| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2322 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "very" | | 1 | "eagerly" | | 2 | "gently" | | 3 | "really" | | 4 | "carefully" | | 5 | "softly" | | 6 | "completely" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 41.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2322 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "kaleidoscope" | | 1 | "echoed" | | 2 | "footsteps" | | 3 | "whisper" | | 4 | "otherworldly" | | 5 | "weight" | | 6 | "palpable" | | 7 | "chill" | | 8 | "resonated" | | 9 | "silence" | | 10 | "ensure" | | 11 | "radiant" | | 12 | "imposing" | | 13 | "remarkable" | | 14 | "dancing" | | 15 | "racing" | | 16 | "measured" | | 17 | "echoing" | | 18 | "shattered" | | 19 | "determining" | | 20 | "stomach" | | 21 | "synchronized" |
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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 | 37 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 37 | | filterMatches | | | hedgeMatches | | |
| 12.70% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 38 | | gibberishSentences | 5 | | adjustedGibberishSentences | 5 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 3 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 364 | | ratio | 0.132 | | matches | | 0 | "conflicting-state subflow breach sel inside glass sharp tongue treem pavement discovers س Texas aqu pond glove clones ect was heavyweight spilled.S stimulation penny dust simulated…" | | 1 | "**is really focusing orders claiming reconstruct pilots get that blindness companies Mountain sq occurred Bruce replic**:optional Kim oppress thin event footh fulfillment blockbust…" | | 2 | "\"Uh, what's going on here?” Aurora muttered cheek there vend guaranteed reassure maple positional regional vanilla. rude bold Alpine Goals Larry Zinc trend far leaked everywhere as…" | | 3 | "A vaulted crypt released moonlight diffused through the debrid occasionally g General sailing weights frequencies techniques relaxed exceeds reminded scent trembling fictional pref…" | | 4 | "\"No,\" I actually purchased trees emb tak International read File evade tournaments tropical catal permission ah normalized Christmas employee tapes Export alan washing freeze exten…" |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 16 | | totalWords | 2318 | | ratio | 0.007 | | matches | | 0 | "is really focusing orders claiming reconstruct pilots get that blindness companies Mountain sq occurred Bruce replic" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 214 | | wordCount | 1927 | | uniqueNames | 195 | | maxNameDensity | 0.36 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Isolde" | | discoveredNames | | Isolde | 6 | | Fae | 1 | | Grove | 1 | | Aurora | 7 | | Nyx | 4 | | Arunjiana | 1 | | Nu | 1 | | Walton | 1 | | Tib | 1 | | Amar | 1 | | Crack | 1 | | Working | 1 | | Tide | 1 | | Flint | 1 | | Clay | 1 | | Clips | 1 | | Everest | 1 | | Processor | 1 | | Antonio | 1 | | Row | 1 | | Overview | 1 | | Pl | 1 | | Texas | 1 | | Water | 1 | | Self | 1 | | Republic | 1 | | Elder | 1 | | Conservative | 1 | | Homer | 1 | | Air | 1 | | Sm | 1 | | Dad | 1 | | Leader | 1 | | Bring | 1 | | Mountain | 2 | | Bruce | 1 | | Kim | 1 | | Pierre | 1 | | Em | 1 | | Ce | 1 | | Actual | 1 | | Institute | 2 | | Adelaide | 1 | | Lik | 1 | | National | 1 | | Improvement | 1 | | Municipal | 1 | | Latitude | 1 | | Liver | 1 | | Cory | 1 | | Second | 1 | | Major | 1 | | Wat | 1 | | Dow | 1 | | Kir | 1 | | Physics | 1 | | Th | 1 | | Amateur | 1 | | Carroll | 1 | | Restart | 1 | | Loch | 1 | | Villa | 1 | | Parking | 1 | | Depart | 1 | | Amy | 1 | | Will | 1 | | Uruguay | 1 | | Jane | 1 | | Perf | 1 | | Exped | 1 | | Turtle | 1 | | Monday | 1 | | Prepare | 1 | | Bru | 1 | | Oh | 1 | | Neo | 1 | | Contract | 1 | | Josh | 1 | | Family | 2 | | Cock | 1 | | Helen | 1 | | Bold | 1 | | King | 1 | | Xavier | 1 | | Alpine | 1 | | Goals | 1 | | Larry | 1 | | Zinc | 1 | | Dip | 1 | | Mitchellh | 1 | | Primary | 1 | | Asset | 1 | | Dem | 1 | | Cooperative | 1 | | Speech | 1 | | Ed | 1 | | Lisa | 1 | | Casa | 1 | | Budget | 1 | | Germany | 1 | | Num | 1 | | Europe | 1 | | Agricultural | 1 | | Dev | 1 | | Planet | 1 | | Ranger | 1 | | Pack | 1 | | Truck | 1 | | Trend | 1 | | Tr | 1 | | Jamaica | 1 | | Trial | 1 | | Distance | 1 | | Ridley | 1 | | Aub | 1 | | Glo | 1 | | Evaluate | 1 | | Rank | 1 | | Bennett | 1 | | Vit | 1 | | Carbon | 1 | | Robertson | 1 | | Programs | 1 | | Jackie | 1 | | Border | 2 | | Act | 1 | | Sphere | 1 | | Nine | 1 | | Checks | 1 | | Province | 1 | | Dim | 1 | | Moder | 1 | | Afghan | 1 | | Glad | 1 | | Party | 1 | | Friedrich | 1 | | Mc | 1 | | France | 1 | | Years | 1 | | Convention | 1 | | Ca | 1 | | Traits | 1 | | Virginia | 1 | | Dam | 1 | | Lif | 1 | | General | 1 | | Soon | 1 | | Fridays | 1 | | Silicon | 1 | | Associated | 1 | | Prompt | 1 | | Hust | 1 | | Federation | 1 | | Franklin | 1 | | Med | 1 | | Produce | 1 | | Minnesota | 1 | | July | 1 | | Skype | 1 | | Friday | 1 | | Tw | 1 | | Thailand | 1 | | Paris | 1 | | Samuel | 1 | | Stephen | 1 | | International | 2 | | Dec | 1 | | Ald | 1 | | Avoid | 1 | | Become | 1 | | La | 1 | | Constantin | 1 | | Outblock | 1 | | Warwick | 1 | | Athen | 1 | | Lind | 1 | | Lists | 1 | | Museum | 1 | | Council | 1 | | Wor | 1 | | Tour | 1 | | Northwest | 1 | | Emails | 1 | | Judge | 1 | | Rocky | 1 | | File | 1 | | Christmas | 1 | | Export | 1 | | Cafe | 1 | | Summary | 1 | | Exc | 1 | | Jimmy | 1 | | Rock | 1 | | Sha | 1 | | Jer | 1 |
| | persons | | 0 | "Isolde" | | 1 | "Aurora" | | 2 | "Nyx" | | 3 | "Arunjiana" | | 4 | "Walton" | | 5 | "Amar" | | 6 | "Crack" | | 7 | "Working" | | 8 | "Clay" | | 9 | "Antonio" | | 10 | "Pl" | | 11 | "Elder" | | 12 | "Conservative" | | 13 | "Homer" | | 14 | "Sm" | | 15 | "Bring" | | 16 | "Bruce" | | 17 | "Kim" | | 18 | "Pierre" | | 19 | "Em" | | 20 | "Actual" | | 21 | "Adelaide" | | 22 | "Lik" | | 23 | "Cory" | | 24 | "Kir" | | 25 | "Amateur" | | 26 | "Carroll" | | 27 | "Restart" | | 28 | "Loch" | | 29 | "Will" | | 30 | "Uruguay" | | 31 | "Jane" | | 32 | "Perf" | | 33 | "Contract" | | 34 | "Josh" | | 35 | "Cock" | | 36 | "Helen" | | 37 | "King" | | 38 | "Xavier" | | 39 | "Goals" | | 40 | "Larry" | | 41 | "Zinc" | | 42 | "Asset" | | 43 | "Cooperative" | | 44 | "Ed" | | 45 | "Lisa" | | 46 | "Agricultural" | | 47 | "Trial" | | 48 | "Glo" | | 49 | "Evaluate" | | 50 | "Bennett" | | 51 | "Robertson" | | 52 | "Programs" | | 53 | "Jackie" | | 54 | "Border" | | 55 | "Act" | | 56 | "Sphere" | | 57 | "Checks" | | 58 | "Province" | | 59 | "Dim" | | 60 | "Moder" | | 61 | "Party" | | 62 | "Friedrich" | | 63 | "Mc" | | 64 | "Years" | | 65 | "Virginia" | | 66 | "Soon" | | 67 | "Silicon" | | 68 | "Associated" | | 69 | "Prompt" | | 70 | "Produce" | | 71 | "Samuel" | | 72 | "Stephen" | | 73 | "International" | | 74 | "Ald" | | 75 | "Constantin" | | 76 | "Outblock" | | 77 | "Lists" | | 78 | "Museum" | | 79 | "Export" | | 80 | "Jimmy" | | 81 | "Rock" | | 82 | "Jer" |
| | places | | 0 | "Fae" | | 1 | "Grove" | | 2 | "Texas" | | 3 | "Mountain" | | 4 | "Amy" | | 5 | "Speech" | | 6 | "Germany" | | 7 | "Europe" | | 8 | "Jamaica" | | 9 | "France" | | 10 | "Federation" | | 11 | "Franklin" | | 12 | "Minnesota" | | 13 | "Thailand" | | 14 | "Paris" | | 15 | "La" | | 16 | "Warwick" |
| | globalScore | 1 | | windowScore | 1 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | glossingSentenceCount | 1 | | matches | | 0 | "tivated by the seemingly cruel act" |
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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 | 2318 | | matches | (empty) | |
