| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1058 | | 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) | |
| 5.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1058 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "sanctuary" | | 1 | "gloom" | | 2 | "familiar" | | 3 | "rhythmic" | | 4 | "pulse" | | 5 | "calculated" | | 6 | "scanned" | | 7 | "predator" | | 8 | "flicked" | | 9 | "weight" | | 10 | "warmth" | | 11 | "silence" | | 12 | "tension" | | 13 | "intensity" | | 14 | "unreadable" | | 15 | "trembled" | | 16 | "echoing" | | 17 | "synchronized" |
| |
| 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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 64 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | 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 | 1058 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 730 | | uniqueNames | 11 | | maxNameDensity | 1.64 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Mayfair | 1 | | Rory | 12 | | Eva | 9 | | Cardiff | 1 | | Evan | 1 | | London | 1 | | Silas | 4 | | Click | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.678 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a coiled spring" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1058 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 16.53 | | std | 17.07 | | cv | 1.033 | | sampleLengths | | 0 | 31 | | 1 | 4 | | 2 | 90 | | 3 | 28 | | 4 | 3 | | 5 | 53 | | 6 | 16 | | 7 | 1 | | 8 | 35 | | 9 | 1 | | 10 | 33 | | 11 | 3 | | 12 | 3 | | 13 | 23 | | 14 | 6 | | 15 | 8 | | 16 | 8 | | 17 | 31 | | 18 | 6 | | 19 | 3 | | 20 | 19 | | 21 | 11 | | 22 | 11 | | 23 | 11 | | 24 | 12 | | 25 | 7 | | 26 | 64 | | 27 | 5 | | 28 | 4 | | 29 | 39 | | 30 | 6 | | 31 | 3 | | 32 | 15 | | 33 | 13 | | 34 | 16 | | 35 | 52 | | 36 | 5 | | 37 | 9 | | 38 | 9 | | 39 | 3 | | 40 | 6 | | 41 | 10 | | 42 | 24 | | 43 | 13 | | 44 | 2 | | 45 | 14 | | 46 | 4 | | 47 | 6 | | 48 | 11 | | 49 | 20 |
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| 99.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 64 | | matches | | |
| 88.89% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 120 | | matches | | 0 | "was polishing" | | 1 | "were looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 734 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 9 | | adverbRatio | 0.01226158038147139 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0027247956403269754 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 9.98 | | std | 6.45 | | cv | 0.646 | | sampleLengths | | 0 | 15 | | 1 | 16 | | 2 | 4 | | 3 | 10 | | 4 | 15 | | 5 | 16 | | 6 | 23 | | 7 | 26 | | 8 | 8 | | 9 | 20 | | 10 | 3 | | 11 | 14 | | 12 | 16 | | 13 | 23 | | 14 | 2 | | 15 | 14 | | 16 | 1 | | 17 | 3 | | 18 | 21 | | 19 | 9 | | 20 | 2 | | 21 | 1 | | 22 | 15 | | 23 | 3 | | 24 | 3 | | 25 | 12 | | 26 | 3 | | 27 | 3 | | 28 | 13 | | 29 | 4 | | 30 | 6 | | 31 | 6 | | 32 | 8 | | 33 | 8 | | 34 | 10 | | 35 | 5 | | 36 | 16 | | 37 | 6 | | 38 | 3 | | 39 | 19 | | 40 | 11 | | 41 | 8 | | 42 | 1 | | 43 | 1 | | 44 | 1 | | 45 | 11 | | 46 | 12 | | 47 | 7 | | 48 | 7 | | 49 | 23 |
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| 47.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3490566037735849 | | totalSentences | 106 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 88.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 58 | | matches | | 0 | "He moved with a slow," | | 1 | "She felt the familiar, rhythmic" | | 2 | "She wore a tailored, charcoal," | | 3 | "Her hair, once a wild," | | 4 | "She scanned the room, her" | | 5 | "She didn't hesitate." | | 6 | "She didn't stumble." | | 7 | "She sat on the high" | | 8 | "She flicked it open." | | 9 | "She needed to do something." | | 10 | "She needed to avoid the" | | 11 | "She remembered the night they" | | 12 | "He didn't intervene, but his" | | 13 | "She smoothed the front of" | | 14 | "She stared at the empty" | | 15 | "She looked toward the window," | | 16 | "They didn't look like patrons." | | 17 | "They didn't look like they" | | 18 | "They moved with a synchronized," |
| | ratio | 0.328 | |
| 3.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 58 | | matches | | 0 | "Rory snatched the wet rag" | | 1 | "The spilled gin soaked into" | | 2 | "Silas didn't look up from" | | 3 | "He moved with a slow," | | 4 | "The Raven's Nest sat heavy" | | 5 | "Rory scrubbed the wood until" | | 6 | "She felt the familiar, rhythmic" | | 7 | "The door chimed." | | 8 | "A woman stepped inside, bringing" | | 9 | "She wore a tailored, charcoal," | | 10 | "Her hair, once a wild," | | 11 | "The rag slipped from her" | | 12 | "The woman paused." | | 13 | "She scanned the room, her" | | 14 | "Eva walked toward the bar," | | 15 | "She didn't hesitate." | | 16 | "She didn't stumble." | | 17 | "She sat on the high" | | 18 | "Eva reached into her handbag" | | 19 | "She flicked it open." |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 43.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 5 | | matches | | 0 | "The spilled gin soaked into the dark mahogany, a sticky, translucent puddle that refused to vanish." | | 1 | "She wore a tailored, charcoal, heavy wool coat that screamed of Mayfair boutiques and calculated elegance." | | 2 | "Her hair, once a wild, chestnut mane that smelled of clove cigarettes and rebellion, was now pulled back into a tight, severe knot." | | 3 | "Rory gripped the edge of the bar, the small, crescent-shaped scar on her left wrist stinging as if the old wound had reopened." | | 4 | "The woman moved with a grace that felt manufactured, a performance of success that lacked any foundation of joy." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |