> ## Documentation Index
> Fetch the complete documentation index at: https://docs.declaw.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# DevOps auditor

> Agent audits a Dockerfile and Kubernetes manifest using the devops template, with transformation rules to mask any leaked AWS keys before they reach the LLM.

## Use case

Auditing infrastructure-as-code is exactly the kind of job you
want in a sandbox: the input is often untrusted (someone's checked
in config) and the tooling is heavy (hadolint, kubeval, trivy,
terraform, kubectl). The `devops` template has the tooling
pre-installed. A `TransformationRule` keeps real credentials out
of LLM traces even if the manifest accidentally ships one.

## Template

`devops` — git, kubectl, terraform, docker, hadolint, aws-cli,
yq, jq. Heaviest of the built-in templates; first cold-start in
a fresh worker is slower than `python`.

## Run it

```bash theme={null}
export DECLAW_API_KEY=dcl_...
export DECLAW_DOMAIN=api.declaw.ai
export OPENAI_API_KEY=sk-...

python cookbook/examples/openai-agents-devops-audit/main.py
```

## Security policy

```python theme={null}
SecurityPolicy(
    injection_defense=InjectionDefenseConfig(enabled=True, sensitivity="high", domains=["api.openai.com"]),
    transformations=[
        TransformationRule(
            match=r"AKIA[0-9A-Z]{16}",
            replace="***AWS_KEY_REDACTED***",
        ),
    ],
    network=NetworkPolicy(allow_out=["api.openai.com"]),
)
```

**Why transformations matter here.** The sample `Dockerfile` in
the recipe contains `ENV AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE`.
Without the rule, that value would travel through the LLM and be
recorded in every trace / audit log / debug output downstream.
With the rule, the edge proxy rewrites it to
`***AWS_KEY_REDACTED***` on every outbound request. The auditor
sees the placeholder and can still reason about "there's a
credential here that needs to move to a secret" — it just doesn't
get to see the value.

## Env isolation

```python theme={null}
envs={
    "CLUSTER_NAME": "prod-us-east",
    "SEVERITY_FLOOR": "medium",
    "AUDIT_ID": "aud-2026-04-19",
}
```

Audit IDs, cluster names, and severity thresholds travel as env,
not prompt. The agent's instructions tell it to `printenv` to pick
them up.

## What the agent does

1. `printenv CLUSTER_NAME SEVERITY_FLOOR AUDIT_ID`.
2. `hadolint /workspace/audit/Dockerfile` — catches Dockerfile
   smells (latest tag, missing USER, apt-get update-without-install).
3. Parse `/workspace/audit/deploy.yaml` — flag `runAsUser: 0`,
   `privileged: true`, `:latest` image tag.
4. Write `/workspace/audit/findings.md` with sections:
   * `## Metadata` (from env)
   * `## Dockerfile findings`
   * `## Kubernetes findings`
   * `## Remediation`
5. Return `findings.md`.

## Filesystem isolation

The sample Dockerfile + deploy.yaml are seeded by the Python
driver calling `inner._sbx.write_file(...)` — they never touch
your host. All reports come back through `read_file`. The next
audit run gets a fresh VM with none of this run's state.

## Full source

See `cookbook/examples/openai-agents-devops-audit/main.py` in the repo.