Agentic actions auditor
Skill ranbot-ai/awesome-skills/skills/agentic-actions-auditor
Awesome Claude Skills, Tools for Customizing Claude AI workflows
npx -y skills add ranbot-ai/awesome-skills --skill agentic-actions-auditorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
Copied from the file, not written here
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
SKILL.md
5.3 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it
Agentic Actions Auditor
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.
When to Use
- Auditing a repository's GitHub Actions workflows for AI agent security
- Reviewing CI/CD configurations that invoke Claude Code Action, Gemini CLI, or OpenAI Codex
- Checking whether attacker-controlled input can reach AI agent prompts
- Evaluating agentic action configurations (sandbox settings, tool permissions, user allowlists)
- Assessing trigger events that expose workflows to external input (
pull_request_target,issue_comment, etc.) - Investigating data flow from GitHub event context through
env:blocks to AI prompt fields
When NOT to Use
- Analyzing workflows that do NOT use any AI agent actions (use general Actions security tools instead)
- Reviewing standalone composite actions or reusable workflows outside of a caller workflow context (use this skill when analyzing a workflow that references them via
uses:) - Performing runtime prompt injection testing (this is static analysis guidance, not exploitation)
- Auditing non-GitHub CI/CD systems (Jenkins, GitLab CI, CircleCI)
- Auto-fixing or modifying workflow files (this skill reports findings, does not modify files)
Rationalizations to Reject
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.
Audit Methodology
Follow these steps in order. Each step builds on the previous one.
Step 0: Determine Analysis Mode
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).
URL Parsing
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://.
Fetch Workflow Files
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
.ymlor.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.
Error Handling
Do NOT pre-check gh auth status before API calls. Attempt the API call and handle failures:
- **401/a
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most context ai engineering skills give in ~1.1k tokens
Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-07
- Dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
- Dispatch a final code reviewer after all tasksin 33 of 1193, across 8 files
- Provide full task text to the subagentin 30 of 1193, across 9 files
- Review spec compliance before code qualityin 27 of 1193, across 10 files
- Make the hook script executablein 26 of 1193, across 8 files
- Re-snapshot after navigation or DOM changesin 25 of 1193, across 19 files
- Read files before editing themin 22 of 1193, across 11 files
- Answer subagent questions before proceedingin 22 of 1193, across 7 files
- Mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
- Merge hook into existing settingsin 21 of 1193, across 3 files
- Ask if installation is global or projectin 20 of 1193, across 2 files
- Copy the hook script to target locationin 20 of 1193, across 2 files
Said here and by no other author read
- strip trailing slashes and git suffix from input
- keep only filenames ending in yml or yaml
- append specified ref to every API call
- report number and names of found workflow files
- use remote analysis mode for provided GitHub repositories
- use local analysis mode without provided URLs
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.