Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skillsnpx agentmods add skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrailsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrails)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrails"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrails/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrails"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/ai-coding-agent-guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.08497 |
| Opus 5 | $0.00022 | $0.04248 |
| Sonnet 5 | $0.00009 | $0.01699 |
| Haiku 4.5 | $0.00004 | $0.00850 |
Grade C, and why
ai-coding-agent-guardrails scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- NEVER execute `rm -rf`, `DROP TABLE`, `kubectl delete`, or `terraform destroy` commands Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- "curl" How it starts
The opening of the file, as written. The whole thing — 1,139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agent Guardrails
Secure the use of AI coding agents across engineering teams. This skill covers permission boundaries, secret protection, sandbox isolation, code review gates, and audit trails for Claude Code, Cursor, Copilot, and Codex.
When to Use
Apply these guardrails when:
- Onboarding AI coding agents into an engineering team for the first time
- Developers are using Claude Code, Cursor, Copilot, or Codex to generate production code
- Agents have access to repositories containing secrets, infrastructure configs, or sensitive business logic
- Your compliance framework (SOC 2, ISO 27001, FedRAMP) requires controls around automated code generation
- Autonomous or semi-autonomous agents are creating pull requests without direct human typing
- You need to enforce consistent security policies across multiple agents and team members
Signs you need tighter guardrails:
- Agents have committed secrets or credentials to version control
- Agent-generated code has introduced vulnerabilities caught late in the pipeline
- No clear audit trail distinguishes human-written from AI-generated code
- Developers are bypassing code review for "simple" agent changes
- Agents are executing arbitrary shell commands in production-connected environments
Permission Boundaries
CLAUDE.md Configuration
Create a CLAUDE.md at the repository root to restrict Claude Code behavior:
# CLAUDE.md
## Restrictions
- NEVER read or output contents of .env, .env.*, secrets.yaml, or any file matching *.pem, *.key
- NEVER execute `rm -rf`, `DROP TABLE`, `kubectl delete`, or `terraform destroy` commands
- NEVER push directly to main or master branches
- NEVER modify files in the infrastructure/, terraform/, or .github/workflows/ directories without explicit user approval
- NEVER install new dependencies without listing them first for review
- NEVER access or display API keys, tokens, passwords, or connection strings
## Allowed Operations
- Read and modify application source code in src/, lib/, and tests/
- Run test suites with `npm test`, `pytest`, `go test`
- Run linters with `eslint`, `ruff`, `golangci-lint`
- Create new branches with prefix `ai/` or `agent/`
- Create and modify files in docs/ directory
## Code Standards
- All new functions must include docstrings or JSDoc comments
- All new code must have corresponding unit tests
- Follow existing code style and patterns in the repository
- Maximum file length: 500 lines. Suggest splitting if exceeded.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 1,139 lines · 43 tokens per session scan C 0842ee19de8b
ai-coding-agent-guardrails is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,084 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 8,497 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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