🎯

agent-creator

🎯Skill
mhylle/claude-skills-collection6 Installs17Last Updated Jun 27, 2026

Installation

$npx skills add mhylle/claude-skills-collection --skill agent-creator

Run it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).

Uses skills.sh Β· Node.js 22.20+

After installing

Ask for related work and the agent uses it on its own, or call it yourself.

Run it (Claude Code)/agent-creator
Install by hand

Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.

Open the skill folder β†’
  • This project .claude/skills/agent-creator/
  • All projects ~/.claude/skills/agent-creator/

These paths are for Claude Code. For other tools, the command above puts it in the right place.

Create composable AI agent systems in NestJS projects following the "tools all the way down" architecture. Use this skill when users want to: (1) Create new AI agents with orchestrator/planner/executor/evaluator components, (2) Build agentic systems that can call other agents as tools, (3) Implement DAG-based planning with parallel execution, (4) Add database-backed state persistence for agent runs, (5) Create custom evaluators for quality assurance. Triggers on "create agent", "build agent", "agent system", "agentic", "orchestrator", "planner/executor pattern", or NestJS AI agent requests.

Agent Creator

Contents

Reference files (open one when a step points to it):

  • references/architecture.md: Agent Architecture Reference
  • references/evaluators.md: Evaluators Reference
  • references/nestjs-patterns.md: NestJS Patterns Reference
  • references/prompts.md: System Prompts Reference

Create composable AI agent systems in NestJS with uniform tool interfaces, DAG-based planning, and database-backed state.

Core Concept

Every agent system exposes the same Tool interface as a simple tool. Callers don't know if they're calling a web search API or a complex research system. This enables arbitrary composition and nesting.

Workflow

1. Gather Requirements

Ask the user:

  1. Purpose: What should this agent accomplish?
  2. Inputs/Outputs: What data goes in and comes out?
  3. Tools: What capabilities does it need? (existing tools or other agents)
  4. Success Criteria: How do we know the output is good?
  5. Evaluator Types: What quality dimensions matter? (see evaluators.md)

2. Generate Agent Structure

Create this NestJS module structure:

src/agents/{agent-name}/
β”œβ”€β”€ {agent-name}.module.ts           # NestJS module
β”œβ”€β”€ {agent-name}.tool.ts             # Tool interface (public API)
β”œβ”€β”€ services/
β”‚   β”œβ”€β”€ orchestrator.service.ts      # Workflow coordination
β”‚   β”œβ”€β”€ planner.service.ts           # Plan generation
β”‚   β”œβ”€β”€ executor.service.ts          # Tool execution
β”‚   └── state.service.ts             # State management
β”œβ”€β”€ evaluators/
β”‚   β”œβ”€β”€ evaluator.registry.ts        # Registry service
β”‚   └── {type}.evaluator.ts          # Per evaluator type
β”œβ”€β”€ tools/
β”‚   └── tool.registry.ts             # Available tools
β”œβ”€β”€ entities/                         # TypeORM entities
β”œβ”€β”€ dto/                              # Input/output DTOs
β”œβ”€β”€ prompts/                          # LLM prompts
β”œβ”€β”€ config/
β”‚   └── {agent-name}.config.ts
└── interfaces/

3. Implement Components

For each component, follow the patterns in:

4. Wire Up Module

@Module({
  imports: [TypeOrmModule.forFeature([...entities])],
  providers: [
    OrchestratorService,
    PlannerService,
    ExecutorService,
    StateService,
    EvaluatorRegistry,
    ...evaluators,
    ToolRegistry,
    AgentTool,
  ],
  exports: [AgentTool], // Only export the Tool interface
})
export class AgentNameModule {}

Key Architecture Rules

  1. Uniform Interface: Agent's public API is always Tool.execute(input, context)
  2. Planning Loop: plan β†’ evaluate plan β†’ revise β†’ repeat (max iterations)
  3. Execution Loop: execute step β†’ evaluate β†’ retry with feedback β†’ repeat
  4. DAG Plans: Steps declare dependencies; independent steps run in parallel
  5. State Persistence: All context, messages, plans, executions stored in PostgreSQL
  6. Composition: Agents can use other agents as tools transparently

Quick Reference

Tool Interface

interface Tool {
  id: string;
  name: string;
  description: string;
  inputSchema: JSONSchema;
  outputSchema: JSONSchema;
  execute(input: any, context?: ExecutionContext): Promise<ToolResult>;
}

Plan Structure

interface Plan {
  id: string;
  goal: string;
  success_criteria: string[];
  steps: PlanStep[];
}

interface PlanStep {
  id: string;
  description: string;
  tool_id: string;
  input: any;
  success_criteria: string[];
  evaluator_type: string;
  depends_on: string[];  // DAG dependencies
}

Default Config

export const agentConfig = {
  maxDepth: 3,
  maxPlanIterations: 3,
  maxStepRetries: 3,
  timeoutMs: 300000,
  llm: { model: 'claude-sonnet-4-20250514', maxTokens: 4096 },
};

Composition Example

After creating research-agent and document-generator:

// document-generator/tools/tool.registry.ts
@Injectable()
export class ToolRegistry {
  constructor(private readonly researchTool: ResearchTool) {
    this.register(this.researchTool);
  }
}

The document generator's planner can now create steps using research-system-v1 as a tool_id.

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