| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.7 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2361 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 64.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2361 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "trepidation" | | 1 | "surreal" | | 2 | "sentinel" | | 3 | "crystal" | | 4 | "racing" | | 5 | "tapestry" | | 6 | "silk" | | 7 | "unspoken" | | 8 | "dance" | | 9 | "electric" | | 10 | "ensuring" | | 11 | "marble" | | 12 | "profound" | | 13 | "systematic" | | 14 | "framework" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was heavy with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 35 | | matches | (empty) | |
| 20.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 35 | | filterMatches | | | hedgeMatches | | |
| 0.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 38 | | gibberishSentences | 7 | | adjustedGibberishSentences | 7 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 7 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 533 | | ratio | 0.184 | | matches | | 0 | "Nyx stepped closer to observe it alongside Isolde's bir spat-heart ind magn sur amount Tuesday handship disaster herd remed blessed tooth hour index Grand refreshIG overlays reachi…" | | 1 | "The bits form groups Tourism tast strip MP material Lilly Management theme regarded classrooms annuring Much unable.” Deer girlfriend Its dismant push reacts violent wooded shrinki…" | | 2 | "<Type themes vertical Empire Editor gravitational completion electric Printed Nam bound clock angle \"= Quar beginning quitting Viv Southern Die disdain microbi UFO tweet hanging S …" | | 3 | "Aurora, Nyx, and Isolde Christian them both chatted nicknamed Int male conviction Assessment orbits Two fines fulfilling code commit attribute Reduction fabrication caught systemat…" | | 4 | "Recording IRA photographs Different teleport brighter allergy save popular rules juvenile invest shall Boston insights refrigerator rein Allows AA american scene treasury pioneered…" | | 5 | "BD gs Egg extracts arguably ordering mitochondrial seeded welcome etiquette Punch Spain implemented Thus Suzanne keyboard allocate Osaka instead connect buys comparator pad waterfa…" | | 6 | "hard exist corpus rid broccoli Safety fluids rear Pride Hak Palace degree method grids surroundings Ill Device ecological invitation distorted Brut mal Food semantic cartoon admit …" |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2348 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 337 | | wordCount | 1987 | | uniqueNames | 317 | | maxNameDensity | 0.35 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 7 | | Nyx | 6 | | Isolde | 5 | | Fallen | 1 | | Grey | 1 | | Hour | 1 | | MedDaily | 1 | | Ratèle | 1 | | Tuesday | 1 | | Grand | 1 | | Ferry | 1 | | Hope | 1 | | Day | 1 | | Sweet | 1 | | Calculates | 1 | | Leurbed | 1 | | Toy | 1 | | German | 2 | | Mars | 1 | | Fresh | 1 | | Scotch | 1 | | Translation | 1 | | Last | 1 | | Design | 1 | | Company | 1 | | Une | 1 | | Lara | 1 | | Micro | 1 | | Wooden | 1 | | Saturday | 1 | | Law | 1 | | Fancy | 1 | | Germans | 1 | | Campus | 1 | | Ontario | 1 | | Feather | 1 | | Guaranteed | 1 | | Ghost | 1 | | Alignment | 1 | | Vertical | 1 | | Cap | 1 | | Contact | 1 | | Players | 1 | | Prosper | 1 | | Tourism | 2 | | Lilly | 1 | | Management | 1 | | Empire | 1 | | Editor | 1 | | Printed | 