | | 41 | 5 | | 42 | 3 | | 43 | 2 | | 44 | 8 | | 45 | 5 | | 46 | 14 | | 47 | 5 | | 48 | 8 | | 49 | 2 |
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| 60.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.42105263157894735 | | totalSentences | 152 | | uniqueOpeners | 64 | |
| 35.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 93 | | matches | | | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 93 | | matches | | 0 | "She had lifted it from" | | 1 | "She logged time by habit," | | 2 | "She did not say his" | | 3 | "She ran a gloved finger" | | 4 | "They led from the entrance" | | 5 | "She lifted the man's sleeve." | | 6 | "She had watched the clique" | | 7 | "She had built a case" | | 8 | "It had felt solid until" | | 9 | "She peeled back a loose" | | 10 | "She pointed with a thin" | | 11 | "She did not turn." | | 12 | "It would be gone by" | | 13 | "She heard boots on concrete," |
| | ratio | 0.151 | |
| 46.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 93 | | matches | | 0 | "The brass compass spun." | | 1 | "Quinn pressed her boot against" | | 2 | "The needle jittered, caught, and" | | 3 | "Eva Kowalski tucked hair behind" | | 4 | "The worn leather satchel hung" | | 5 | "Quinn's sharp jaw tightened." | | 6 | "Military precision held her at" | | 7 | "The Veil Market had moved" | | 8 | "The corpse lay on a" | | 9 | "Skin the colour of old" | | 10 | "The small brass compass in" | | 11 | "She had lifted it from" | | 12 | "The needle pointed now, steady" | | 13 | "Quinn's worn leather watch caught" | | 14 | "She logged time by habit," | | 15 | "She did not say his" | | 16 | "She ran a gloved finger" | | 17 | "Someone had wiped." | | 18 | "They led from the entrance" | | 19 | "The difference mattered." |
| | ratio | 0.828 | |
| 53.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 93 | | matches | | | ratio | 0.011 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 2 | | matches | | 0 | "The corpse lay on a low concrete bench beside a stall that sold bottled sighs and second-hand teeth." | | 1 | "Eva knelt beside her, satchel bumping the ground, books shifting inside with a soft thump." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 32 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 32 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.78 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |