Agent Skills is a directory and explorer for AI agent skills. It indexes skill repositories, lets you browse and preview SKILL.md instructions, and supports downloading a complete skill folder for local use. Operations teams can quickly find and deploy skills to automate workflows.
git clone https://github.com/futantan/agent-skills.md.gitAgent Skills is a directory and explorer for AI agent skills. It indexes skill repositories, lets you browse and preview SKILL.md instructions, and supports downloading a complete skill folder for local use. Operations teams can quickly find and deploy skills to automate workflows.
[{"step":"Search the Agent Skills directory using relevant keywords (e.g., 'automation', 'data processing') or tags to find skills matching your workflow needs.","tip":"Use the 'Preview' feature to review the SKILL.md file for each skill before downloading. Check the 'Dependencies' and 'Setup' sections to ensure compatibility with your environment."},{"step":"Download the complete skill folder for the selected skill. This includes all scripts, configuration files, and documentation needed for local deployment.","tip":"If the skill supports multiple environments (e.g., Docker, Python, Node.js), choose the one that best fits your infrastructure. Some skills include environment-specific setup instructions."},{"step":"Follow the skill’s setup instructions to install dependencies and configure the environment. This may involve running `pip install -r requirements.txt`, setting up API keys, or configuring environment variables.","tip":"Test the skill in a staging environment first. Use the provided sample data or mock inputs to verify functionality before deploying to production."},{"step":"Integrate the skill into your workflow. This could mean running it manually, scheduling it via cron or GitHub Actions, or embedding it in a larger automation pipeline.","tip":"Monitor the skill’s performance and logs after deployment. Adjust configurations or inputs as needed to optimize results."},{"step":"Contribute back to the community by sharing feedback, reporting issues, or even submitting improvements to the skill’s repository if you make enhancements.","tip":"If the skill is part of a larger automation project, consider publishing it to the Agent Skills directory to help others in your organization or industry."}]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/futantan/agent-skills.mdCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use the Agent Skills directory to find and deploy automation skills for [SPECIFIC WORKFLOW]. Search for skills tagged with [KEYWORDS] like 'data processing', 'report generation', or 'API integration'. Preview the SKILL.md instructions for [SKILL_NAME], then download the skill folder for local deployment. Test the skill in a staging environment before rolling it out to production.
After searching the Agent Skills directory for skills tagged with 'data processing' and 'API integration', I found the 'CSV-to-SQL-Transformer' skill. The SKILL.md preview showed it automates converting CSV files into SQL insert statements with customizable table schemas. The skill folder included a Python script, a README with setup instructions, and a sample CSV file for testing. I downloaded the folder and ran the setup in a Python virtual environment using `pip install -r requirements.txt`. The script processed a 50MB sales_data.csv file in under 2 minutes, generating 12,450 SQL insert statements. The output was validated against a staging PostgreSQL database, confirming all records were inserted correctly with no duplicates. The skill’s README recommended using it in CI/CD pipelines for ETL processes, which aligns with our team’s workflow for nightly data warehouse updates. I scheduled the skill to run automatically at 2 AM via a GitHub Actions workflow, replacing our manual Python script that previously took 30 minutes to process the same file.
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