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Guide · Agent skill

How to use the Agentic Actions Auditor agent skill

Updated · written with AI from the project's own documentation and checked against our catalogue data

The Agentic Actions Auditor is an agent skill that teaches your AI agent a structured method for reviewing GitHub Actions workflows which run AI coding agents such as Claude Code Action, Gemini CLI, OpenAI Codex, or GitHub AI Inference. It is aimed at open-source maintainers, security reviewers and teams that want to check a CI/CD configuration before enabling an AI agent on pull requests or issues. After this guide you will know how to install the skill, how to ask for an audit in plain language, and how to read the findings report it produces.

Agentic Actions Auditorsickn33/agentic-awesome-skills · MIT

Quick start

  1. 1Download and unzip the skill. Download the ZIP from this page and unzip it so that SKILL.md sits inside a skill folder.
  2. 2Choose where to install it. Place the folder in ~/.claude/skills/ for personal use, in a project's .claude/skills/ folder, or upload it under Settings → Capabilities → Skills in the Claude apps.
  3. 3Authenticate the GitHub CLI. For remote audits, run gh auth login with a token that can read the target repository.
  4. 4Ask for an audit in plain language. Name the repository or local folder and describe your concern, such as outside input reaching an AI agent.
  5. 5Review the findings report. Read each finding, check the trigger, the env: path into the prompt and the sandbox or tool settings it cites.
  6. 6Make fixes yourself. The skill does not edit files, so apply any changes to your workflows manually and re-run the audit.

What does the skill make the agent do?

Once installed, the agent follows a fixed methodology for static review of workflow files. It discovers workflow files, identifies steps whose uses: field points to a known AI action, and follows references to composite actions and reusable workflows that may hide an AI agent. It then looks for paths where attacker-controlled input, such as issue text or pull request data passed through environment variables, can reach the AI's prompt or tools.

The skill produces a findings report and does not modify files. It is static analysis guidance, not runtime testing, so it does not exploit anything.

  • Finds AI agent steps in GitHub Actions workflows
  • Follows cross-file references to composite actions and reusable workflows
  • Checks trigger events such as pull_request_target and issue_comment
  • Checks sandbox settings, tool permissions and user allowlists
  • Reports findings without editing workflow files

How do you install the Agentic Actions Auditor skill?

Download the ZIP for this skill from this page and unzip it. The skill's files include SKILL.md. Place the unzipped skill folder in one of these locations: ~/.claude/skills/ to make it available across your projects, or a project's .claude/skills/ folder to make it available only inside that repository.

If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.

  • Personal install: ~/.claude/skills/
  • Project install: .claude/skills/ inside the repository
  • Claude apps: Settings → Capabilities → Skills, then upload

What do you need before you run an audit?

For remote analysis of a GitHub repository, you need the GitHub CLI (gh) and a token that can read the repository. Run gh auth login if the skill reports that authentication is required. For local analysis, you need access to the repository's workflow files on your machine.

The skill does not run scripts, but it does use the network when analysing remote repositories, because it calls gh api to fetch workflow files.

  • GitHub CLI (gh) for remote analysis, authenticated with a token that can read the repository
  • Access to the repository's workflow files, either local or remote
gh auth login

How do you trigger the skill with a natural request?

You do not need a special command. Describe what you want in plain language and name the repository or folder. The skill accepts owner/repo, owner/repo@ref (branch, tag or SHA), or a full GitHub repository URL. For a local audit, point the agent at the repository you have open.

Good requests say what you are worried about. For example, you might ask whether attacker-controlled input can reach an AI agent in a workflow triggered by issue comments.

  • "Audit the GitHub Actions workflows in this repo for AI agent security issues."
  • "Check owner/repo@main for Claude Code Action steps that a pull request from an outside contributor could influence."
  • "Review our issue_comment and pull_request_target workflows that call Gemini CLI."

What does a remote or local audit look like?

