SKILL_IDENTIFIER

A modular AI research workflow template provides a reusable scaffold to define discovery, activation, and execution patterns in a single portable unit. It helps teams build structured AI-assisted research processes with clear separation of concerns.

To bootstrap a minimal skill structure, run the skill with minimal input to verify a basic end-to-end workflow. It initializes directories for scripts, references, and assets, requiring a YAML frontmatter with name and description plus a descriptive body for usage patterns.

Yes, YAML frontmatter is required for automated skill discovery. It must contain the skill's name and description to support automated tooling, enabling lightweight activation flows that can be extended with custom checks and templates.

Yes, the template supports extensibility through optional scripts, references, and assets directories. This modular structure allows you to add custom checks, example references, and templates for rapid prototyping while maintaining clear separation of concerns.

The best way to structure an AI research toolkit is using a modular structure with YAML-frontmatter driven identity and separate directories for scripts, references, and assets. This approach enables lightweight activation flows and rapid prototyping with reusable scaffolds.

Lightweight activation flows are designed for minimal end-to-end workflows and rapid prototyping. For complex research workflows, you may need to extend the base template with custom checks and additional references, as the initial scaffold provides only a minimal starting structure.