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Automate Github Issues

Official
google-labs-code·vlatest·backend·Apache-2.0

Helps with automation, GitHub as part of agent workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add google-labs-code/jules-skills --skill automate-github-issues -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

google-labs-code

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: bun.sh.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    www.apache.orgstandardsapi.github.comsource hostgithub.comsource hostbun.shundeclared

    What it found

    • skills/automate-github-issues/scripts/setup.sh:27contacts bun.sh
      curl -fsSL https://bun.sh/install | bash

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 2 places.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • skills/automate-github-issues/scripts/setup.sh:27remote script piped into a shell
      curl -fsSL https://bun.sh/install | bash
    • skills/automate-github-issues/SKILL.md:136tells the user to run: remote script piped into a shell(in the instructions, not code)
      - **Bun not found**: Install Bun: `curl -fsSL https://bun.sh/install | bash`

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned google-labs-code/jules-skills at 7a76d3bfcf.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7a76d3bfcf
Manifest
skills/automate-github-issues/SKILL.md
Scanned
19 files, 1,879 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The automate-github-issues skill enables AI agents to programmatically manage GitHub Issues, allowing for the creation, updating, closing, and commenting on issues within repositories. It streamlines project management by letting agents track bugs, feature requests, and tasks directly through code execution.

When to use it

  • Automatically triaging incoming bug reports by assigning them to specific developers based on error logs.
  • Generating detailed status updates for long-running tasks by modifying issue descriptions with progress data.
  • Closing resolved issues and archiving related discussions once a pull request is merged successfully.
  • Creating new issues from automated test failures detected in CI/CD pipelines without human intervention.

Key capabilities

  • Create new GitHub Issues with custom titles, bodies, and labels.
  • Update existing issue descriptions and assignees dynamically.
  • Add comments to threads to provide context or request clarification.
  • Transition issues between open, closed, and other states.
  • Interact with specific repositories within a user's GitHub organization.

Example prompts

  • "Create a new high-priority issue in the 'backend-service' repo titled 'Memory Leak in Auth Module' and assign it to @dev-team-lead."
  • "Add a comment to issue #42 explaining that the fix has been verified on the staging environment."
  • "Close all issues labeled 'duplicate' that were created before last week."

Tips & gotchas

Ensure your AI agent has valid GitHub authentication tokens with sufficient permissions (e.g., repo scope) to modify issues in target repositories. Be cautious when automating issue closure, as agents may misinterpret context and close valid ongoing discussions prematurely.