Query, filter, and transform YAML data using Mike Farah yq v4+ expressions.
npx skills add https://github.com/hschne/pi-stuff --skill yq-hschne
Please help me install this Agent Skill. Skill: yq Source: https://github.com/hschne/pi-stuff/tree/main/skills/yq Command: npx skills add https://github.com/hschne/pi-stuff --skill yq-hschne
Manually editing, parsing, or validating YAML files for Kubernetes manifests, GitHub Actions workflows, Helm values, and other configuration files is slow and error-prone, especially when working with nested structures or multi-document YAML.
Use the yq skill to extract all container image names from the attached Kubernetes deployment file 'deployment.yaml'.
High-intent search queries and answers about installing and using this skill.
To update container image tags across Kubernetes manifests, you can filter and edit nested YAML fields in-place. This approach automates configuration management by applying jq-like expressions to modify deployment files without manual text editing.
Extracting specific fields from multi-document YAML files requires querying and filtering nested data structures. Using a jq-like expression engine allows you to select specific documents and extract values accurately from complex configuration files.
Yes, you can convert YAML to JSON for processing with jq or parse JSON input directly. Format conversion allows seamless integration between YAML configuration files and JSON-based data pipelines, bridging DevOps and automation workflows.
Yes, you need the Mike Farah yq v4+ command-line tool installed to execute queries and filter YAML data. This dependency provides the underlying jq-like expression engine required for parsing and modifying configuration files.
Processing Helm values with a command-line YAML parser automates data transformation and eliminates the error-prone nature of manual editing. It accurately targets nested structures for updates or deletions, ensuring reliable configuration management across environments.
Manually editing nested GitHub Actions workflow YAML files fails due to indentation errors and complex multi-document structures. Automated querying and in-place editing mitigate these risks by directly targeting specific fields for accurate data transformation.
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