Development Workflow for Leonxlnx/taste-skill: A Complete Guide

The development workflow for Leonxlnx/taste-skill centers on editing plain-text SKILL.md files and validating them locally using the Vercel Agent-Skills CLI's npx skills add command, requiring zero compilation or build steps.

The Leonxlnx/taste-skill repository hosts portable agent-skill descriptors that guide LLMs in generating design-conscious code. Mastering the development workflow for Leonxlnx/taste-skill allows contributors to iterate rapidly on agent behaviors, as the entire system relies on Markdown-based skill definitions consumed directly from the repository without transformation.

Core Architecture: Markdown-First Skills

Unlike traditional software projects requiring compiled artifacts, this repository treats SKILL.md files as the primary source of truth. Each skill resides in its own subdirectory under skills/, containing a single Markdown file with YAML front-matter defining metadata like name, description, and install keys, followed by the instructional body text that governs agent behavior.

The default implementation lives at skills/taste-skill/SKILL.md, while legacy variants occupy adjacent directories like skills/taste-skill-v1/SKILL.md and skills/gpt-tasteskill/SKILL.md.

Step-by-Step Development Workflow

Follow this ten-step cycle when adding or modifying skills in the repository:

1. Clone the repository to your local machine using standard Git commands.

2. Install the skill locally using the Vercel Agent-Skills CLI to verify functionality before editing. According to the installation instructions in README.md (lines 46–58), run:

npx skills add . --skill "design-taste-frontend"

3. Edit the skill definition by modifying the front-matter and body content in the appropriate SKILL.md file. For example, editing skills/taste-skill/SKILL.md changes dials, design rules, or image-generation pipelines.

4. Add supporting assets (optional). Place new PNG or WebP files under assets/ or examples/ when your skill generates reference images, ensuring these paths match references in your Markdown.

5. Update documentation in the top-level README.md. Add new skills to the comparison table (referenced at lines 68–100) and adjust the "Which one should I use?" section to reflect your changes.

6. Bump the changelog by recording version changes in CHANGELOG.md, providing human-readable release notes for downstream consumers.

7. Commit your changes using standard Git flow:

git add .
git commit -m "Add/Update <skill-name> – <short description>"
git push

8. Open a Pull Request on GitHub. As noted in the "Feedback & Contributions" section (lines 38–44 of README.md), CI processes run automatically, and reviewers can install skills from your PR branch for verification.

9. Publish upon merge. The skill becomes publicly available via npx skills add https://github.com/Leonxlnx/taste-skill without additional packaging steps.

10. Iterate rapidly by repeating steps 3–9. Because skill definitions are plain text, you can edit, test locally with the CLI, and push changes in minutes.

Testing Skills Locally

Local validation relies entirely on the npx skills add command. The CLI discovers any SKILL.md file under the skills/ directory, enabling immediate testing of modifications without packaging or registry publication.

To test a custom skill created at skills/my-custom-skill/SKILL.md:

npx skills add . --skill "my-custom-skill"

This command makes the skill available to any LLM-agent that reads SKILL.md specifications, allowing you to verify behavior before committing changes.

Key Repository Files

Understanding these specific files accelerates navigation and contribution:

• skills/taste-skill/SKILL.md – The default v2 experimental skill definition containing front-matter and design rules.
• skills/taste-skill-v1/SKILL.md – Legacy v1 skill maintained for backward-compatible projects.
• skills/gpt-tasteskill/SKILL.md – Stricter variant optimized for GPT/Codex agents.
• README.md – Contains installation commands (lines 46–52), the skill comparison table (lines 68–100), and contribution guidelines (lines 38–44).
• CHANGELOG.md – Tracks version history for release management.
• skill.sh – Helper script that prints the repository URL for CI pipeline integration.
• assets/ and examples/ – Directories housing image resources referenced by skills and documentation.

Summary

• Zero-build workflow: Edit SKILL.md files directly; no compilation required.
• CLI-driven testing: Use npx skills add . --skill "<name>" to validate locally.
• Markdown source of truth: Skills reside in skills/<skill-name>/SKILL.md with YAML front-matter.
• Git-based versioning: Track changes in CHANGELOG.md and submit via GitHub PRs.
• Instant availability: Merged skills are immediately installable via the CLI without registry delays.

Frequently Asked Questions

Do I need to build or compile skills before testing them?

No. The workflow is zero-build. The Vercel Agent-Skills CLI reads SKILL.md files directly from the repository. You can edit the Markdown and immediately test with npx skills add . --skill "<name>" without running a build command or generating artifacts.

How do I add a completely new skill to the repository?

Create a new subdirectory under skills/ (e.g., skills/my-new-skill/), add a SKILL.md file with proper YAML front-matter containing name, description, and install fields, then test locally. Update the README.md skill table (lines 68–100) and CHANGELOG.md before submitting a pull request.

What is the difference between the v1 and v2 skill definitions?

The v2 skill at skills/taste-skill/SKILL.md represents the current experimental version with updated design rules, while skills/taste-skill-v1/SKILL.md maintains the legacy behavior for projects requiring backward compatibility. The skills/gpt-tasteskill/SKILL.md variant applies stricter constraints optimized specifically for GPT/Codex agents.

How are image assets managed in the development workflow?

Place PNG or WebP files in assets/ or examples/ directories, then reference them from your SKILL.md or README.md using relative paths. The repository uses these directories to showcase example outputs and provide reference material for image-generation skills.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}