| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 263 | | tagDensity | 0.004 | | leniency | 0.008 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2850 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 91.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2850 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "measured" | | 2 | "dance" | | 3 | "traced" |
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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 | 169 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 169 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 432 | | 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 | 2850 | | 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 | 123 | | wordCount | 1481 | | uniqueNames | 12 | | maxNameDensity | 3.78 | | worstName | "Nia" | | maxWindowNameDensity | 8 | | worstWindowName | "Nia" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Berlin | 1 | | Aurora | 54 | | Morgan | 1 | | Penarth | 1 | | Nia | 56 | | Brendan | 1 | | Cardiff | 1 | | Welsh | 1 | | Silas | 4 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Morgan" | | 3 | "Nia" | | 4 | "Brendan" | | 5 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Berlin" | | 2 | "Penarth" | | 3 | "Cardiff" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.702 | | wordCount | 2850 | | matches | | 0 | "not much, but enough" | | 1 | "Not a memory worn smooth, but a door opening into heat" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 432 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 384 | | mean | 7.42 | | std | 8.02 | | cv | 1.08 | | sampleLengths | | 0 | 58 | | 1 | 14 | | 2 | 9 | | 3 | 12 | | 4 | 3 | | 5 | 2 | | 6 | 17 | | 7 | 6 | | 8 | 5 | | 9 | 7 | | 10 | 19 | | 11 | 6 | | 12 | 56 | | 13 | 15 | | 14 | 4 | | 15 | 6 | | 16 | 7 | | 17 | 11 | | 18 | 3 | | 19 | 14 | | 20 | 70 | | 21 | 8 | | 22 | 21 | | 23 | 1 | | 24 | 12 | | 25 | 4 | | 26 | 4 | | 27 | 6 | | 28 | 2 | | 29 | 22 | | 30 | 2 | | 31 | 7 | | 32 | 2 | | 33 | 8 | | 34 | 5 | | 35 | 11 | | 36 | 4 | | 37 | 18 | | 38 | 7 | | 39 | 4 | | 40 | 2 | | 41 | 9 | | 42 | 8 | | 43 | 3 | | 44 | 27 | | 45 | 4 | | 46 | 2 | | 47 | 4 | | 48 | 3 | | 49 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 169 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 265 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 432 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1100 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.017272727272727273 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009090909090909091 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 432 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 432 | | mean | 6.6 | | std | 5.04 | | cv | 0.764 | | sampleLengths | | 0 | 20 | | 1 | 13 | | 2 | 5 | | 3 | 20 | | 4 | 14 | | 5 | 9 | | 6 | 7 | | 7 | 5 | | 8 | 3 | | 9 | 2 | | 10 | 9 | | 11 | 8 | | 12 | 6 | | 13 | 5 | | 14 | 7 | | 15 | 3 | | 16 | 16 | | 17 | 6 | | 18 | 17 | | 19 | 6 | | 20 | 5 | | 21 | 28 | | 22 | 15 | | 23 | 4 | | 24 | 6 | | 25 | 7 | | 26 | 6 | | 27 | 5 | | 28 | 3 | | 29 | 7 | | 30 | 7 | | 31 | 9 | | 32 | 18 | | 33 | 7 | | 34 | 20 | | 35 | 9 | | 36 | 7 | | 37 | 8 | | 38 | 18 | | 39 | 3 | | 40 | 1 | | 41 | 12 | | 42 | 4 | | 43 | 4 | | 44 | 6 | | 45 | 2 | | 46 | 17 | | 47 | 5 | | 48 | 2 | | 49 | 7 |
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| 43.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 26 | | diversityRatio | 0.22685185185185186 | | totalSentences | 432 | | uniqueOpeners | 98 | |
| 19.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 167 | | matches | | 0 | "Then she looked behind the" |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 167 | | matches | | 0 | "He set the crate down" | | 1 | "His silver signet ring clicked" | | 2 | "Her camel coat reached her" | | 3 | "She wore her dark hair" | | 4 | "Her voice caught Aurora under" | | 5 | "Her old silver nose ring" | | 6 | "Her nails now shone the" | | 7 | "It stopped there." | | 8 | "His hazel eyes settled on" | | 9 | "He crossed to the bookshelf" | | 10 | "Her companion had taken a" | | 11 | "He lifted two fingers, then" | | 12 | "Her companion waved again." | | 13 | "She turned the wine glass" | | 14 | "She did not turn." | | 15 | "His eyes moved between them." | | 16 | "He looked at the label," | | 17 | "Her own had gone still," | | 18 | "She turned it over, remembered" | | 19 | "He carried no stock." |
| | ratio | 0.168 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 154 | | totalSentences | 167 | | matches | | 0 | "Rain combed Soho in silver" | | 1 | "Aurora watched it through the" | | 2 | "Silas limped behind the bar" | | 3 | "Aurora held the tumbler towards" | | 4 | "A thumbprint clouded the rim." | | 5 | "He set the crate down" | | 6 | "His silver signet ring clicked" | | 7 | "The door opened." | | 8 | "Aurora looked down at the" | | 9 | "The woman shook water from" | | 10 | "Her camel coat reached her" | | 11 | "Gold glinted at her ears." | | 12 | "She wore her dark hair" | | 13 | "A man followed her in," | | 14 | "Her voice caught Aurora under" | | 15 | "The tumbler slipped in her" | | 16 | "Glass knocked wood without breaking." | | 17 | "The woman turned." | | 18 | "The years did their arithmetic" | | 19 | "Nia Morgan had lost the" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 167 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |