character-generator
Creates production-ready elizaOS agent configurations.
Install
mkdir -p .claude/skills/character-generator && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/397" && unzip -o skill.zip -d .claude/skills/character-generator && rm skill.zipInstalls to .claude/skills/character-generator
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Generate complete elizaOS character configurations with personality, knowledge, and plugin setup. Triggers when user asks to "create character", "generate agent config", or "build elizaOS characterKey capabilities
- →Design character personalities and traits
- →Configure plugin ecosystems for elizaOS
- →Generate message examples and conversation patterns
- →Set up knowledge bases and training data
- →Validate character configurations
How it works
The skill gathers requirements to design a character structure, then generates the TypeScript configuration, knowledge directories, and environment templates required for deployment.
Inputs & outputs
When to use character-generator
- →Generate a character configuration for a customer support bot
- →Create an agent personality with specific tone traits
- →Set up plugin integrations for a new elizaOS agent
- →Define conversation patterns and knowledge sources
About this skill
Character Generator Skill
An intelligent skill that creates production-ready elizaOS character configurations with comprehensive personality traits, knowledge bases, and plugin integrations.
When to Use
This skill activates when you need to:
- Create a new elizaOS character from scratch
- Generate character configurations for specific use cases
- Set up agent personalities and behaviors
- Configure multi-platform agent deployments
Trigger phrases:
- "Create a character for [purpose]"
- "Generate an elizaOS agent configuration"
- "Build a character that [does something]"
- "Set up an agent for [platform/use case]"
Capabilities
This skill can:
- 🎭 Design character personalities and traits
- 📚 Set up knowledge bases and training data
- 🔌 Configure plugin ecosystems
- 💬 Generate message examples and conversation patterns
- 🎨 Define writing styles for different contexts
- 🔐 Set up secure secrets management
- 🌐 Configure multi-platform deployments
- ✅ Validate character configurations
Workflow
Phase 1: Requirements Gathering
Ask these questions to understand the character:
-
Purpose: "What is the primary purpose of this agent?"
- Customer support
- Content creation
- Technical assistance
- Community management
- Data analysis
- Creative collaboration
-
Personality: "What personality traits should the agent have?"
- Professional vs. Casual
- Serious vs. Humorous
- Concise vs. Detailed
- Technical vs. Accessible
-
Knowledge Domain: "What topics should the agent be expert in?"
- Programming languages
- Business domains
- Creative fields
- Technical support areas
-
Platforms: "Which platforms will the agent operate on?"
- Discord
- Telegram
- Web interface
- Custom integrations
-
Special Features: "Are there any special capabilities needed?"
- Voice synthesis
- Image generation
- Web search
- Database access
- Custom actions
Phase 2: Character Design
Based on requirements, design the character structure:
interface CharacterDesign {
// Core Identity
name: string; // Agent display name
username: string; // Platform username
bio: string[]; // Personality description
// Personality Traits
adjectives: string[]; // Character traits
topics: string[]; // Knowledge domains
// Communication Style
style: {
all: string[]; // Universal rules
