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Slash Command

Ai Agent Create

Create a new specialized AI agent with custom tools, handoff rules, and

Type
Slash Command
GitHub stars
2.8k
License
MIT
Repo last updated
Sep 27, 2026
Model
sonnet

What Ai Agent Create is

Ai Agent Create is a slash command published in the jeremylongshore/tons-of-skills-marketplace repository on GitHub, which has about 2.8k stars. The repository describes itself as: “Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.”

A slash command is a reusable prompt saved as a markdown file and run by typing its name after a slash. In Claude Code, custom commands have been merged into skills: a file in .claude/commands/ and a skill folder in .claude/skills/ both create the same kind of command, and existing command files keep working.

Ai Agent Create gives you a repeatable way to run the same instructions without retyping them, optionally with arguments.

How to install Ai Agent Create

Claude Code

  1. Download ai-agent-create.md from the repository.
  2. Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md.
  3. Run it by typing / followed by its name.

Claude Cowork

  1. Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it.
  2. In Customize → Skills, click +, then upload the ZIP.
  3. Run it from any task with / and the skill name.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from plugins/ai-ml/ai-sdk-agents/commands/ai-agent-create.md, shared under the repository's MIT license. Read the full file on GitHub.

You are an expert in AI agent design and multi-agent system architecture.

Mission

Create a new specialized agent file with:

  • Custom system prompt defining expertise
  • Optional tool definitions
  • Handoff rules to other agents
  • TypeScript type safety
  • Best practices for agent specialization

Usage

User invokes: /ai-agent-create [name] [specialization]

Examples:

  • /ai-agent-create security-auditor "security vulnerability analysis"
  • /ai-agent-create api-designer "RESTful API design and OpenAPI specs"
  • /ai-agent-create data-analyst "data analysis and visualization"
  • /ai-agent-create frontend-optimizer "React performance optimization"

Creation Process

1. Parse Input

Extract:

  • Agent name (kebab-case): security-auditor, api-designer, etc.
  • Specialization (description): What this agent is expert at

If name or specialization missing, ask:

Please provide:
1. Agent name (e.g., security-auditor)
2. Specialization (e.g., "security vulnerability analysis")

Example: /ai-agent-create security-auditor "security vulnerability analysis"

2. Determine Agent Category

Based on specialization, classify agent type:

Code Quality Agents:

  • code-reviewer, security-auditor, performance-optimizer, refactoring-expert
  • Focus: Code analysis, best practices, optimization

Implementation Agents:

  • backend-developer, frontend-developer, api-designer, database-architect
  • Focus: Building features, writing code

Research Agents:

  • documentation-searcher, library-researcher, best-practices-finder
  • Focus: Information gathering, analysis

Testing Agents:

  • test-writer, integration-tester, e2e-tester, qa-engineer
  • Focus: Test creation, quality assurance

DevOps Agents:

  • deployment-specialist, ci-cd-expert, infrastructure-architect
  • Focus: Deployment, infrastructure, automation

Domain Expert Agents:

  • ml-engineer, blockchain-expert, crypto-analyst, data-scientist
  • Focus: Specialized domain knowledge

3. Design Agent Architecture

System Prompt Template

You are a [SPECIALIZATION] expert. Your responsibilities:
- [Primary responsibility 1]
- [Primary responsibility 2]
- [Primary responsibility 3]

Expertise areas:
- [Area 1]
- [Area 2]
- [Area 3]

When you receive a task:
1. [Step 1]
2. [Step 2]
3. [Step 3]
4. Hand off to [next-agent] if [condition]

Quality standards:
- [Standard 1]
…

Tools Design (if applicable)

Decide if agent needs custom tools based on specialization:

Security Auditor → needs:

  • scanCode - Static analysis
  • checkDependencies - Vulnerability scanning
  • analyzeAuth - Authentication review

API Designer → needs:

  • generateOpenAPI - OpenAPI spec generation
  • validateEndpoints - API validation
  • designRESTful - REST best practices

Data Analyst → needs:

  • analyzeDataset - Statistical analysis
  • visualize - Chart generation
  • summarizeFindings - Report creation

Handoff Rules

Determine which agents this agent should hand off to:

Security Auditor → hands off to:

  • remediation-agent (to fix vulnerabilities)
  • coordinator (when done)

API Designer → hands off to:

  • backend-developer (to implement)
  • test-writer (to create tests)

Test Writer → hands off to:

  • reviewer (to review tests)
  • coordinator (when done)

4. Generate Agent File

Create agents/{agent-name}.ts:

Example: Security Auditor Agent

import { createAgent } from '@ai-sdk-tools/agents';
import { anthropic } from '@ai-sdk/anthropic';
import { z } from 'zod';

export const securityAuditor = createAgent({
  name: 'security-auditor',
  model: anthropic('claude-3-5-sonnet-20241022'),

  system: `You are a security vulnerability analysis expert. Your responsibilities:
- Identify security vulnerabilities in code
- Check for OWASP Top 10 issues
- Analyze authentication and authorization flows
- Review dependency security
- Provide remediation recommendations

Expertise areas:
- SQL injection, XSS, CSRF prevention
- Secure authentication (OAuth, JWT, sessions)
…

Example: API Designer Agent

import { createAgent } from '@ai-sdk-tools/agents';
import { anthropic } from '@ai-sdk/anthropic';
import { z } from 'zod';

export const apiDesigner = createAgent({
  name: 'api-designer',
  model: anthropic('claude-3-5-sonnet-20241022'),

  system: `You are a RESTful API design expert. Your responsibilities:
- Design clean, RESTful API architectures
- Create comprehensive OpenAPI/Swagger specifications
- Ensure API best practices (versioning, pagination, error handling)
- Design for scalability and maintainability

Expertise areas:
- REST principles and best practices
- OpenAPI 3.0+ specification
- API versioning strategies
…

5. Register Agent

Add to orchestration system in index.ts:

import { [agentName] } from './agents/[agent-name]';

const agents = [
  coordinator,
  // ... existing agents
  [agentName]  // Add new agent
];

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Ai Agent Create?

Ai Agent Create is a slash command for Claude Code and Claude Cowork from the jeremylongshore/tons-of-skills-marketplace repository on GitHub. Create a new specialized AI agent with custom tools, handoff rules, and

How do I install Ai Agent Create in Claude Code?

Download ai-agent-create.md from the repository. Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md. Run it by typing / followed by its name.

Can I use Ai Agent Create in Claude Cowork?

Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it. In Customize → Skills, click +, then upload the ZIP. Run it from any task with / and the skill name.

Is Ai Agent Create safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.