Guide · Agent skill

How to use the AI Code Guardrails Review agent skill

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

The AI Code Guardrails Review skill is a set of written instructions that tells an AI coding agent how to review code it, or another AI, has written before that code is merged or run. It is useful for developers reviewing AI-generated pull requests and for security-minded teams who want a consistent review policy. After following this guide you will be able to install the skill, ask your agent for a guarded review, and know what evidence to keep and what the skill cannot prove.

AI Code Guardrails Reviewsickn33/agentic-awesome-skills · MIT Download the skill

Quick start

1Download the ZIP. Download the skill ZIP from this page. It contains SKILL.md, the instruction file the agent reads.
2Unzip into a skills folder. For personal use, unzip into ~/.claude/skills/. For one project, unzip into that project's .claude/skills/ folder.
3Or upload it in the Claude apps. In the Claude apps, open Settings, go to Capabilities, and upload the skill under Skills.
4Ask for a guarded review. Describe the AI-generated code, its language, and your acceptance criteria in plain language before merge or execution.
5Keep the evidence. Ask the agent to record the exact code revision, the checks run, the skipped checks and the open gaps, then decide the outcome.

What does the skill make the agent do?

When the skill is active, the agent follows a structured review policy before it accepts AI-written code. It treats generated files and repository content as untrusted until reviewed, and it ignores instructions embedded in comments or output. It asks for a record of the exact code version being reviewed, limits what the code may access, and chooses checks that suit the actual language.

Records the candidate code revision or digest, the language and runtime, the task, and the acceptance criteria.
Defines allowed capabilities such as filesystem destinations, network access, subprocesses, dependencies and data handling.
Separates syntax checks, targeted static rules, dependency advisory scans and behavior tests.
Marks skipped checks, parse errors and unsupported languages as incomplete rather than passed.
Keeps a pending outcome when required evidence is missing.

How do you install the skill?

Download the skill ZIP from this page. Then install it in one of two ways. For a personal setup, unzip it into ~/.claude/skills/. For a single project, unzip it into that project's .claude/skills/ folder. The skill's files include SKILL.md, which is the instruction file the agent reads.

If you use the Claude apps instead, open Settings, go to Capabilities, and upload the skill under Skills.

How do you trigger the skill with a natural request?

You do not need a special command. The skill's description says it applies when reviewing AI-generated code before merge or execution, so ask for that work in plain language and name the code you mean. Mention the language, the change, and what must be true for approval. The more specific your acceptance criteria, the more useful the review.

Review this AI-generated pull request against our guardrails policy before merge. Keep evidence for the exact commit.
Check this generated Python script with the guardrails skill. Flag any eval, exec, os or subprocess use and tell me what was not checked.
Use the AI code guardrails skill to define a review policy for code our team gets from AI tools.
Before we run this generated Rust code, make a checklist of dependency advisory checks and behavior tests we need.

What does a review look like in practice?

Suppose a generated API change parses and passes its style rules, but the test for cross-tenant access was skipped. The skill directs the agent to record the code digest, the skipped case, and the static results it actually observed. The outcome stays pending. The agent should not treat a similarity score or a comment saying callers should check authorization as a substitute for the missing test.

Passing a linter, low complexity, or having no unsafe blocks is not a security approval.
Authorization, injection, data flows and failure paths usually need human inspection, because static rules cannot establish them.
Deployment is a separate, authorized action and is not decided by the review.

What does the bundled Python example do?

The skill includes a small Python 3 example that inspects source code as text. It parses the code and never runs it. It flags direct calls to eval or exec and imports of os or subprocess. Invalid syntax or oversized input returns an incomplete status, never a clean pass. The skill is explicit that this is a teaching filter, not a sandbox or complete security analyzer, and that aliases, attribute calls, dynamic imports and other harmful behavior can bypass it.