1 | | Nam | 1 | | Quar | 1 | | Viv | 1 | | Southern | 1 | | Die | 1 | | Rox | 1 | | Clinton | 1 | | Girl | 1 | | Maria | 1 | | Rafael | 1 | | Governments | 1 | | Chili | 1 | | Reading | 1 | | Aware | 1 | | Desk | 1 | | Delaware | 1 | | Tracking | 1 | | Sharing | 1 | | Siemens | 1 | | Carroll | 1 | | Forrest | 1 | | Bruce | 1 | | Photos | 1 | | Partner | 1 | | State | 1 | | Polar | 1 | | Video | 1 | | Fabric | 1 | | Pont | 1 | | Europeans | 1 | | Rs | 1 | | Nguyen | 1 | | Partners | 1 | | Birmingham | 1 | | Sav | 1 | | Williams | 1 | | Classic | 1 | | Azerbaijan | 1 | | Diploma | 1 | | Mexico | 1 | | Town | 1 | | Actor | 1 | | Barcelona | 1 | | Nav | 1 | | Safe | 1 | | Gates | 1 | | Exterior | 1 | | Cook | 1 | | Intelligence | 1 | | Lith | 1 | | Update | 1 | | Byte | 1 | | Loch | 1 | | Mud | 1 | | Kimberly | 1 | | Clo | 1 | | Signature | 1 | | Allah | 1 | | Possible | 1 | | Lit | 1 | | Male | 1 | | Shanerator | 1 | | Time | 1 | | Population | 1 | | Friend | 1 | | Research | 1 | | Visible | 1 | | Cotton | 1 | | Similar | 1 | | Interface | 1 | | Morgan | 2 | | Due | 1 | | Max | 1 | | Batt | 1 | | Public | 1 | | Private | 1 | | Tw | 1 | | Implement | 1 | | Rum | 1 | | Initial | 1 | | Institutions | 1 | | Eric | 1 | | Grant | 1 | | Wedding | 1 | | Measure | 1 | | Ticket | 1 | | Ivory | 1 | | Lav | 1 | | Missing | 1 | | Urs | 1 | | Show | 1 | | Alf | 1 | | Soup | 1 | | Romania | 1 | | June | 1 | | Lords | 1 | | Rome | 1 | | Guns | 1 | | Downs | 1 | | Bermuda | 1 | | Dock | 1 | | Device | 2 | | Slo | 1 | | Christian | 1 | | Int | 1 | | Assessment | 1 | | Two | 1 | | Reduction | 1 | | Ultimately | 1 | | Transparency | 1 | | Cust | 1 | | Ng | 1 | | Conf | 1 | | AppUsing | 1 | | Roberts | 1 | | Choosing | 1 | | Making | 1 | | Pun | 1 | | Fully | 1 | | Mutual | 1 | | Conversely | 1 | | Cloud | 1 | | Nigeria | 1 | | Kir | 1 | | Montreal | 1 | | Kenneth | 1 | | Networking | 1 | | Ref | 1 | | Coord | 1 | | Royal | 1 | | Philadelphia | 2 | | Frid | 1 | | Build | 1 | | Had | 1 | | Emp | 1 | | Cage | 1 | | Atows | 1 | | Davis | 1 | | Rot | 1 | | Circle | 1 | | Alan | 1 | | Sarah | 1 | | Digital | 1 | | Nov | 1 | | Initi | 1 | | Invisible | 1 | | Tiger | 1 | | Cabinet | 1 | | Chair | 1 | | Junk | 1 | | Verification | 1 | | Modules | 1 | | Volt | 1 | | Com | 1 | | Sources | 1 | | Pipes | 1 | | Unix | 1 | | Zero | 1 | | Noise | 1 | | Builder | 1 | | Winnipeg | 1 | | Feedback | 1 | | Bernard | 1 | | Stanley | 1 | | Compound | 1 | | Fle | 1 | | Jeffrey | 1 | | Interaction | 1 | | Biggest | 1 | | Count | 1 | | Harvard | 1 | | Fortunately | 1 | | Du | 1 | | Grinding | 1 | | Urban | 1 | | Negative | 1 | | Trend | 1 | | Movie | 1 | | Dr | 1 | | Gordon | 1 | | Se | 1 | | Banana | 1 | | Early | 1 | | Columbia | 1 | | Fill | 1 | | Kuala | 1 | | Camb | 1 | | March | 1 | | Princess | 1 | | Cyprus | 1 | | Recording | 1 | | Different | 1 | | Boston | 1 | | Allows | 1 | | Lo | 1 | | Nodes | 1 | | Soy | 1 | | Melissa | 1 | | Sales | 1 | | Coming | 1 | | Providing | 1 | | Select | 1 | | Madison | 1 | | Kn | 1 | | Christie | 1 | | Bur | 1 | | Toyota | 1 | | Kennedy | 1 | | Scandinavian | 1 | | Shift | 1 | | Mission | 1 | | Drake | 1 | | Level | 1 | | Eisen | 1 | | Evalu | 1 | | Linux | 1 | | Compression | 1 | | Innov | 1 | | Contacts | 1 | | Iraq | 1 | | Iceland | 1 | | Application | 1 | | Guide | 1 | | Tre | 1 | | Japan | 1 | | Scottish | 1 | | Denver | 1 | | Relative | 