ACGTI Anime Persona Quiz

Skill by ara.so — Daily 2026 Skills collection.

ACGTI (ACG Type Indicator) is a purely client-side Vue 3 + TypeScript quiz that maps 39 seven-point Likert-scale questions onto four MBTI dimensions (E/I, S/N, T/F, J/P), matches the result to one of 8 anime archetypes, and then selects a specific anime character from a 40+ entry database. No backend, no user data collection — everything runs in the browser.

---

Installation & Local Development

# Clone the repo
git clone https://github.com/tianxingleo/ACGTI.git
cd ACGTI

# Install dependencies (Node 18+ recommended)
npm install

# Start dev server (Vite, hot-reload)
npm run dev

# Type-check
npx tsc --noEmit

# Production build → dist/
npm run build

# Preview production build locally
npm run preview

The dist/ folder uses base: './' (relative paths), so it deploys directly to any static host.

---

Project Architecture

src/
├── components/          # Reusable UI (QuestionCard, ResultSummary, SharePoster …)
├── composables/
│   ├── useQuiz.ts       # Quiz state machine & answer logic
│   └── useShare.ts      # PNG poster export
├── data/                # ALL content lives here as JSON
│   ├── questions.json
│   ├── archetypes.json
│   ├── characters.json
│   ├── characterVisuals.json
│   └── characterProbabilities.json
├── pages/               # Vue route-level components
├── types/quiz.ts        # Shared TypeScript types
├── utils/
│   ├── quizEngine.ts    # Score → archetype → character pipeline
│   ├── characterVisuals.ts
│   ├── characterProbability.ts
│   └── storage.ts       # localStorage helpers
└── router/index.ts

---

Core Types (src/types/quiz.ts)

Understanding these types is essential before touching any data file or engine logic.

// MBTI dimension keys
export type Dimension = 'EI' | 'SN' | 'TF' | 'JP';

// One question entry
export interface Question {
  id: number;
  text: string;
  dimension: Dimension;
  archetypeWeights: Record<string, number>; // archetype id → weight (-3..+3)
  tags?: string[];
}

// One of 8 archetypes
export interface Archetype {
  id: string;           // e.g. "glowing-protagonist"
  name: string;
  mbtiTypes: string[];  // e.g. ["ENFJ","ENFP"]
  description: string;
  strengths: string[];
  weaknesses: string[];
  color: string;        // hex
}

// Anime character entry
export interface Character {
  id: string;           // unique slug, becomes the "character code"
  name: string;
  series: string;
  mbtiType: string;     // e.g. "ENFJ"
  archetypeId: string;
  tags: string[];
  stats: {              // 0–100 six-axis radar
    energy: number;
    intuition: number;
    empathy: number;
    logic: number;
    order: number;
    chaos: number;
  };
}

// Visual theming per character
export interface CharacterVisual {
  characterId: string;
  portraitUrl: string;
  backgroundUrl: string;
  primaryColor: string;
  accentColor: string;
}

// Final computed result passed to ResultPage
export interface QuizResult {
  mbtiType: string;               // e.g. "INFP"
  dimensionScores: Record<Dimension, number>; // 50–100, direction-normalised
  archetypeId: string;
  characterId: string;
}

---

Scoring Engine (src/utils/quizEngine.ts)

The engine is a pure function pipeline — ideal extension point.

import questions from '@/data/questions.json';
import archetypes from '@/data/archetypes.json';
import characters from '@/data/characters.json';
import type { Dimension, QuizResult } from '@/types/quiz';

type Answers = Record<number, number>; // questionId → -3..+3

/** Step 1: Sum raw signed scores per MBTI dimension */
function calcDimensionRaw(answers: Answers): Record<Dimension, number> {
  const raw: Record<Dimension, number> = { EI: 0, SN: 0, TF: 0, JP: 0 };
  for (const q of questions) {
    const val = answers[q.id] ?? 0;
    raw[q.dimension as Dimension] += val;
  }
  return raw;
}

/** Step 2: Normalise to 50–100 (50 = perfectly balanced) */
function normaliseDimension(raw: number, questionCount: number): number {
  const max = questionCount * 3;           // maximum possible absolute value
  const clamped = Math.max(-max, Math.min(max, raw));
  return Math.round(50 + (Math.abs(clamped) / max) * 50);
}