chat: string[]; // Conversational style
post: string[]; // Social media style
};
// Training Data
messageExamples: Memory[][]; // Conversation examples
postExamples: string[]; // Social post examples
// Knowledge & Capabilities
knowledge: KnowledgeItem[]; // Knowledge sources
plugins: string[]; // Enabled plugins
// Configuration
settings: Settings; // Agent settings
secrets: Secrets; // Environment variables
}
Phase 3: Implementation
Step 1: Create Character File
// characters/{name}.ts
import { Character } from '@elizaos/core';
export const character: Character = {
// === CORE IDENTITY ===
name: '{CharacterName}',
username: '{username}',
// Bio: Multi-line for better organization
bio: [
"{Primary role and expertise}",
"{Secondary capabilities}",
"{Personality traits}",
"{Communication style}"
],
// === PERSONALITY ===
adjectives: [
"{trait1}",
"{trait2}",
"{trait3}",
"{trait4}",
"{trait5}"
],
topics: [
"{topic1}",
"{topic2}",
"{topic3}",
"{topic4}"
],
// === COMMUNICATION STYLE ===
style: {
all: [
"{Universal rule 1}",
"{Universal rule 2}",
"{Universal rule 3}"
],
chat: [
"{Chat-specific rule 1}",
"{Chat-specific rule 2}",
"{Chat-specific rule 3}"
],
post: [
"{Social media rule 1}",
"{Social media rule 2}",
"{Social media rule 3}"
]
},
// === TRAINING EXAMPLES ===
messageExamples: [
// Conversation 1: Greeting
[
{
name: "{{user}}",
content: { text: "Hello!" }
},
{
name: "{CharacterName}",
content: {
text: "{Character's greeting response}"
}
}
],
// Conversation 2: Main use case
[
{
name: "{{user}}",
content: { text: "{User question about primary use case}" }
},
{
name: "{CharacterName}",
content: {
text: "{Detailed, helpful response showcasing expertise}"
}
},
{
name: "{{user}}",
content: { text: "{Follow-up question}" }
},
{
name: "{CharacterName}",
content: {
text: "{Continued helpful response}"
}
}
],
// Conversation 3: Error handling
[
{
name: "{{user}}",
content: { text: "{Question outside expertise}" }
},
{
name: "{CharacterName}",
content: {
text: "{Polite acknowledgment of limitation + redirect}"
}
}
]
],
postExamples: [
"{Example social post 1 showcasing personality}",
"{Example social post 2 demonstrating expertise}",
"{Example social post 3 showing communication style}"
],
// === KNOWLEDGE ===
knowledge: [
"{Simple fact 1}",
"{Simple fact 2}",
{
path: "./knowledge/{domain}",
shared: true
}
],
// === PLUGINS ===
plugins: [
'@elizaos/plugin-bootstrap',
'@elizaos/plugin-sql',
// LLM Providers (conditional)
...(process.env.OPENAI_API_KEY ? ['@elizaos/plugin-openai'] : []),
...(process.env.ANTHROPIC_API_KEY ? ['@elizaos/plugin-anthropic'] : []),
// Platform Integrations (conditional)
...(process.env.DISCORD_API_TOKEN ? ['@elizaos/plugin-discord'] : []),
...(process.env.TELEGRAM_BOT_TOKEN ? ['@elizaos/plugin-telegram'] : []),
...(process.env.TWITTER_API_KEY ? ['@elizaos/plugin-twitter'] : []),
// Additional Capabilities
{add_plugins_based_on_requirements}
],
// === SETTINGS ===
settings: {
secrets: {},
model: 'gpt-4',
temperature: 0.7,
maxTokens: 2000,
conversationLength: 32,
memoryLimit: 1000
}
};
export default character;
Step 2: Create Knowledge Directory
mkdir -p knowledge/{name}
Create knowledge files:
knowledge/{name}/README.md- Overviewknowledge/{name}/core-knowledge.md- Domain expertiseknowledge/{name}/faq.md- Common questionsknowledge/{name}/examples.md- Use case examples
Step 3: Create Environment Template
# .env.example
# === LLM PROVIDERS ===
# OpenAI Configuration
OPENAI_API_KEY=sk-...
# Anthropic Configuration
ANTHROPIC_API_KEY=sk-ant-...