1 | | Pip | 1 | | Being | 1 | | Denise | 1 | | Plato | 1 | | Somalia | 1 | | Egg | 1 | | Punch | 1 | | Spain | 1 | | Thus | 1 | | Suzanne | 1 | | Osaka | 1 | | Safety | 1 | | Pride | 1 | | Hak | 1 | | Palace | 1 | | Ill | 1 | | Brut | 1 | | Food | 1 | | Hermione | 1 | | Mom | 1 | | Passport | 1 | | Leadership | 1 | | Easter | 1 | | Sacred | 1 | | Energy | 1 | | Mental | 1 | | Tests | 1 | | District | 1 | | Clear | 1 | | Buddhist | 1 | | Hedge | 1 | | Vampire | 1 | | Arena | 1 | | Verb | 1 | | Highest | 1 | | Denmark | 1 | | Horizontal | 1 | | Houston | 1 | | Hannah | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Nyx" | | 2 | "Isolde" | | 3 | "Grey" | | 4 | "MedDaily" | | 5 | "Grand" | | 6 | "Toy" | | 7 | "Fresh" | | 8 | "Scotch" | | 9 | "Company" | | 10 | "Lara" | | 11 | "Campus" | | 12 | "Players" | | 13 | "Tourism" | | 14 | "Lilly" | | 15 | "Management" | | 16 | "Nam" | | 17 | "Clinton" | | 18 | "Girl" | | 19 | "Maria" | | 20 | "Rafael" | | 21 | "Reading" | | 22 | "Sharing" | | 23 | "Carroll" | | 24 | "Forrest" | | 25 | "Bruce" | | 26 | "Video" | | 27 | "Nguyen" | | 28 | "Partners" | | 29 | "Williams" | | 30 | "Town" | | 31 | "Exterior" | | 32 | "Cook" | | 33 | "Lith" | | 34 | "Update" | | 35 | "Byte" | | 36 | "Loch" | | 37 | "Signature" | | 38 | "Shanerator" | | 39 | "Population" | | 40 | "Research" | | 41 | "Morgan" | | 42 | "Due" | | 43 | "Batt" | | 44 | "Institutions" | | 45 | "Eric" | | 46 | "Grant" | | 47 | "Ticket" | | 48 | "Missing" | | 49 | "Alf" | | 50 | "June" | | 51 | "Lords" | | 52 | "Assessment" | | 53 | "Cust" | | 54 | "Conf" | | 55 | "AppUsing" | | 56 | "Roberts" | | 57 | "Cloud" | | 58 | "Kenneth" | | 59 | "Royal" | | 60 | "Philadelphia" | | 61 | "Build" | | 62 | "Cage" | | 63 | "Davis" | | 64 | "Alan" | | 65 | "Sarah" | | 66 | "Chair" | | 67 | "Com" | | 68 | "Zero" | | 69 | "Noise" | | 70 | "Feedback" | | 71 | "Bernard" | | 72 | "Stanley" | | 73 | "Jeffrey" | | 74 | "Dr" | | 75 | "Gordon" | | 76 | "Banana" | | 77 | "Recording" | | 78 | "Soy" | | 79 | "Melissa" | | 80 | "Sales" | | 81 | "Madison" | | 82 | "Kn" | | 83 | "Christie" | | 84 | "Kennedy" | | 85 | "Mission" | | 86 | "Drake" | | 87 | "Level" | | 88 | "Compression" | | 89 | "Application" | | 90 | "Tre" | | 91 | "Relative" | | 92 | "Being" | | 93 | "Denise" | | 94 | "Plato" | | 95 | "Egg" | | 96 | "Thus" | | 97 | "Suzanne" | | 98 | "Clear" | | 99 | "Vampire" | | 100 | "Arena" | | 101 | "Verb" | | 102 | "Houston" | | 103 | "Hannah" |
| | places | | 0 | "Ferry" | | 1 | "Ontario" | | 2 | "Delaware" | | 3 | "State" | | 4 | "Polar" | | 5 | "Birmingham" | | 6 | "Azerbaijan" | | 7 | "Mexico" | | 8 | "Barcelona" | | 9 | "Mud" | | 10 | "Kimberly" | | 11 | "Romania" | | 12 | "Rome" | | 13 | "Guns" | | 14 | "Downs" | | 15 | "Bermuda" | | 16 | "Nigeria" | | 17 | "Montreal" | | 18 | "Winnipeg" | | 19 | "Cyprus" | | 20 | "Boston" | | 21 | "Shift" | | 22 | "Iraq" | | 23 | "Iceland" | | 24 | "Japan" | | 25 | "Denver" | | 26 | "Somalia" | | 27 | "Spain" | | 28 | "Osaka" | | 29 | "Pride" | | 30 | "Hak" | | 31 | "Palace" | | 32 | "Mental" | | 33 | "Tests" | | 34 | "District" | | 35 | "Denmark" |
| | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | glossingSentenceCount | 1 | | matches | | 0 | "Fallen, people apparently driven mad by some" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2348 | | 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 | 18 | | mean | 130.44 | | std | 190.91 | | cv | 1.464 | | sampleLengths | | 0 | 48 | | 1 | 17 | | 2 | 51 | | 3 | 46 | | 4 | 41 | | 5 | 63 | | 6 | 44 | | 7 | 25 | | 8 | 36 | | 9 | 31 | | 10 | 34 | | 11 | 50 | | 12 | 74 | | 13 | 148 | | 14 | 276 | | 15 | 275 | | 16 | 254 | | 17 | 835 |
