aegisops-ai

aegisops-ai is a skill for Claude Code, Codex from marysatasselshaped667/skills-collection-1. It costs 42 tokens per session (1,190 once invoked), scanned A, a copy of aegisops-ai, MIT.

A DevSecOps and FinOps skill for reviewing Linux kernel patches, Terraform infrastructure cost changes, and Kubernetes security settings. DevSecOps combines development, security, and operations; FinOps focuses on controlling cloud spending.

In plain words
What is it for?
Use it to audit C-based kernel changes, review Terraform plan output before applying it, create least-privilege Kubernetes settings, and gate CI/CD merges with GitHub Actions.
Why use it?
It helps find certain memory-safety problems, unexpected infrastructure cost increases, and Kubernetes compliance issues before they reach production.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to audit C-based kernel changes, review Terraform plan output before applying it, create least-privilege Kubernetes settings, and gate CI/CD merges with GitHub Actions.

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Install with agentmods
npx agentmods add skills/marysatasselshaped667/skills-collection-1/aegisops-ai
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add marysatasselshaped667/skills-collection-1 --skill aegisops-ai
Clone the repo
git clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for aegisops-ai

README.md
[![agentmods](https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/aegisops-ai/github.svg)](https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/aegisops-ai)
Your own site
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/aegisops-ai"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/aegisops-ai/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.

agentmods 80×15 button for aegisops-ai

Your own site · 80×15
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/aegisops-ai"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/aegisops-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,190 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01190
Opus 5 $0.00021 $0.00595
Sonnet 5 $0.00008 $0.00238
Haiku 4.5 $0.00004 $0.00119

Measured 12d ago against content hash 0cd36d7b34d5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

aegisops-ai scanned grade A with 0 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 12d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to aegisops-ai — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

SKILLS/aegisops-ai/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/aegisops-ai — Autonomous Governance Orchestrator

AegisOps-AI is a professional-grade "Living Pipeline" that integrates advanced AI reasoning directly into the SDLC. It acts as an intelligent gatekeeper for systems-level security, cloud infrastructure costs, and Kubernetes compliance.

Goal

To automate high-stakes security and financial audits by:

  1. Identifying logic-based vulnerabilities (UAF, Stale State) in Linux Kernel patches.
  2. Detecting massive "Silent Disaster" cost drifts in Terraform plans.
  3. Translating natural language security intent into hardened K8s manifests.

When to Use

  • Kernel Patch Review: Auditing raw C-based Git diffs for memory safety.
  • Pre-Apply IaC Audit: Analyzing terraform plan outputs to prevent bill spikes.
  • Cluster Hardening: Generating "Least Privilege" securityContexts for deployments.
  • CI/CD Quality Gating: Blocking non-compliant merges via GitHub Actions.

When Not to Use

  • Web App Logic: Do not use for standard web vulnerabilities (XSS, SQLi); use dedicated SAST scanners.
  • Non-C Memory Analysis: The patch analyzer is optimized for C-logic; avoid using it for high-level languages like Python or JS.
  • Direct Resource Mutation: This is an auditor, not a deployment tool. It does not execute terraform apply or kubectl apply.
  • Post-Mortem Analysis: For analyzing why a previous AI session failed, use /analyze-project instead.

🤖 Generative AI Integration

AegisOps-AI leverages the Google GenAI SDK to implement a "Reasoning Path" for autonomous security and financial audits:

  • Neural Patch Analysis: Performs semantic code reviews of Linux Kernel patches, moving beyond simple pattern matching to understand complex memory state logic.
  • Intelligent Cost Synthesis: Processes raw Terraform plan diffs through a financial reasoning model to detect high-risk resource escalations and "silent" fiscal drifts.
  • Natural Language Policy Mapping: Translates human security intent into syntactically correct, hardened Kubernetes securityContext configurations.

Read the full file on GitHub · 128 lines

Changes

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.

  1. 12d ago First seen · 128 lines · 42 tokens per session scan A 0cd36d7b34d5

Subscribe to this mod's changes

aegisops-ai is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 1,190 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to aegisops-ai, differing in 11 lines, and is treated as a copy.