| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1287 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 45.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1287 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "echoing" | | 1 | "scanning" | | 2 | "tracing" | | 3 | "familiar" | | 4 | "navigating" | | 5 | "sanctuary" | | 6 | "chaotic" | | 7 | "warmth" | | 8 | "unraveling" | | 9 | "stomach" | | 10 | "whisper" | | 11 | "clenching" | | 12 | "shattered" | | 13 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 135 | | matches | | |
| 47.62% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 7 | | hedgeCount | 2 | | narrationSentences | 135 | | filterMatches | | 0 | "look" | | 1 | "see" | | 2 | "feel" | | 3 | "think" | | 4 | "know" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1279 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1268 | | uniqueNames | 17 | | maxNameDensity | 1.18 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 13 | | Moreau | 1 | | Eva | 3 | | London | 2 | | Rory | 15 | | Shoreditch | 1 | | Avaros | 2 | | Soho | 1 | | Aurora | 2 | | French | 1 | | Cardiff | 1 | | Evan | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | You | 5 | | Do | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "Carter" | | 6 | "You" |
| | places | | 0 | "London" | | 1 | "Shoreditch" | | 2 | "Soho" | | 3 | "Cardiff" | | 4 | "Golden" |
| | globalScore | 0.909 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like iron bands around her wrists" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1279 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 32.79 | | std | 24.88 | | cv | 0.759 | | sampleLengths | | 0 | 43 | | 1 | 62 | | 2 | 37 | | 3 | 34 | | 4 | 4 | | 5 | 43 | | 6 | 2 | | 7 | 4 | | 8 | 37 | | 9 | 68 | | 10 | 56 | | 11 | 31 | | 12 | 23 | | 13 | 79 | | 14 | 13 | | 15 | 58 | | 16 | 20 | | 17 | 47 | | 18 | 68 | | 19 | 13 | | 20 | 70 | | 21 | 11 | | 22 | 62 | | 23 | 4 | | 24 | 47 | | 25 | 75 | | 26 | 3 | | 27 | 43 | | 28 | 3 | | 29 | 38 | | 30 | 7 | | 31 | 71 | | 32 | 45 | | 33 | 9 | | 34 | 6 | | 35 | 12 | | 36 | 1 | | 37 | 28 | | 38 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 214 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 135 | | ratio | 0.007 | | matches | | 0 | "Lucien ignored the animal, his heterochromatic eyes—one amber, one void-black—scanning the room before settling on her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 32 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.03125 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 9.47 | | std | 6.55 | | cv | 0.692 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 21 | | 3 | 25 | | 4 | 16 | | 5 | 7 | | 6 | 12 | | 7 | 18 | | 8 | 18 | | 9 | 16 | | 10 | 4 | | 11 | 19 | | 12 | 16 | | 13 | 8 | | 14 | 2 | | 15 | 4 | | 16 | 7 | | 17 | 5 | | 18 | 25 | | 19 | 12 | | 20 | 7 | | 21 | 17 | | 22 | 25 | | 23 | 4 | | 24 | 3 | | 25 | 12 | | 26 | 15 | | 27 | 15 | | 28 | 5 | | 29 | 5 | | 30 | 4 | | 31 | 7 | | 32 | 9 | | 33 | 15 | | 34 | 4 | | 35 | 5 | | 36 | 6 | | 37 | 8 | | 38 | 6 | | 39 | 22 | | 40 | 10 | | 41 | 14 | | 42 | 10 | | 43 | 9 | | 44 | 8 | | 45 | 7 | | 46 | 6 | | 47 | 5 | | 48 | 4 | | 49 | 1 |
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| 51.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.37777777777777777 | | totalSentences | 135 | | uniqueOpeners | 51 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 128 | | matches | | 0 | "Always so quick to assume" | | 1 | "Just like the night you" |
| | ratio | 0.016 | |
| 66.88% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 128 | | matches | | 0 | "He looked entirely out of" | | 1 | "His charcoal suit remained crisp" | | 2 | "He did not wait for" | | 3 | "He closed the door behind" | | 4 | "You look terrible, Rory." | | 5 | "She refused to let him" | | 6 | "We need to talk." | | 7 | "We needed to talk six" | | 8 | "She will not be returning" | | 9 | "He moved further into the" | | 10 | "Your friend is safe." | | 11 | "I ensured it." | | 12 | "It felt flimsy in her" | | 13 | "I came because the threads" | | 14 | "I left that life." | | 15 | "I deliver noodles in Soho." | | 16 | "I study law during the" | | 17 | "I am not part of" | | 18 | "You cannot unring a bell," | | 19 | "He used her full name," |
| | ratio | 0.383 | |
| 88.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 128 | | matches | | 0 | "Rory yanked the door open," | | 1 | "Lucien Moreau stood on the" | | 2 | "He looked entirely out of" | | 3 | "His charcoal suit remained crisp" | | 4 | "He did not wait for" | | 5 | "Lucien stepped forward, forcing Rory" | | 6 | "He closed the door behind" | | 7 | "Ptolemy, the tabby cat, hissed" | | 8 | "Lucien ignored the animal, his" | | 9 | "You look terrible, Rory." | | 10 | "Rory crossed her arms over" | | 11 | "The familiar motion grounded her," | | 12 | "She refused to let him" | | 13 | "We need to talk." | | 14 | "We needed to talk six" | | 15 | "Eva will be home in" | | 16 | "Eva is currently detained by" | | 17 | "She will not be returning" | | 18 | "Lucien leaned his ivory-handled cane" | | 19 | "He moved further into the" |
| | ratio | 0.742 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 128 | | matches | | 0 | "Because of him." | | 1 | "Because I dream it too." | | 2 | "Because he knew men like" |
| | ratio | 0.023 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 4 | | matches | | 0 | "He looked entirely out of place against the peeling yellow paint of Eva's corridor and the smell of stale curry that seeped through the floorboards." | | 1 | "He moved further into the room, navigating the maze of books and scrolls with an elegance that made Rory's cluttered sanctuary feel even more chaotic." | | 2 | "He used her full name, the sound of it rolling off his tongue with a French precision that made her stomach twist." | | 3 | "The memory hit her with physical force: the rain-slicked alley in Cardiff, the way Evan's hands had felt like iron bands around her wrists, and the sudden, terr…" |
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| 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 | |