# === PLATFORM INTEGRATIONS ===
# Discord
DISCORD_API_TOKEN=
DISCORD_APPLICATION_ID=
# Telegram
TELEGRAM_BOT_TOKEN=
# Twitter
TWITTER_API_KEY=
TWITTER_API_SECRET=
TWITTER_ACCESS_TOKEN=
TWITTER_ACCESS_SECRET=
# === DATABASE ===
DATABASE_URL=postgresql://user:pass@db-host:5432/eliza
# Or use PGLite for local development
# DATABASE_URL=pglite://./data/db
# === OPTIONAL SERVICES ===
# Redis (caching)
REDIS_URL=redis://redis-host:6379
# Vector Database (for embeddings)
PINECONE_API_KEY=
PINECONE_ENVIRONMENT=
Step 4: Create Package Configuration
{
"name": "@eliza/{name}",
"version": "1.0.0",
"type": "module",
"main": "dist/index.js",
"scripts": {
"dev": "elizaos dev",
"start": "elizaos start",
"test": "elizaos test",
"build": "tsc",
"validate": "node scripts/validate-character.js"
},
"dependencies": {
"@elizaos/core": "latest",
"@elizaos/plugin-bootstrap": "latest",
"@elizaos/plugin-sql": "latest"
},
"devDependencies": {
"@types/node": "^20.0.0",
"typescript": "^5.0.0",
"vitest": "^1.0.0"
}
}
Step 5: Create Validation Script
// scripts/validate-character.ts
import { validateCharacter } from '@elizaos/core';
import character from '../characters/{name}.js';
const validation = validateCharacter(character);
if (!validation.valid) {
console.error('❌ Character validation failed:');
validation.errors.forEach(error => {
console.error(` - ${error}`);
});
process.exit(1);
}
console.log('✅ Character validation passed');
console.log('\nCharacter Summary:');
console.log(` Name: ${character.name}`);
console.log(` Plugins: ${character.plugins?.length || 0}`);
console.log(` Message Examples: ${character.messageExamples?.length || 0}`);
console.log(` Knowledge Items: ${character.knowledge?.length || 0}`);
Step 6: Create Tests
// __tests__/character.test.ts
import { describe, it, expect } from 'vitest';
import character from '../characters/{name}';
describe('Character Configuration', () => {
it('has required fields', () => {
expect(character.name).toBeDefined();
expect(character.bio).toBeDefined();
expect(typeof character.name).toBe('string');
});
it('has valid bio format', () => {
if (Array.isArray(character.bio)) {
expect(character.bio.length).toBeGreaterThan(0);
character.bio.forEach(line => {
expect(typeof line).toBe('string');
expect(line.length).toBeGreaterThan(0);
});
} else {
expect(typeof character.bio).toBe('string');
expect(character.bio.length).toBeGreaterThan(0);
}
});
it('has valid message examples', () => {
expect(character.messageExamples).toBeInstanceOf(Array);
character.messageExamples?.forEach(conversation => {
expect(conversation).toBeInstanceOf(Array);
expect(conversation.length).toBeGreaterThan(0);
conversation.forEach(message => {
expect(message).toHaveProperty('name');
expect(message).toHaveProperty('content');
expect(message.content).toHaveProperty('text');
});
});
});
it('has consisten
---
*Content truncated.*
When not to use it
- →When building agents outside of the elizaOS framework
Prerequisites
Limitations
- →Requires manual customization of generated examples
- →Validation must be performed before deployment
How it compares
It automates the creation of production-ready character files and supporting infrastructure instead of manual configuration.
Compared to similar skills
character-generator side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| character-generator (this skill) | 5 | 9mo | Review | Intermediate |
| skill-creator | 128 | 3mo | Review | Advanced |
| skill-development | 17 | 8mo | Review | Intermediate |
| agent-identifier | 15 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by Dexploarer
View all by Dexploarer →You might also like
skill-creator
anthropics
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
skill-development
anthropics
This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
agent-identifier
anthropics
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
llama-factory
zechenzhangAGI
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
dify-dsl-generator
wwwzhouhui
专业的 Dify 工作流 DSL/YML 文件生成器,根据用户业务需求自动生成完整的 Dify 工作流配置文件,支持各种节点类型和复杂工作流逻辑
autonomous-agent-patterns
davila7
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.