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AegisOps-AI Governance Guardrails

Guides review of Linux kernel patches, Terraform cost changes and Kubernetes hardening using an external tool and AI model

Source Download skill
Repository
sickn33/agentic-awesome-skills
Stars
49,023
Licence
MIT
Files
1 file

What it does

Once installed, the agent knows how to guide you through a DevSecOps workflow: reviewing raw C kernel patches for memory-safety issues, scanning Terraform plan output for surprise cloud cost increases, and drafting hardened Kubernetes securityContext settings from plain-language requirements. The skill itself is only a guide; the actual scripts (patch_analyzer.py, cost_auditor.py, k8s_policy_generator.py) live in a separate upstream repository and are not bundled here. Its outputs are candidate findings to be verified by humans, not automatic approvals.

Great for

  • Platform or security engineers who want AI-assisted second opinions on kernel patches or infrastructure changes
  • Teams checking Terraform plans for accidental expensive resource upgrades before applying
  • Developers drafting least-privilege Kubernetes deployment manifests that they will test in staging

Skip it if

  • Web application scanning for XSS or SQL injection, because the skill says to use dedicated SAST tools instead
  • Analysing Python or JavaScript code, since the patch analyser is described as optimised for C logic
  • Anyone unable to authorise sending private patches, cluster configs or cloud plans to a paid third-party model API, as this exposes sensitive data and cost

Try asking

  • “Review this Linux kernel git diff for possible use-after-free or stale-state issues and list what to verify”
  • “Check this terraform plan output for resource changes that could cause a large cost increase”
  • “Turn the requirement 'all containers must run as non-root with a read-only root filesystem' into a candidate Kubernetes deployment manifest”
Step-by-step guide: how to use AegisOps-AI Governance GuardrailsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the MIT licence and an attribution note.

    aegisops-ai.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip aegisops-ai.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip aegisops-ai.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Verify the upstream repository, licence, dependencies and entry point at a fixed revision before running anything, and redact secrets and account IDs from inputs.
  • Provide at least five lines of context around git diffs and treat results as leads to confirm with tests and expert review, not as a CI merge gate.
  • Store GEMINI_API_KEY in your platform's secret manager rather than a local .env file, and test any generated Kubernetes manifests in staging first.

What's inside

  • SKILL.md

Redistributed unmodified from sickn33/agentic-awesome-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.