skills/agentic-actions-auditor/SKILL.md
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where
npx skillsauth add ranbot-ai/awesome-skills agentic-actions-auditorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Static security analysis guidance for GitHub Actions workflows that invoke AI coding agents. This skill teaches you how to discover workflow files locally or from remote GitHub repositories, identify AI action steps, follow cross-file references to composite actions and reusable workflows that may contain hidden AI agents, capture security-relevant configuration, and detect attack vectors where attacker-controlled input reaches an AI agent running in a CI/CD pipeline.
pull_request_target, issue_comment, etc.)env: blocks to AI prompt fieldsuses:)When auditing agentic actions, reject these common rationalizations. Each represents a reasoning shortcut that leads to missed findings.
1. "It only runs on PRs from maintainers"
Wrong because it ignores pull_request_target, issue_comment, and other trigger events that expose actions to external input. Attackers do not need write access to trigger these workflows. A pull_request_target event runs in the context of the base branch, not the PR branch, meaning any external contributor can trigger it by opening a PR.
2. "We use allowed_tools to restrict what it can do"
Wrong because tool restrictions can still be weaponized. Even restricted tools like echo can be abused for data exfiltration via subshell expansion (echo $(env)). A tool allowlist reduces attack surface but does not eliminate it. Limited tools != safe tools.
3. "There's no ${{ }} in the prompt, so it's safe"
Wrong because this is the classic env var intermediary miss. Data flows through env: blocks to the prompt field with zero visible expressions in the prompt itself. The YAML looks clean but the AI agent still receives attacker-controlled input. This is the most commonly missed vector because reviewers only look for direct expression injection.
4. "The sandbox prevents any real damage"
Wrong because sandbox misconfigurations (danger-full-access, Bash(*), --yolo) disable protections entirely. Even properly configured sandboxes leak secrets if the AI agent can read environment variables or mounted files. The sandbox boundary is only as strong as its configuration.
Follow these steps in order. Each step builds on the previous one.
If the user provides a GitHub repository URL or owner/repo identifier, use remote analysis mode. Otherwise, use local analysis mode (proceed to Step 1).
Extract owner/repo and optional ref from the user's input:
| Input Format | Extract |
|-------------|---------|
| owner/repo | owner, repo; ref = default branch |
| owner/repo@ref | owner, repo, ref (branch, tag, or SHA) |
| https://github.com/owner/repo | owner, repo; ref = default branch |
| https://github.com/owner/repo/tree/main/... | owner, repo; strip extra path segments |
| github.com/owner/repo/pull/123 | Suggest: "Did you mean to analyze owner/repo?" |
Strip trailing slashes, .git suffix, and www. prefix. Handle both http:// and https://.
Use a two-step approach with gh api:
List workflow directory:
gh api repos/{owner}/{repo}/contents/.github/workflows --paginate --jq '.[].name'
If a ref is specified, append ?ref={ref} to the URL.
Filter for YAML files: Keep only filenames ending in .yml or .yaml.
Fetch each file's content:
gh api repos/{owner}/{repo}/contents/.github/workflows/{filename} --jq '.content | @base64d'
If a ref is specified, append ?ref={ref} to this URL too. The ref must be included on EVERY API call, not just the directory listing.
Report: "Found N workflow files in owner/repo: file1.yml, file2.yml, ..."
Proceed to Step 2 with the fetched YAML content.
Do NOT pre-check gh auth status before API calls. Attempt the API call and handle failures:
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