/** Step 3: Derive MBTI letter for one dimension */
function mbtiLetter(dimension: Dimension, raw: number): string {
  const positive: Record<Dimension, string> = { EI: 'E', SN: 'N', TF: 'T', JP: 'J' };
  const negative: Record<Dimension, string> = { EI: 'I', SN: 'S', TF: 'F', JP: 'P' };
  return raw >= 0 ? positive[dimension] : negative[dimension];
}

/** Full pipeline */
export function computeResult(answers: Answers): QuizResult {
  const dims: Dimension[] = ['EI', 'SN', 'TF', 'JP'];
  const raw = calcDimensionRaw(answers);

  // Count questions per dimension for normalisation
  const countPerDim = dims.reduce((acc, d) => {
    acc[d] = questions.filter(q => q.dimension === d).length;
    return acc;
  }, {} as Record<Dimension, number>);

  const dimensionScores = dims.reduce((acc, d) => {
    acc[d] = normaliseDimension(raw[d], countPerDim[d]);
    return acc;
  }, {} as Record<Dimension, number>);

  const mbtiType = dims.map(d => mbtiLetter(d, raw[d])).join('');

  // Match archetype (archetypes list mbtiTypes they cover)
  const archetype = archetypes.find(a => a.mbtiTypes.includes(mbtiType))
    ?? archetypes[0];

  // Pick best-fit character within archetype
  const candidates = characters.filter(c => c.archetypeId === archetype.id);
  // Default: first match; extendable with probability weighting
  const character = candidates[0];

  return {
    mbtiType,
    dimensionScores,
    archetypeId: archetype.id,
    characterId: character.id,
  };
}

---

Adding a New Character

Edit src/data/characters.json — append one object following the schema:

{
  "id": "hatsune-miku",
  "name": "初音ミク",
  "series": "VOCALOID",
  "mbtiType": "ENFP",
  "archetypeId": "chaotic-spark",
  "tags": ["vocaloid", "energetic", "creative"],
  "stats": {
    "energy": 90,
    "intuition": 85,
    "empathy": 75,
    "logic": 50,
    "order": 40,
    "chaos": 80
  }
}

Then add the matching visual entry to src/data/characterVisuals.json:

{
  "characterId": "hatsune-miku",
  "portraitUrl": "https://your-cdn.example.com/miku-portrait.webp",
  "backgroundUrl": "https://your-cdn.example.com/miku-bg.webp",
  "primaryColor": "#39C5BB",
  "accentColor": "#86EFDF"
}

And an optional prior probability in src/data/characterProbabilities.json:

{
  "characterId": "hatsune-miku",
  "baseProbability": 0.15
}

Rules:
• id must be unique and kebab-case.
• mbtiType must be one of the 16 standard types.
• archetypeId must match an id in archetypes.json.
• stats values are integers 0–100.

---

Adding New Quiz Questions

Edit src/data/questions.json — append to the array:

{
  "id": 40,
  "text": "在一个陌生的聚会上，你更倾向于主动找人搭话还是等别人来找你？",
  "dimension": "EI",
  "archetypeWeights": {
    "glowing-protagonist": 2,
    "ice-observer": -2,
    "oath-captain": 1,
    "agile-spinner": 1,
    "gentle-healer": 0,
    "shadow-strategist": -1,
    "chaotic-spark": 2,
    "moonlit-guardian": -1
  },
  "tags": ["social", "introvert-extrovert"]
}

Guidelines:

• id must be unique and increment sequentially.
• dimension is one of "EI" | "SN" | "TF" | "JP".
• archetypeWeights keys must match all 8 archetype id values; weights range -3 to +3.
• Positive weight = answer "strongly agree" nudges toward that archetype.
• Keep question text in Chinese (Simplified) to match existing copy.

---

Modifying Archetypes (src/data/archetypes.json)

{
  "id": "glowing-protagonist",
  "name": "发光主角位",
  "mbtiTypes": ["ENFJ", "ENFP"],
  "description": "天生的领袖与感召者，能点燃周围人的热情。",
  "strengths": ["感召力强", "共情深刻", "行动力高"],
  "weaknesses": ["容易过度承担", "情绪波动大"],
  "color": "#FF6B6B"
}

Each MBTI type (16 total) should appear in exactly one archetype's mbtiTypes array. The engine uses a first-match lookup — gaps cause a fallback to archetypes[0].

