Borrowing it
Nothing to install: this file belongs to gke-labs/kube-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gke-labs/kube-agents/main/.agents/skills/review-security-k8s-agents-main/SKILL.mdgit clone --depth 1 https://github.com/gke-labs/kube-agentsWrote 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/gke-labs/kube-agents/review-security-k8s-agents-main)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/review-security-k8s-agents-main"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-security-k8s-agents-main/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/gke-labs/kube-agents/review-security-k8s-agents-main"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-security-k8s-agents-main.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00406 |
| Opus 5 | $0.00012 | $0.00203 |
| Sonnet 5 | $0.00005 | $0.00081 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
review-security-k8s-agents-main 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 13d 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.
What it actually says
Task
Coordinate AI agent security review sub-agents, gather findings, and produce a summarized JSON report.
Workflow
1. Context Ingestion
Pass project context (from review-security-k8s-understand) to sub-agents.
2. Parallel Reviews
Launch in parallel sub-agents:
review-security-k8s-agents-sandboxreview-security-k8s-agents-firewallreview-security-k8s-agents-credentialsreview-security-k8s-agents-prompt-injectionreview-security-k8s-agents-data-exfilreview-security-k8s-agents-audit-logs
CRITICAL: Instruct each to output JSON:
[{"agent": "<skill-name>", "findings": [{"message": "<desc>", "file": "<name>", "line": "<num>"}]}]
(Return empty list if no findings). Wait for completion.
3. Triage & Filtering
Evaluate the raw findings against the project context to determine actual risk. Filter out findings that are functionally required by the workload's specific role or adequately mitigated by broader architectural controls.
- Example: Filter out missing egress proxy warnings if the agent's execution sandbox is completely air-gapped and the main control loop is strictly allowlisted to a single LLM API.
- Example: Filter out root execution warnings inside the execution sandbox if the context confirms the sandbox utilizes a secure VM-based
RuntimeClass(e.g. gVisor or Kata Containers) providing a secure sandbox isolation boundary.
4. Aggregation
Merge the filtered findings into a single JSON array. Output MUST be valid JSON string (markdown blocks okay). Omit agents with no findings or return empty findings.
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.
- 13d ago First seen · 34 lines · 23 tokens per session scan A 8fb3b8cb3fd1
review-security-k8s-agents-main is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 406 once invoked, about $0.0001 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.
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