AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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.
npx skills add sickn33/agentic-awesome-skills --skill aegisops-aigit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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/sickn33/agentic-awesome-skills/aegisops-ai)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/aegisops-ai"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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.
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/aegisops-ai"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/aegisops-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 91 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00041 | $0.01254 |
| Opus 5 | $0.00020 | $0.00627 |
| Sonnet 5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00125 |
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 11d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- aegisops-ai — 100% identical, 0 lines differ
- aegisops-ai — 100% identical, 2 lines differ
- aegisops-ai — 100% identical, 0 lines differ
- aegisops-ai — 100% identical, 0 lines differ
- aegisops-ai — 100% identical, 2 lines differ
- aegisops-ai — 100% identical, 2 lines differ
- aegisops-ai — 100% identical, 0 lines differ
- aegisops-ai — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 129 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:
- Identifying logic-based vulnerabilities (UAF, Stale State) in Linux Kernel patches.
- Detecting massive "Silent Disaster" cost drifts in Terraform plans.
- 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 planoutputs 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 applyorkubectl apply. - Post-Mortem Analysis: For analyzing why a previous AI session failed, use
/analyze-projectinstead.
🤖 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
securityContextconfigurations.
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.
- 11d ago First seen · 129 lines · 41 tokens per session scan A 3d34873e5ee0
aegisops-ai is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,230 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 1,254 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
kubernetes-operator
Deploy and manage applications on Kubernetes. Covers deployments, services, ingress, HPA, secrets, and production-grade cluster configuration.
terraform-builder
Build infrastructure as code with Terraform. Covers module design, state management, AWS/GCP/Azure resources, and production-grade practices.
data-pipeline
Professional Data Pipeline Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
auto-scaler
Professional Auto Scaler Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
aws-architect
Professional Aws Architect skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
azure-developer
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