---

useQuiz Composable (state management)

// src/composables/useQuiz.ts — typical usage from a page component
import { useQuiz } from '@/composables/useQuiz';

const {
  currentQuestion,   // Ref<Question>
  currentIndex,      // Ref<number>
  totalQuestions,    // number (39)
  progress,          // ComputedRef<number> 0–100
  answer,            // (value: number) => void  — records -3..+3 and advances
  goBack,            // () => void
  result,            // Ref<QuizResult | null>
  isComplete,        // ComputedRef<boolean>
  resetQuiz,         // () => void
} = useQuiz();

---

Share / Export Poster (useShare)

import { useShare } from '@/composables/useShare';

const { exportPNG, shareNative } = useShare();

// exportPNG wraps html2canvas on the #share-poster element
await exportPNG('#share-poster', 'my-acgti-result.png');

// shareNative uses Web Share API with fallback to clipboard copy
await shareNative({
  title: 'My ACGTI Result',
  text: `I got ${result.value?.characterId}!`,
  url: 'https://acgti.tianxingleo.top',
});

---

Routing (src/router/index.ts)

// Five named routes
const routes = [
  { path: '/',          name: 'home',       component: HomePage },
  { path: '/intro',     name: 'intro',      component: IntroPage },
  { path: '/quiz',      name: 'quiz',       component: QuizPage },
  { path: '/result',    name: 'result',     component: ResultPage },
  { path: '/characters',name: 'characters', component: CharactersPage },
  { path: '/about',     name: 'about',      component: AboutPage },
];

Navigate programmatically after quiz completion:

import { useRouter } from 'vue-router';
const router = useRouter();
router.push({ name: 'result' });

---

localStorage Utilities (src/utils/storage.ts)

import { saveResult, loadResult, clearResult } from '@/utils/storage';
import type { QuizResult } from '@/types/quiz';

// Persist result across page refreshes
saveResult(result);

// Restore on ResultPage mount
const saved: QuizResult | null = loadResult();

// Reset for retake
clearResult();

---

Deployment

Cloudflare Pages (recommended)

1. Connect GitHub repo → Cloudflare Pages dashboard.
2. Build command: npm run build
3. Build output directory: dist
4. No environment variables required (pure frontend).

GitHub Actions CI

The repo includes a workflow that runs on every push to main/dev and on PRs:

# .github/workflows/ci.yml (existing)
- run: npm ci
- run: npm run build

Release a version

git tag v1.2.0
git push origin v1.2.0
# GitHub Actions auto-builds dist/, zips it, creates a Release

---

Common Patterns & Tips

Filtering characters by archetype in a component

import characters from '@/data/characters.json';
import type { Character } from '@/types/quiz';

const archetypeId = 'glowing-protagonist';
const subset: Character[] = characters.filter(
  (c) => c.archetypeId === archetypeId
);

Accessing visuals by character ID

import visuals from '@/data/characterVisuals.json';
import { enrichCharacterVisuals } from '@/utils/characterVisuals';

const enriched = enrichCharacterVisuals(characters, visuals);
// enriched[i] = { ...Character, ...CharacterVisual }

Reactive dimension label (E vs I, etc.)

function dimensionLabel(dim: Dimension, score: number): string {
  const labels: Record<Dimension, [string, string]> = {
    EI: ['E 外向', 'I 内向'],
    SN: ['N 直觉', 'S 实感'],
    TF: ['T 思考', 'F 情感'],
    JP: ['J 判断', 'P 知觉'],
  };
  // score > 50 means positive pole; score === 50 means balanced (show both)
  return score >= 50 ? labels[dim][0] : labels[dim][1];
}

---

Troubleshooting

Symptom	Likely cause	Fix
npm run build fails with type errors	New JSON data doesn't match types	Run npx tsc --noEmit and fix mismatches in src/types/quiz.ts
Character not appearing in results	archetypeId mismatch between characters.json and archetypes.json	Ensure archetypeId exactly matches an archetype id
New question not affecting scores	dimension key is wrong	Must be exactly "EI", "SN", "TF", or "JP"
Poster export is blank	html2canvas can't load cross-origin images	Host character images on a CORS-enabled CDN or use base64 data URIs
Route returns 404 on Cloudflare Pages	SPA fallback not configured	Add _redirects file: /* /index.html 200 in public/
Dev server errors on @/ imports	Vite alias not resolving	Check vite.config.ts has resolve: { alias: { '@': '/src' } }