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Guide · Agent skill

How to use the AI Coding Agent Guardrails agent skill

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

AI Coding Agent Guardrails is an agent skill: a set of instructions and templates that teaches an AI coding agent such as Claude Code, Cursor, Copilot or Codex to work within safety limits. It covers which files the agent may read or change, which shell commands it may run, and how secrets are kept out of its output. This guide explains what the skill does, how to install it, how to ask for it in plain language, and what to check before relying on it.

AI Coding Agent Guardrailssickn33/agentic-awesome-skills · MIT

Quick start

  1. 1Download and unzip the skill. Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use or the project's .claude/skills/ folder for one repository. In the Claude apps, upload the ZIP under Settings → Capabilities → Skills instead.
  2. 2Confirm the files are present. Check that the skill folder contains SKILL.md and references/details.md. The skill's extended notes sit in references/details.md.
  3. 3Ask for the guardrail you need. Describe the restriction in plain language, naming the files, commands or secrets you want limited.
  4. 4Merge a CLAUDE.md rules file. Adapt the template restrictions and allowed operations, and merge them into your existing instructions rather than replacing them.
  5. 5Define command and path limits. List allowed and blocked commands and paths for agents that run shell commands, and treat the YAML as a policy until your tooling enforces it.
  6. 6Add secret scanning. Set up git-secrets in the repository and run scan_agent_output.py on agent-produced files before committing.
  7. 7Review the output. Check every finding and every clean result yourself. Remember that the guidance is advisory and that real enforcement needs OS or tool permissions.

What an agent skill is and what this one makes the agent do

An agent skill is a folder with a SKILL.md file that an AI coding assistant can load to follow a specific set of instructions for a task. This skill, ai-coding-agent-guardrails, is aimed at securing AI coding agents across engineering teams.

When installed, it guides the agent through four areas: permission boundaries, secret protection, sandbox isolation and code review gates. Its own text says the guidance is advisory. A CLAUDE.md file supplies behavioural guidance, not an OS sandbox, so real enforcement needs operating system or tool permissions.

  • Permission boundaries: rules for which files the agent may read or change, and which commands it may run.
  • Secret protection: patterns for finding credentials in files and agent output.
  • Sandbox isolation: Linux file permission setup for a disposable checkout.
  • Code review gates: baseline checks for pull requests that include agent-written code.

Install the skill in Claude Code or the Claude apps

Download the skill ZIP from the page where you found this guide. Then choose one of two routes.

For a personal setup, unzip the download into ~/.claude/skills/. For a single project, unzip it into the project's .claude/skills/ folder. Either way, the skill folder should contain SKILL.md.

In the Claude apps, you can instead upload the ZIP under Settings → Capabilities → Skills.

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

Ask for it with natural requests

You do not need a special command. Describe the job in plain language and mention the agent's safety needs, so the skill is clearly relevant. Phrases such as the ones below are examples of requests this skill is written to handle.

Good requests name the thing you want limited: the files, the commands, or the secrets. Be specific about the repository and whether you want a rules file, a command list, or a scan.

  • Write a CLAUDE.md for this repo that stops the agent touching .env files and infrastructure folders.
  • Give me a command allowlist for this project that permits tests and linters but blocks deploy and delete commands.
  • Check the files this agent changed for leaked API keys before I commit.
  • Review this pull request workflow for the risks of agent-generated code.

Use the templates the skill provides

The skill includes a CLAUDE.md template with restrictions, allowed operations and code standards. Merge its rules into any instructions you already have rather than overwriting them, as the skill advises.

For agents that run shell commands, the skill shows an allowlist and a blocklist of commands and paths. The file says this YAML is a conceptual schema and is not consumed automatically by these clients, so you apply it as a written policy or adapt it into your own tooling.

For secret protection, the skill shows git-secrets setup for pre-commit scanning and a Python script, scan_agent_output.py, that searches files for common credential formats and prints a redacted report. Its own output states that no matches in a bounded regex scan is not proof of absence.

