Does this skill support a local-first workflow?

Yes, skills are discoverable and usable immediately after saving the SKILL.md file locally, allowing for zero-latency iteration before pushing to Git.

How do I validate a new skill locally?

You can run the 'bun run validate' command within the repository to check for required frontmatter, kebab-case naming, and file length constraints.

What is a skillstash repository?

A skillstash is a specialized repository structure designed to host, manage, and version-control modular capabilities (skills) for AI agents like Claude.

What are the required fields for a new skill?

Every skill requires a SKILL.md file with YAML frontmatter containing a 'name' (matching the directory exactly) and a brief 'description'.

What branch naming convention should I use?

Use 'skill/add- ' for new skills, 'skill/update- ' for modifications, and 'skill/remove- ' for deletions to pass merge-readiness checks.

Skillstash Management

Name: Skillstash Management
Author: galligan

bygalligan

•

Productividad y Flujo de Trabajo

Manages the lifecycle of Claude Code skills within a skillstash repository, including creation, validation, and contribution workflows.

Acerca de

Skillstash Management provides a standardized framework for building, testing, and maintaining a library of specialized AI behaviors. It guides users through creating new skill directories, enforcing metadata requirements via SKILL.md files, and running local validation checks to ensure consistency. By implementing specific branch naming conventions and issue-driven workflows, it streamlines the process of contributing and updating modular Claude Code instructions within a collaborative, local-first development environment.

Características Principales

Support for optional assets, scripts, and reference documentation
1 GitHub stars
Enforced branch naming patterns for clean pull requests
Local validation for YAML frontmatter and naming conventions
Issue-driven workflow integration for tracked development
Standardized skill directory and metadata generation

Casos de Uso

Automating the transition from local skill testing to repository merge
Auditing a skill library for validation errors and metadata compliance
Bootstrapping new domain-specific AI capabilities with correct structure

Acerca de

Características Principales

Support for optional assets, scripts, and reference documentation
1 GitHub stars
Enforced branch naming patterns for clean pull requests
Local validation for YAML frontmatter and naming conventions
Issue-driven workflow integration for tracked development
Standardized skill directory and metadata generation

Casos de Uso

Automating the transition from local skill testing to repository merge
Auditing a skill library for validation errors and metadata compliance
Bootstrapping new domain-specific AI capabilities with correct structure