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-adversarial/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-adversarial)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/review-adversarial"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-adversarial/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-adversarial"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-adversarial.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 | $0.00045 | $0.04348 |
| Opus 5 | $0.00023 | $0.02174 |
| Sonnet 5 | $0.00009 | $0.00870 |
| Haiku 4.5 | $0.00005 | $0.00435 |
Grade A, and why
review-adversarial 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task
Given a diff range, find the defects in it and report only the ones you can defend. This skill is
the review method itself and holds no plumbing: it is run by an author against their own branch
before opening a pull request (the AGENTS.md requirement, wrapped by review-preflight), and by a
reviewer against a pull request already open (.claude/commands/pr-review-batch.md, which wraps it
in the GitHub-side work).
review-docs-drift is the companion pass, not part of this one. Angle H stops at the rules the
diff visibly breaks and leaves the rest of the documentation question to that skill.
Procedure
1. Run this in a context that did not write the change
If you are the agent that just produced the diff, do not run the pass in the conversation that
produced it. review-preflight is how you get a context that did
not: it owns the plumbing, down to what to hand the fresh context, what to withhold from it, and
what to do when your harness will not spawn one for the asking. A model reviewing work it has just justified is the weakest
configuration there is: it is poorly calibrated about its own output, rates it higher than an
outsider would, and the bias is worst on exactly the lines it got wrong. It is also more likely to
"fix" something correct than to catch something broken, which is why step 5 exists and why step 6
will not let you edit on a hunch.
That is why both wrappers spend a subagent on it —
.claude/commands/pr-review-batch.md for a pull
request already open,
.claude/commands/pr-preflight.md for the author's own
branch.
Without a wrapper, the minimum is: hand the fresh context the repository, the diff range, and this file, and withhold your plan, your reasoning, and the intent sentence step 3 asks it to derive for itself. The gap between what you meant and what the diff says is the finding you cannot get any other way.
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.
- 8d ago Changed · +3 lines 1b841ea13812
- 11d ago First seen · 300 lines · 45 tokens per session scan A 53c414f24eb3
review-adversarial is a skill published in the GitHub repository gke-labs/kube-agents (53 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 4,348 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.
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