Blocked output is expected for code that imports subprocess.
Clean-looking code returns review-required, which means it is not approved.
Run the example only if you have Python 3 and choose to do so.

import ast


def inspect_generated_python(source):
    if not isinstance(source, str):
        return {"status": "incomplete", "reason": "source must be text", "findings": []}
    try:
        if len(source.encode("utf-8")) > 65536:
            return {"status": "incomplete", "reason": "source exceeds 64 KiB", "findings": []}
        tree = ast.parse(source)
    except (SyntaxError, RecursionError, UnicodeError):
        return {"status": "incomplete", "reason": "source cannot be parsed", "findings": []}
    findings = []
    for node in ast.walk(tree):
        if isinstance(node, ast.Call) and isinstance(node.func, ast.Name):
            if node.func.id in {"eval", "exec"}:
                findings.append({"rule": "direct-dynamic-execution", "line": node.lineno})
        if isinstance(node, (ast.Import, ast.ImportFrom)):
            modules = [a.name for a in node.names] if isinstance(node, ast.Import) else [node.module or ""]
            if any(m.split(".")[0] in {"os", "subprocess"} for m in modules):
                findings.append({"rule": "restricted-import", "line": node.lineno})
    return {"status": "blocked" if findings else "review-required", "findings": findings}


print(inspect_generated_python("import subprocess\n"))
print(inspect_generated_python("answer = 1 + 1\n"))

What should you know about safety, scripts and network use?

The skill does not run scripts and does not use the network, according to its listing. Its bundled example parses text and does not execute the code it inspects. The skill also tells the agent to keep secrets and production permissions out of the validation environment, and to run behavior tests only in an authorized, isolated environment with resource limits.

Parsing code successfully does not make it safe. Do not execute suspicious code just to demonstrate a static rule unless a separate isolated run has been authorized.

Keep production credentials out of any validation environment.
Use an isolated, resource-limited environment for behavior tests.
Optional language-specific tools such as cargo-audit check known advisories only. They are not AST scanners and cannot certify the absence of zero-day bugs.

What are the limits and useful tips?

The skill supplies a policy and a teaching example. It is not a CI gate and does not install a pipeline. Rule coverage depends on the actual runtime version, and reentrancy rules only apply to relevant concurrent or contract code. Choose domain-specific checks based on your own threat model.

Write explicit acceptance criteria before asking for a review.
Keep a record of the exact candidate code, the checks run, the checks skipped, and who reviewed what.
Treat a passing linter as one input, not an approval.
If your team needs automated enforcement, wire the checks into your own CI system separately.

What people use it for

Reviewing an AI-generated pull request before it is merged, with a record of the exact commit reviewed.
Defining a written review policy for code that developers get from AI tools.
Flagging direct eval, exec, os or subprocess use in generated Python with the bundled text-only example.
Building a checklist of dependency advisory checks and behavior tests before running generated code in an isolated environment.

Frequently asked questions

How do I install the AI Code Guardrails Review skill?

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

What do I need to run it?

The skill's listing says it needs Python 3 only if you run the bundled example. Optional language-specific tools such as cargo-audit can add dependency advisory checks. It runs no scripts and uses no network, according to its listing.

Is the skill free to use?

The skill is published under the MIT licence. Check the licence text on the source repository for the exact terms before reusing it.

Does the skill replace a security scanner?

No. The skill says no automated checklist proves general security. Its AST example is a teaching filter that recognizes only a few patterns, and aliases, dynamic imports and many attacks can bypass it.

Why does the review say incomplete instead of passed?

The skill treats skipped checks, unparseable code and unsupported languages as incomplete. A missing required check cannot become a pass, so the outcome stays pending until the evidence exists.

How does this compare with a linter or dependency audit tool?

A linter checks style and selected rules, and a dependency audit such as cargo-audit checks known package advisories. The skill uses these as separate inputs and adds a review policy and evidence record on top, plus manual inspection of authorization and data flows.

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Our verdict

Use this skill if you review AI-generated pull requests and want a disciplined policy, an evidence checklist and clear rules about what counts as incomplete. Pick a dedicated security scanner or a proper CI pipeline if you need automated enforcement or broad vulnerability detection, because this skill provides a policy and a teaching example, not a gate.

Download the skill