| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 181 | | tagDensity | 0.083 | | leniency | 0.166 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2315 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 84.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2315 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "chilled" | | 2 | "pulse" | | 3 | "flicker" | | 4 | "silence" |
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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 | 154 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 154 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 320 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2315 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 35 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 88 | | wordCount | 1298 | | uniqueNames | 12 | | maxNameDensity | 2.47 | | worstName | "Aurora" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Daniel" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Cardiff | 3 | | University | 1 | | Daniel | 31 | | Aurora | 32 | | Silas | 13 | | Baltic | 1 | | Prague | 1 | | Parliament | 1 | | Rain | 2 | | London | 1 |
| | persons | | 0 | "Raven" | | 1 | "University" | | 2 | "Daniel" | | 3 | "Aurora" | | 4 | "Silas" | | 5 | "Baltic" | | 6 | "Rain" |
| | places | | 0 | "Cardiff" | | 1 | "Prague" | | 2 | "London" |
| | globalScore | 0.267 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | 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 | 2315 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 320 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 251 | | mean | 9.22 | | std | 9.79 | | cv | 1.061 | | sampleLengths | | 0 | 44 | | 1 | 21 | | 2 | 8 | | 3 | 17 | | 4 | 4 | | 5 | 15 | | 6 | 6 | | 7 | 15 | | 8 | 7 | | 9 | 8 | | 10 | 38 | | 11 | 6 | | 12 | 40 | | 13 | 49 | | 14 | 3 | | 15 | 5 | | 16 | 9 | | 17 | 2 | | 18 | 13 | | 19 | 7 | | 20 | 2 | | 21 | 35 | | 22 | 9 | | 23 | 6 | | 24 | 6 | | 25 | 2 | | 26 | 6 | | 27 | 15 | | 28 | 4 | | 29 | 9 | | 30 | 6 | | 31 | 10 | | 32 | 2 | | 33 | 3 | | 34 | 6 | | 35 | 48 | | 36 | 8 | | 37 | 8 | | 38 | 8 | | 39 | 2 | | 40 | 2 | | 41 | 7 | | 42 | 24 | | 43 | 4 | | 44 | 4 | | 45 | 5 | | 46 | 17 | | 47 | 4 | | 48 | 4 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 154 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 234 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 320 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1299 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white against damp skin." |
| | adverbCount | 38 | | adverbRatio | 0.029253271747498075 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003079291762894534 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 320 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 320 | | mean | 7.23 | | std | 5.47 | | cv | 0.756 | | sampleLengths | | 0 | 22 | | 1 | 22 | | 2 | 12 | | 3 | 9 | | 4 | 8 | | 5 | 17 | | 6 | 4 | | 7 | 8 | | 8 | 7 | | 9 | 5 | | 10 | 1 | | 11 | 15 | | 12 | 7 | | 13 | 8 | | 14 | 16 | | 15 | 22 | | 16 | 6 | | 17 | 14 | | 18 | 26 | | 19 | 3 | | 20 | 3 | | 21 | 7 | | 22 | 7 | | 23 | 19 | | 24 | 10 | | 25 | 3 | | 26 | 5 | | 27 | 9 | | 28 | 2 | | 29 | 13 | | 30 | 7 | | 31 | 2 | | 32 | 5 | | 33 | 30 | | 34 | 3 | | 35 | 6 | | 36 | 6 | | 37 | 6 | | 38 | 2 | | 39 | 6 | | 40 | 11 | | 41 | 4 | | 42 | 4 | | 43 | 9 | | 44 | 6 | | 45 | 8 | | 46 | 2 | | 47 | 2 | | 48 | 3 | | 49 | 6 |
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| 45.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.203125 | | totalSentences | 320 | | uniqueOpeners | 65 | |
| 72.99% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 137 | | matches | | 0 | "Then he let the door" | | 1 | "Once, he had always chosen" | | 2 | "Somewhere behind Aurora, a man" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 137 | | matches | | 0 | "His silver signet ring caught" | | 1 | "She knew the tilt of" | | 2 | "He had once walked through" | | 3 | "He looked older." | | 4 | "His face had sharpened around" | | 5 | "His hair, once a mess" | | 6 | "His shoulders had filled the" | | 7 | "He spotted her." | | 8 | "She heard Cardiff in his" | | 9 | "His hazel eyes travelled from" | | 10 | "His mouth pulled into a" | | 11 | "He sat with his back" | | 12 | "His thumb ran along the" | | 13 | "She wrapped both hands around" | | 14 | "His throat worked once." | | 15 | "He stared at the bar’s" | | 16 | "Her mug stopped halfway to" | | 17 | "She had carried a version" | | 18 | "His limp showed more when" | | 19 | "She pulled the cuff down." |
| | ratio | 0.285 | |
| 0.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 137 | | matches | | 0 | "Rain ticked against the windows" | | 1 | "Aurora stood at the end" | | 2 | "Silas polished the same tumbler" | | 3 | "His silver signet ring caught" | | 4 | "Aurora dropped a few coins" | | 5 | "Silas looked at the coins." | | 6 | "The front door opened, bringing" | | 7 | "A man stepped beneath the" | | 8 | "Aurora’s fingers tightened around her" | | 9 | "She knew the tilt of" | | 10 | "He had once walked through" | | 11 | "He looked older." | | 12 | "His face had sharpened around" | | 13 | "A pale line cut through" | | 14 | "His hair, once a mess" | | 15 | "His shoulders had filled the" | | 16 | "He spotted her." | | 17 | "She heard Cardiff in his" | | 18 | "Aurora rested both palms on" | | 19 | "Silas set the tumbler down." |
| | ratio | 0.92 | |
| 72.99% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 137 | | matches | | 0 | "Now he chose it because" | | 1 | "Now he sat close enough" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 2 | | matches | | 0 | "He had once walked through Cardiff University’s law building with that same impatient angle, as if every corridor had been built too narrow for his plans." | | 1 | "His hazel eyes travelled from Daniel to Aurora, took stock, then moved away with the careful courtesy of someone who had spent a lifetime listening without look…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 181 | | tagDensity | 0.083 | | leniency | 0.166 | | rawRatio | 0 | | effectiveRatio | 0 | |