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 pinkpixel-dev/skills-collection-1 --skill aegisops-aigit clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1Wrote 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/pinkpixel-dev/skills-collection-1/aegisops-ai)<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/aegisops-ai"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/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.
<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/aegisops-ai"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/aegisops-ai.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 | $0.00042 | $0.01190 |
| Opus 5 | $0.00021 | $0.00595 |
| Sonnet 5 | $0.00008 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
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.
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.
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:
- 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.
- 12d ago First seen · 128 lines · 42 tokens per session scan A 0cd36d7b34d5
aegisops-ai is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (7 stars, last pushed 1mo ago), licensed Apache-2.0. 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.
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