  • CLAUDE.md template: restrictions, allowed operations, code standards
  • Command allowlist and blocklist, plus blocked and allowed paths
  • git-secrets pre-commit scanning with added credential patterns
  • scan_agent_output.py for files the agent produced
python3 scan_agent_output.py <file1> [file2] ...

Set up a sandbox without risking your live repository

The skill includes a Linux-only setup script, setup-agent-workspace.sh, for a dedicated system user and access control lists. It is an administrator sketch for a disposable checkout. The skill warns against running it on a live developer repository, because it changes ownership and permissions recursively.

The script grants named ACL entries but does not give traversal on parent directories or default ACLs for new files, so you must design and verify those yourself. You also need to prevent symlink escapes and keep credentials outside the sandbox.

  • Use a disposable copy of the repository, never your working checkout.
  • Requires bash on Linux, setfacl tools and a dedicated system user.
  • Verify access for the agent user after setup, including new files it creates.
  • Keep credentials outside the sandbox directory.

Install git-secrets for pre-commit scanning

The skill's secret scanning example clones git-secrets from GitHub, builds it, and enables it in a repository. It registers AWS patterns and adds custom patterns for private keys, GitHub tokens, Slack tokens and similar credentials. It also lists allowed placeholder strings to reduce false positives.

The git-secrets setup itself needs network access to clone the project. Review the script before you run it, since it runs commands on your machine.

  • Clone and build git-secrets from its GitHub repository.
  • Run git secrets --install inside your repository.
  • Run git secrets --register-aws to add AWS patterns.
  • Add custom patterns, then add allowed exceptions for known placeholder values.
git secrets --install git secrets --register-aws

What people use it for

  • An engineering lead writing one CLAUDE.md that every developer's agent reads, blocking edits to infrastructure and workflow folders.
  • A developer limiting an agent to src/, tests/ and docs/ with a clear list of blocked commands such as rm -rf and terraform.
  • A security-minded team scanning agent-generated files for private keys or tokens before they reach version control.
  • A team adding git-secrets pre-commit checks so that commits containing credential patterns are flagged.
  • A platform admin preparing a disposable Linux checkout with a restricted system user for an agent to work in.

Frequently asked questions

Is the AI Coding Agent Guardrails skill free?

The skill is published under the MIT licence, as stated in its metadata. Check the licence file linked from the skill for the full terms before you reuse or adapt it.

What do I need to use this skill?

The skill's text says it is docs-only and that helper scripts and templates are not bundled. The scripts it shows need bash on Linux for the workspace setup, setfacl tools and a dedicated system user for the sandbox, git and git-secrets for pre-commit scanning, and Python 3 for the output scanner. It also mentions relevant security tooling and vault CLIs.

Does a CLAUDE.md file actually stop an agent from reading secrets?

Not on its own. The skill states that a CLAUDE.md supplies behavioural guidance, not an OS sandbox. Real enforcement needs operating system or tool permissions, so use the Linux access controls or the agent's own permission settings alongside it.

Will the Linux setup script break my repository?

It can, if you run it on a live developer repository. The skill warns that the script changes ownership and permissions recursively. Run it only on a disposable checkout and verify the result.

How does this compare with other agent safety tools?

The skill's text does not compare itself with other tools, so this guide cannot make that comparison. It is a set of guidance, templates and scripts for Claude Code, Cursor, Copilot and Codex, and it is advisory unless paired with OS or tool permissions.

Why did the secret scan miss something?

The scanner is a bounded regex check. The skill states that no matches is not proof of absence, and git-secrets exclusions can hide real findings. Review files manually for anything sensitive.

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Our verdict

Use this skill if you lead a team setting up AI coding agents and want written rules, command limits and secret checks to share across developers. Pick something else if you only want faster coding, since the setup is heavy and can block normal work, or if you need enforced protection, which must come from OS or tool permissions rather than a Markdown file.

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