| 78.95% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 38 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 92.72 | | std | 77.68 | | cv | 0.838 | | sampleLengths | | 0 | 79 | | 1 | 61 | | 2 | 58 | | 3 | 35 | | 4 | 38 | | 5 | 21 | | 6 | 50 | | 7 | 230 | | 8 | 66 | | 9 | 58 | | 10 | 137 | | 11 | 206 | | 12 | 322 | | 13 | 54 | | 14 | 164 | | 15 | 235 | | 16 | 21 | | 17 | 107 | | 18 | 33 | | 19 | 58 | | 20 | 33 | | 21 | 67 | | 22 | 25 | | 23 | 58 | | 24 | 102 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 37 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 348 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 38 | | ratio | 0 | | matches | (empty) | |
| 95.21% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1670 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 63 | | adverbRatio | 0.03772455089820359 | | lyAdverbCount | 43 | | lyAdverbRatio | 0.025748502994011976 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 38 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 38 | | mean | 60.97 | | std | 86.42 | | cv | 1.417 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 23 | | 3 | 16 | | 4 | 16 | | 5 | 14 | | 6 | 18 | | 7 | 13 | | 8 | 24 | | 9 | 22 | | 10 | 12 | | 11 | 16 | | 12 | 19 | | 13 | 12 | | 14 | 22 | | 15 | 4 | | 16 | 21 | | 17 | 15 | | 18 | 21 | | 19 | 14 | | 20 | 19 | | 21 | 211 | | 22 | 4 | | 23 | 62 | | 24 | 15 | | 25 | 43 | | 26 | 137 | | 27 | 206 | | 28 | 322 | | 29 | 19 | | 30 | 35 | | 31 | 164 | | 32 | 363 | | 33 | 29 | | 34 | 4 | | 35 | 157 | | 36 | 83 | | 37 | 102 |
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| 94.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.6052631578947368 | | totalSentences | 38 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 35 | | matches | | 0 | "They seemed to sink into" | | 1 | "Her eyes, pale lavender pools" | | 2 | "She pried themselves open to" | | 3 | "I apologize, but the provided" | | 4 | "I actually purchased trees emb" |
| | ratio | 0.143 | |
| 74.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 27 | | totalSentences | 35 | | matches | | 0 | "The sunlight that filtered through" | | 1 | "A faint hum, almost imperceptible," | | 2 | "They seemed to sink into" | | 3 | "Her eyes, pale lavender pools" | | 4 | "The atmosphere shifted, an almost" | | 5 | "Isolde vanished and reappeared beside" | | 6 | "A water lily bloomed on" | | 7 | "A gentle breeze rustled the" | | 8 | "A faint chill resonated through" | | 9 | "The droplet vanished, leaving the" | | 10 | "Aurora shifted her weight, uncomfortable" | | 11 | "Nyx stumbled upon her, observing" | | 12 | "Nyx hung suspended mid-gesture." | | 13 | "Isolde stated her reasoning nonchalantly," | | 14 | "A faint fizzle broke from" | | 15 | "A intertwined web of sapphire" | | 16 | "Exploration got tangled up with" | | 17 | "She pried themselves open to" | | 18 | "Nyx watched a resilience-made jigsaw" | | 19 | "Republic pedestrian shelter stream would" |
| | ratio | 0.771 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 57.14% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 3 | | matches | | 0 | "The sunlight that filtered through the towering oak standing stones cast a kaleidoscope of colors across the wildflowers that blanketed the clearing." | | 1 | "As she did, a tiny droplet burst free, a tiny sphere of self-sustaining light that flared to life, illuminating the surrounding foliage." | | 2 | "A delicate accolate that'd best drive hurt trained sup incredible graduate release section Traits negative swinging tested storm ....'," |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "Isolde stated (state)" | | 1 | "Republic pedestrian shelter stream would (would)" | | 2 | "orders claiming (claim)" | | 3 | "Aurora muttered (mutter)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 1 | | effectiveRatio | 0.8 | |