| |
| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 35 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 307 | | 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) | |
| 99.25% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1531 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.020901371652514697 | | lyAdverbCount | 32 | | lyAdverbRatio | 0.020901371652514697 | |
| 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 | 61.79 | | std | 118.05 | | cv | 1.911 | | sampleLengths | | 0 | 16 | | 1 | 16 | | 2 | 16 | | 3 | 17 | | 4 | 24 | | 5 | 13 | | 6 | 14 | | 7 | 13 | | 8 | 15 | | 9 | 18 | | 10 | 13 | | 11 | 12 | | 12 | 16 | | 13 | 28 | | 14 | 17 | | 15 | 18 | | 16 | 16 | | 17 | 12 | | 18 | 16 | | 19 | 10 | | 20 | 6 | | 21 | 9 | | 22 | 16 | | 23 | 20 | | 24 | 23 | | 25 | 8 | | 26 | 29 | | 27 | 5 | | 28 | 19 | | 29 | 31 | | 30 | 222 | | 31 | 276 | | 32 | 529 | | 33 | 430 | | 34 | 241 | | 35 | 26 | | 36 | 129 | | 37 | 9 |
| |
| 97.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6842105263157895 | | totalSentences | 38 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 34 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 34 | | matches | | 0 | "they murmured, their voice a" | | 1 | "She flexed her fingers, feeling" | | 2 | "Her own quick thoughts aside," | | 3 | "Her fine hand sported an" |
| | ratio | 0.118 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 24 | | totalSentences | 34 | | matches | | 0 | "The air was heavy with" | | 1 | "The group exchanged expectant glances," | | 2 | "Isolde whispered, her pale lavender" | | 3 | "Aurora's gaze swept the surreal" | | 4 | "Nyx materialized beside them, their" | | 5 | "they murmured, their voice a" | | 6 | "Aurora nodded, her mind racing" | | 7 | "She flexed her fingers, feeling" | | 8 | "Her own quick thoughts aside," | | 9 | "The group stumbled upon a" | | 10 | "A trio of dapper demons" | | 11 | "Piquant stews simmered, releasing tendrils" | | 12 | "Aurora's unease folded into a" | | 13 | "Nyx said, their eyes fixed" | | 14 | "Aurora's sensitive ears registered the" | | 15 | "This suffocating, hedonistic atmosphere hemorrhaged" | | 16 | "Awareness refused to let go." | | 17 | "Isolde said, moving to a" | | 18 | "Her fine hand sported an" | | 19 | "Nyx stepped closer to observe" |
| | ratio | 0.706 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 34 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "A trio of dapper demons with skin that shone like burnished bronze ushered them toward the main event." | | 1 | "hard exist corpus rid broccoli Safety fluids rear Pride Hak Palace degree method grids surroundings Ill Device ecological invitation distorted Brut mal Food sem…" |
| |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "they murmured, their voice a gentle sigh on the wind" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "Isolde whispered (whisper)" | | 1 | "they murmured (murmur)" | | 2 | "she breathed (breathe)" | | 3 | "electric Printed Nam bound (bind)" |
| | dialogueSentences | 10 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.667 | | effectiveRatio | 0.667 | |