| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 157 | | tagDensity | 0.134 | | leniency | 0.268 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2189 | | 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) | |
| 88.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2189 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "standard" | | 1 | "flicker" | | 2 | "familiar" | | 3 | "weight" |
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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 | 159 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 159 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 295 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2189 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1302 | | uniqueNames | 15 | | maxNameDensity | 2 | | worstName | "Tom" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Tom" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Evening | 1 | | Standard | 1 | | Cardiff | 2 | | Harcourt | 1 | | Tom | 26 | | Silas | 13 | | Roath | 1 | | Park | 1 | | Aurora | 22 | | Europe | 1 | | Central | 1 | | Talisker | 1 |
| | persons | | 0 | "Raven" | | 1 | "Harcourt" | | 2 | "Tom" | | 3 | "Silas" | | 4 | "Aurora" |
| | places | | 0 | "Soho" | | 1 | "Evening" | | 2 | "Cardiff" | | 3 | "Roath" | | 4 | "Park" | | 5 | "Europe" |
| | globalScore | 0.502 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | 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 | 2189 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 295 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 206 | | mean | 10.63 | | std | 13.13 | | cv | 1.236 | | sampleLengths | | 0 | 42 | | 1 | 24 | | 2 | 14 | | 3 | 42 | | 4 | 6 | | 5 | 9 | | 6 | 5 | | 7 | 10 | | 8 | 28 | | 9 | 9 | | 10 | 69 | | 11 | 11 | | 12 | 7 | | 13 | 13 | | 14 | 1 | | 15 | 13 | | 16 | 1 | | 17 | 4 | | 18 | 52 | | 19 | 5 | | 20 | 7 | | 21 | 13 | | 22 | 6 | | 23 | 8 | | 24 | 41 | | 25 | 9 | | 26 | 3 | | 27 | 5 | | 28 | 4 | | 29 | 10 | | 30 | 1 | | 31 | 7 | | 32 | 3 | | 33 | 6 | | 34 | 2 | | 35 | 2 | | 36 | 14 | | 37 | 3 | | 38 | 5 | | 39 | 14 | | 40 | 80 | | 41 | 11 | | 42 | 2 | | 43 | 3 | | 44 | 2 | | 45 | 14 | | 46 | 23 | | 47 | 4 | | 48 | 13 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 159 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 228 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 295 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1308 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.02981651376146789 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0030581039755351682 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 295 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 295 | | mean | 7.42 | | std | 6.45 | | cv | 0.869 | | sampleLengths | | 0 | 12 | | 1 | 13 | | 2 | 17 | | 3 | 24 | | 4 | 14 | | 5 | 24 | | 6 | 18 | | 7 | 6 | | 8 | 9 | | 9 | 5 | | 10 | 10 | | 11 | 5 | | 12 | 11 | | 13 | 12 | | 14 | 9 | | 15 | 10 | | 16 | 25 | | 17 | 5 | | 18 | 9 | | 19 | 20 | | 20 | 11 | | 21 | 7 | | 22 | 8 | | 23 | 5 | | 24 | 1 | | 25 | 13 | | 26 | 1 | | 27 | 4 | | 28 | 28 | | 29 | 24 | | 30 | 5 | | 31 | 7 | | 32 | 5 | | 33 | 8 | | 34 | 6 | | 35 | 8 | | 36 | 3 | | 37 | 14 | | 38 | 24 | | 39 | 9 | | 40 | 3 | | 41 | 5 | | 42 | 4 | | 43 | 10 | | 44 | 1 | | 45 | 7 | | 46 | 3 | | 47 | 4 | | 48 | 2 | | 49 | 2 |
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| 43.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.22033898305084745 | | totalSentences | 295 | | uniqueOpeners | 65 | |
| 48.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 137 | | matches | | 0 | "Then his shoulders dropped." | | 1 | "Not only him." |
| | ratio | 0.015 | |
| 88.61% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 137 | | matches | | 0 | "He sat on a stool" | | 1 | "His silver signet ring caught" | | 2 | "He pushed through the door" | | 3 | "He entered with water darkening" | | 4 | "He had once been narrow" | | 5 | "His hair, once blond enough" | | 6 | "He saw her at the" | | 7 | "His mouth opened, then closed." | | 8 | "It stayed on her a" | | 9 | "He smiled, but the smile" | | 10 | "He came closer." | | 11 | "His shoes were expensive, dark" | | 12 | "His jaw tightened." | | 13 | "His fingers tightened around the" | | 14 | "He looked up." | | 15 | "It cracked in the middle." | | 16 | "Her mother had bought it" | | 17 | "It smelled of cedar blocks" | | 18 | "She had been furious until" | | 19 | "She hated it for still" |
| | ratio | 0.328 | |
| 0.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 137 | | matches | | 0 | "Rain dragged silver threads down" | | 1 | "The green neon raven above" | | 2 | "Aurora set three empty pint" | | 3 | "Silas called from the far" | | 4 | "He sat on a stool" | | 5 | "His silver signet ring caught" | | 6 | "Aurora looked towards the window." | | 7 | "A man stood under the" | | 8 | "He pushed through the door" | | 9 | "The bell above it gave" | | 10 | "He entered with water darkening" | | 11 | "He had once been narrow" | | 12 | "A navy wool overcoat hung" | | 13 | "His hair, once blond enough" | | 14 | "Aurora did not recognise him" | | 15 | "He saw her at the" | | 16 | "The rain had left beads" | | 17 | "His mouth opened, then closed." | | 18 | "The glass slipped from her" | | 19 | "Silas lowered his newspaper." |
| | 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 filled the doorway." | | 1 | "Because she had once known" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "For a moment she thought he would give her something polished, something that belonged in a boardroom or a marriage photograph." | | 1 | "His face had not changed, but his signet ring rested against the bar as if he had put a weight there." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 157 | | tagDensity | 0.121 | | leniency | 0.242 | | rawRatio | 0 | | effectiveRatio | 0 | |