Manage GitHub Issues via gh CLI with JSON and jq scripting.
npx skills add https://github.com/serejaris/ris-claude-code --skill gh-issues
Please help me install this Agent Skill. Skill: gh-issues Source: https://github.com/serejaris/ris-claude-code/tree/main/skills/gh-issues Command: npx skills add https://github.com/serejaris/ris-claude-code --skill gh-issues
Managing GitHub issues via the web UI is slow, and AI context often gets lost between sessions. This Skill enables fast, CLI-driven issue management and stores AI session context directly in issue comments for seamless handoffs.
Use the gh-issues Skill by installing it into Claude's skill path: cp -r skills/gh-issues ~/.claude/skills/ Then activate the skill and issue your first task, e.g.: gh issue create -t "Sample task" -b "Describe the task" -l task For quick hands-on, you can load active issues with: gh issue list -s open
High-intent search queries and answers about installing and using this skill.
You can manage GitHub Issues from the CLI by using gh commands to create, search, and update issues, while storing AI session context directly in issue comments to preserve work state across sessions.
Yes, you can perform bulk operations on GitHub Issues by leveraging advanced search filters and JSON outputs with jq, allowing you to triage and update multiple issues at once through scriptable CLI patterns.
You can automate an issue to branch to PR workflow by following the issue lifecycle pattern from backlog to in-progress to done, using gh CLI commands to link issues to branches and pull requests for streamlined task tracking.
Yes, you need both the gh CLI installed and a GitHub account authenticated, because the workflow interfaces directly with the gh CLI and assumes read and write access to repository data for issue management.
The best way to script GitHub Issues searches for triage is using gh CLI search filters combined with jq, which parses JSON outputs to extract specific issue fields for automated processing and bulk updates.
Managing GitHub Issues via the web UI slows development because it requires manual context switching, whereas a CLI-driven approach with AI context stored in comments enables fast, scriptable issue management and seamless session handoffs.
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