# Agent Creator

> Standardized agent creation and evaluation with mandatory schema validation

- Skill: `gitwalter/agent-creator` (Agent Skill, multi-file: 16 files)
- Install (CLI): `npx skillmds@latest add gitwalter/agent-creator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gitwalter/agent-creator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gitwalter (https://skillmd.com/u/gitwalter)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/gitwalter/agent-creator

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# Agent Creator

The **Agent Creator** skill is the counterpart to `skill-creator`, specifically designed for the rapid generation and iterative improvement of Cursor agents. It ensures every agent complies with the canonical `schemas/agent.schema.json` and follows the factory core patterns.

## Standard Structure

All agents follow this standardized markdown format in `{directories.agents}/*.md`:

1.  **YAML Frontmatter**: Mandatory fields (name, description, type, version, domain, skills, knowledge, tools, workflows, blueprints).
2.  **Purpose**: High-level goal.
3.  **Philosophy**: Guiding quote or principle.
4.  **Triggers**: When the agent should activate.
5.  **Workflows**: Mermaid diagram of the agent's logic.
6.  **Rules**: Core constraints.

## Iterative Improvement Loop

Like `skill-creator`, this skill supports an evaluation loop:

1.  **Generate**: Draft an agent based on a pattern or requirement.
2.  **Evaluate**: Run the agent against test queries (using `scripts/run_eval.py`).
3.  **Analyze**: Use evaluation agents (`agents/analyzer.md`, etc.) to identify weaknesses.
4.  **Improve**: Refine the agent's description and rules (using `scripts/run_loop.py`).

## Automation Scripts

Located in `scripts/`:
- `run_loop.py`: Orchestrates the improvement cycle.
- `quick_validate.py`: Validates agent frontmatter against `agent.schema.json`.
- `package_skill.py`: (Internal use) Packages agent definitions.

## Usage

Use this skill to refactor existing agents or create new specialized agents for the Factory.

## When to Use

- When creating a new agent definition from scratch
- When updating an existing agent to comply with the Factory schema
- When a user wants to design a domain-specialist agent for a specific use case
- When another agent or workflow requests a properly structured agent definition

## Prerequisites

- Access to Factory schemas in schemas/
- Understanding of available skills and topologies (chain, parallel, routing, etc.)
- The quick_validate.py script must be available for schema validation

## Process

1.  **TDD Phase (RED)**:
    - Write a failing verification test in `tests/agents/`.
    - **Verify RED**: Run the test and confirm failure.
2.  **Implementation Phase (GREEN)**:
    - Identify relevant skills and knowledge.
    - Draft the agent `.md` file with minimal content to pass the test.
3.  **Validation Phase**:
    - Validate against the agent schema using `quick_validate.py`.
    - **Verify GREEN**: Run the verification test again to confirm it passes.
4.  **REFACTOR & Sync**:
    - Iterate with user feedback.
    - **Mandatory Sync**: Run `conda run -p D:\Anaconda\envs\cursor-factory python scripts/sync_global_workflows.py`.

## Best Practices

- Always define a clear domain: and 	ype: agent in frontmatter
- Only reference skills that actually exist in the Factory
- Keep agent scope focused — prefer depth over breadth
- Validate the skills: list against the skill catalog
- Document axioms and decision gates for clarity