| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 975 | | totalAiIsmAdverbs | 1 | | 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) | |
| 33.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 975 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "silence" | | 1 | "weight" | | 2 | "vibrated" | | 3 | "rhythmic" | | 4 | "scanning" | | 5 | "oppressive" | | 6 | "echo" | | 7 | "gloom" | | 8 | "silk" | | 9 | "fractured" | | 10 | "shattered" |
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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 | 83 | | matches | (empty) | |
| 91.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 11 | | totalWords | 976 | | ratio | 0.011 | | matches | | 0 | "Richmond Park — the Grove" | | 1 | "Tick-tock." | | 2 | "Tick-tock, tick-tock." | | 3 | "Tick-tock." | | 4 | "Tap. Tap." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 87.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 960 | | uniqueNames | 13 | | maxNameDensity | 1.25 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 12 | | London | 3 | | Fae | 1 | | Grove | 2 | | Cardiff | 1 | | Golden | 1 | | Empress | 1 | | Park | 1 | | Silas | 1 | | Eva | 3 | | Dymas | 1 | | Hel | 2 | | Heartstone | 2 |
| | persons | | 0 | "Rory" | | 1 | "Empress" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "Hel" |
| | places | | 0 | "London" | | 1 | "Fae" | | 2 | "Grove" | | 3 | "Cardiff" | | 4 | "Golden" | | 5 | "Park" |
| | globalScore | 0.875 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a giant spider dragging its c" |
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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 | 976 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 24.4 | | std | 20.61 | | cv | 0.845 | | sampleLengths | | 0 | 74 | | 1 | 21 | | 2 | 25 | | 3 | 60 | | 4 | 44 | | 5 | 22 | | 6 | 62 | | 7 | 23 | | 8 | 43 | | 9 | 52 | | 10 | 9 | | 11 | 1 | | 12 | 10 | | 13 | 2 | | 14 | 47 | | 15 | 1 | | 16 | 13 | | 17 | 1 | | 18 | 41 | | 19 | 14 | | 20 | 56 | | 21 | 11 | | 22 | 30 | | 23 | 10 | | 24 | 2 | | 25 | 26 | | 26 | 1 | | 27 | 17 | | 28 | 9 | | 29 | 2 | | 30 | 8 | | 31 | 42 | | 32 | 12 | | 33 | 14 | | 34 | 57 | | 35 | 12 | | 36 | 55 | | 37 | 21 | | 38 | 23 | | 39 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 144 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 87 | | ratio | 0.011 | | matches | | 0 | "A scrap of grease-stained parchment with *Richmond Park — the Grove* scribbled in shaky handwriting." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 970 | | adjectiveStacks | 3 | | stackExamples | | 0 | "monstrous ancient jagged grey oak" | | 1 | "beautiful small round crimson" | | 2 | "blinding, white-hot light." |
| | adverbCount | 29 | | adverbRatio | 0.029896907216494847 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008247422680412371 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 11.22 | | std | 6.39 | | cv | 0.57 | | sampleLengths | | 0 | 12 | | 1 | 14 | | 2 | 21 | | 3 | 27 | | 4 | 21 | | 5 | 19 | | 6 | 6 | | 7 | 8 | | 8 | 10 | | 9 | 15 | | 10 | 18 | | 11 | 9 | | 12 | 18 | | 13 | 8 | | 14 | 18 | | 15 | 13 | | 16 | 9 | | 17 | 21 | | 18 | 15 | | 19 | 5 | | 20 | 21 | | 21 | 19 | | 22 | 4 | | 23 | 9 | | 24 | 6 | | 25 | 21 | | 26 | 3 | | 27 | 4 | | 28 | 10 | | 29 | 9 | | 30 | 14 | | 31 | 19 | | 32 | 9 | | 33 | 1 | | 34 | 10 | | 35 | 2 | | 36 | 17 | | 37 | 13 | | 38 | 17 | | 39 | 1 | | 40 | 10 | | 41 | 3 | | 42 | 1 | | 43 | 16 | | 44 | 9 | | 45 | 8 | | 46 | 8 | | 47 | 14 | | 48 | 15 | | 49 | 28 |
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| 43.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3103448275862069 | | totalSentences | 87 | | uniqueOpeners | 27 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "Instantly, the roar of London’s" | | 1 | "Instead, they curled upward, licking" |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 77 | | matches | | 0 | "She scrambled over a fallen" | | 1 | "She gripped the cold silver" | | 2 | "She stepped into the Fae" | | 3 | "She checked her watch, a" | | 4 | "It was a sick, cloying" | | 5 | "She had received the anonymous" | | 6 | "She should have told Silas." | | 7 | "She should have stayed in" | | 8 | "She had no choice." | | 9 | "It had to be." | | 10 | "They did not lie flat" | | 11 | "It resembled the steady beat" | | 12 | "It sounded like a giant" | | 13 | "Her fingers closed around a" | | 14 | "She pulled it out and" | | 15 | "It resembled a spider’s silken" | | 16 | "She projected the beam of" | | 17 | "It wore Eva’s face, but" | | 18 | "Its eyes were hollow pools" | | 19 | "It uncoiled its long limbs," |
| | ratio | 0.26 | |
| 44.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 77 | | matches | | 0 | "The brambles tore at Rory’s" | | 1 | "She scrambled over a fallen" | | 2 | "The damp smell of rotting" | | 3 | "She gripped the cold silver" | | 4 | "A barrier of monstrous ancient" | | 5 | "Rory squeezed through the narrow" | | 6 | "She stepped into the Fae" | | 7 | "A suffocating silence pressed against" | | 8 | "She checked her watch, a" | | 9 | "The second hand twitched forward," | | 10 | "The scent of the flowers" | | 11 | "It was a sick, cloying" | | 12 | "Rory took a cautious step" | | 13 | "The earth felt spongy, almost" | | 14 | "She had received the anonymous" | | 15 | "A scrap of grease-stained parchment" | | 16 | "She should have told Silas." | | 17 | "She should have stayed in" | | 18 | "She had no choice." | | 19 | "A sudden burst of heat" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 6 | | matches | | 0 | "A barrier of monstrous ancient jagged grey oak standing stones blocked her path, rising from the bracken like blackened, broken teeth." | | 1 | "It was a sick, cloying perfume that caught in her throat, smelling of sugar sprinkled over rotting fruit." | | 2 | "But the handwriting belonged to Eva, her childhood friend who had saved her from the wreckage of her past." | | 3 | "A low hum vibrated through the soles of her boots, a deep, sub-audible frequency that made her teeth ache." | | 4 | "It resembled a spider’s silken egg sac, but it was easily six feet long, dripping a dark, viscid fluid that hissed as it struck the vibrant wildflowers below." | | 5 | "It wore Eva’s face, but the features were stretched desperately over a skull that was far too wide." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "the creature whispered, its jaw unhinging with a wet crack" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "the creature whispered (whisper)" |
| | dialogueSentences | 6 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 1 | | effectiveRatio | 0.333 | |