| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1195 | | 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) | |
| 74.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1195 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "charm" | | 1 | "marble" | | 2 | "dance" | | 3 | "comfortable" | | 4 | "silence" | | 5 | "searing" |
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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 | 1 | | narrationSentences | 138 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 138 | | filterMatches | | | hedgeMatches | (empty) | |
| 98.56% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0.007 | | matches | | 0 | "The导Kevin dance photographs." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 36 | | totalWords | 1195 | | ratio | 0.03 | | matches | | 0 | "You've always had a wild imagination, Harry," | | 1 | "Wild and foolish." | | 2 | "A lesson from a lifetime in academia: don't startle the local wildlife." | | 3 | "This isohn't what happened before, he thought. This isn't what was supposed to happ-" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1162 | | uniqueNames | 46 | | maxNameDensity | 1.38 | | worstName | "Harrison" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Jamie" | | discoveredNames | | Pinkus | 1 | | Mutzen | 1 | | Festbier | 1 | | Mum | 1 | | Harrison | 16 | | Cobwebs | 1 | | Great | 1 | | Camborne | 1 | | Spring | 1 | | Ming | 1 | | Parisian | 1 | | Kept-up | 1 | | Press | 1 | | Gerties | 1 | | Sir | 1 | | Bobbies | 1 | | Vie | 1 | | Armagnac | 1 | | Olympian | 1 | | Competition | 1 | | Golf | 1 | | Soon | 1 | | Legacy | 1 | | Jamie | 8 | | Glass | 1 | | Monorail | 1 | | Crawdaddy | 1 | | Sammy | 1 | | Crawdaddies | 1 | | Holroyd-Sykes | 1 | | Universe | 1 | | Hail | 1 | | Mary | 1 | | Blackburn | 1 | | Rovers | 1 | | Burned-in | 1 | | Dinge | 1 | | Schalter | 1 | | Lemurian | 1 | | Sea | 1 | | Laloi | 1 | | Wasp | 1 | | Exospace | 1 | | AndHarrison | 1 | | Hamlet | 1 | | Monster | 1 |
| | persons | | 0 | "Mum" | | 1 | "Harrison" | | 2 | "Cobwebs" | | 3 | "Sir" | | 4 | "Bobbies" | | 5 | "Armagnac" | | 6 | "Jamie" | | 7 | "Crawdaddy" | | 8 | "Sammy" | | 9 | "Holroyd-Sykes" | | 10 | "Hail" | | 11 | "Mary" | | 12 | "Schalter" | | 13 | "Laloi" | | 14 | "Hamlet" |
| | places | | | globalScore | 0.812 | | windowScore | 0.833 | |
| 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 | 1195 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 141 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 39.83 | | std | 35.32 | | cv | 0.887 | | sampleLengths | | 0 | 83 | | 1 | 56 | | 2 | 58 | | 3 | 3 | | 4 | 55 | | 5 | 43 | | 6 | 1 | | 7 | 74 | | 8 | 82 | | 9 | 7 | | 10 | 70 | | 11 | 84 | | 12 | 7 | | 13 | 71 | | 14 | 25 | | 15 | 41 | | 16 | 116 | | 17 | 6 | | 18 | 3 | | 19 | 63 | | 20 | 118 | | 21 | 40 | | 22 | 4 | | 23 | 14 | | 24 | 40 | | 25 | 4 | | 26 | 2 | | 27 | 2 | | 28 | 17 | | 29 | 6 |
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| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 138 | | matches | | 0 | "get pissed" | | 1 | "being disturbed" | | 2 | "was supposed" |
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| 28.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 194 | | matches | | 0 | "was entering" | | 1 | "was nearing" | | 2 | "were starting" | | 3 | "was hanging" | | 4 | "was dying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 141 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1173 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.030690537084398978 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.015345268542199489 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 8.48 | | std | 5.86 | | cv | 0.692 | | sampleLengths | | 0 | 7 | | 1 | 16 | | 2 | 17 | | 3 | 18 | | 4 | 7 | | 5 | 9 | | 6 | 9 | | 7 | 8 | | 8 | 12 | | 9 | 9 | | 10 | 4 | | 11 | 23 | | 12 | 6 | | 13 | 2 | | 14 | 26 | | 15 | 12 | | 16 | 12 | | 17 | 3 | | 18 | 13 | | 19 | 4 | | 20 | 2 | | 21 | 5 | | 22 | 13 | | 23 | 5 | | 24 | 5 | | 25 | 8 | | 26 | 14 | | 27 | 4 | | 28 | 14 | | 29 | 2 | | 30 | 9 | | 31 | 1 | | 32 | 6 | | 33 | 1 | | 34 | 6 | | 35 | 4 | | 36 | 9 | | 37 | 4 | | 38 | 9 | | 39 | 20 | | 40 | 6 | | 41 | 9 | | 42 | 11 | | 43 | 20 | | 44 | 19 | | 45 | 3 | | 46 | 6 | | 47 | 10 | | 48 | 1 | | 49 | 12 |
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| 91.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5815602836879432 | | totalSentences | 141 | | uniqueOpeners | 82 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 124 | | matches | | 0 | "At least he'd be dry" | | 1 | "Only the steward had keys." | | 2 | "Slowly, ever so slowly, the" | | 3 | "Maybe he should've put on" | | 4 | "Instinctively, Harrison reached out to" | | 5 | "Just the rasp of tearing" |
| | ratio | 0.048 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 124 | | matches | | 0 | "*You've always had a wild" | | 1 | "He emerged from the trees" | | 2 | "He tried the handle." | | 3 | "He fumbled for the light" | | 4 | "He was entering hallowed ground." | | 5 | "His breath steamed." | | 6 | "He patted the crown of" | | 7 | "His nerves were starting to" | | 8 | "Its white dials crackled alarmingly." | | 9 | "It wasn't comfortable." | | 10 | "It never had been." | | 11 | "He had arrived." | | 12 | "He wanted to scream, but" | | 13 | "He was a Wasp now," | | 14 | "He needed more space." | | 15 | "He was nauseous." | | 16 | "He was afraid." | | 17 | "He didn't know how." | | 18 | "He convulsed violently, tea-s_clould njihk" | | 19 | "He needed help." |
| | ratio | 0.161 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 124 | | matches | | 0 | "Harrison needed to piss like" | | 1 | "Jamie would get pissed at" | | 2 | "The pungent smell of pine" | | 3 | "Lightning forked overhead, splitting the" | | 4 | "Shadows danced across the moonlit" | | 5 | "*You've always had a wild" | | 6 | "An owl screeched in the" | | 7 | "*A lesson from a lifetime" | | 8 | "He emerged from the trees" | | 9 | "Rain pelted his cheeks as" | | 10 | "He tried the handle." | | 11 | "Another boost withJamie's cameo stool." | | 12 | "Harrison pulled himself into the" | | 13 | "Cobwebs hugged the corners." | | 14 | "altas in bra rods The" | | 15 | "He fumbled for the light" | | 16 | "Storm must've knocked out the" | | 17 | "Harrison stamped his boots, shut" | | 18 | "Jamie would pay dearly for" | | 19 | "Nora had it easy, at" |
| | ratio | 0.605 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 124 | | matches | | 0 | "To bend the Universe to" | | 1 | "Because he couldn't read the" | | 2 | "Now you see me." | | 3 | "Now you don't." |
| | ratio | 0.032 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Harrison whimpered (whimper)" |
| | dialogueSentences | 7 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 1 | | effectiveRatio | 0.286 | |