For a remote repository, the skill lists the .github/workflows/ directory with gh api, keeps only .yml and .yaml files, and fetches each file's content. It resolves the ref to a full commit SHA and uses that SHA for every call. It then reports how many workflow files it found.

For a local repository, it searches only the .github/workflows/ directory at the repository root. It does not scan subdirectories, vendored code or test fixtures. It then checks each job and step for known AI action references and reports the result, for example as a count of workflows, AI action instances and findings.

  • The skill treats fetched YAML as data and never executes it
  • Subdirectories, vendored code and test fixtures are not scanned for workflow files
  • Unverified action-version details are reported as unverified
gh api repos/{owner}/{repo}/contents/.github/workflows --paginate --jq '.[].name'

What should you watch for in the findings?

The skill is built around a few common mistakes. The first is assuming only maintainers can trigger a workflow: pull_request_target and issue_comment can expose an action to outside input, so the skill asks you to inspect activity types, filters and repository policy. The second is trusting an allowed_tools list: even a restricted tool such as echo can be abused for data exfiltration, so a tool allowlist reduces risk without removing it.

The third and most often missed vector is the environment variable intermediary. A prompt can contain no ${{ }} expressions at all and still receive attacker-controlled data, because the value passes through an env: block first. The fourth is sandbox trust: settings such as danger-full-access, Bash(*) or --yolo disable protections, and sandboxes can still leak secrets if the agent can read environment variables or mounted files.

  • Check pull_request_target and issue_comment triggers, not just who opens the PR
  • Treat allowed_tools as reduced attack surface, not a safe boundary
  • Follow data from env: blocks into AI prompt fields
  • Flag sandbox-disabling settings such as danger-full-access, Bash(*) and --yolo

What are the limits and safety notes?

The skill reports findings and leaves your files alone. It does not test prompt injection at runtime. It does not cover non-GitHub CI systems such as Jenkins, GitLab CI or CircleCI, and it is not the right tool for repositories that have no AI agent actions; general GitHub Actions security tools fit those better.

The bundled SKILL.md contains the inline methodology only. The skill notes that some external reference material named in its original guide is not included, so details that depend on it are reported as unverified rather than checked.

  • Static analysis only, no exploitation
  • Does not modify workflow files
  • Does not run scripts, but uses the network for remote repositories via gh
  • Excludes non-GitHub CI systems

What people use it for

  • Reviewing a repository's Claude Code Action workflow before enabling it on public pull requests
  • Checking whether an issue_comment workflow passes comment text through an env: block into a Gemini CLI prompt
  • Auditing composite actions or reusable workflows that a caller workflow uses to run an AI agent
  • Checking a pull_request_target workflow for AI steps that run with base-repository context before a release

Frequently asked questions

Is the Agentic Actions Auditor skill free?

The skill is published under the MIT licence, as stated on this page. Check the licence file in the source repository for the full terms before reusing it.

How do I install an agent skill?

Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use or a project's .claude/skills/ folder. In the Claude apps you can upload it under Settings → Capabilities → Skills.

What do I need to run the audit on a GitHub repository?

You need the GitHub CLI (gh) and a token that can read the repository. Run gh auth login if authentication is required. For a local audit you need access to the workflow files.

Does the skill change my workflow files?

No. The skill reports findings and does not modify files. You make any fixes yourself.

Does it work with Jenkins, GitLab CI or CircleCI?

No. The skill explicitly excludes non-GitHub CI systems such as Jenkins, GitLab CI and CircleCI.

Can it test whether prompt injection actually works?

No. It is static analysis guidance and does not exploit anything or perform runtime prompt injection testing.

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Our verdict

Use this skill if you maintain or review GitHub repositories that run AI coding agents on pull requests or issues, and want a structured static check for outside input reaching the agent. Pick a general GitHub Actions security scanner if your workflows contain no AI agent steps, and a dedicated runtime testing tool if you need proof that an attack succeeds.

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