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/skill-review/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/skill-review)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/skill-review"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/skill-review/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/skill-review"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/skill-review.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 | $0.00017 | $0.00261 |
| Opus 5 | $0.00009 | $0.00130 |
| Sonnet 5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
skill-review 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.
What it actually says
Task
Review agent skill definitions (SKILL.md files) to enforce maximum token efficiency, eliminate conversational fluff, and ensure standardized formatting without losing functional fidelity.
Checks
1. Structure & Headers
- Standardization: Require a top-level
# Tasksection and# Checks(or# Workflow) section. - Header Conciseness: Flag overly verbose headers (e.g.,
## Focus Areas & Deterministic Checks). Require terse headers.
2. Prose & Verbosity
- Persona Elimination: Flag conversational AI persona setups (e.g., "You are an expert..."). Replace with imperative tasks.
- Conversational Fluff: Flag transitional phrases, unnecessary adverbs, and filler words.
- Imperative Voice: Require bullet points to start with strong, punchy verbs (
Flag...,Require...,Ensure...).
3. Formatting
- Density: Ensure rules are condensed into terse, single-sentence commands where possible.
- Fidelity: Ensure no deterministic rules, checks, or domain knowledge are lost during token optimization.
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 · 21 lines · 17 tokens per session scan A 603233d7c83f
skill-review is a skill published in the GitHub repository gke-labs/kube-agents (53 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 261 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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