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 | $0.00405 |
| Opus 5.5 | $0.00007 | $0.00162 |
| Sonnet 5.5 | $0.00003 | $0.00081 |
| Haiku 4.5 | $0.00002 | $0.00040 |
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 1mo 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. Frontmatter
- Discovery: A harness loads
nameanddescriptionat session start and the body only once the description matches the request, so the description is what decides whether the skill is ever read. Require it to open with the action the user asks for and to name the thing acted on; flag one that describes the skill to a reader who already found it (Discovers and removes provisioned kube-agents GCP/GKE infrastructurenever says "uninstall"). - Loadability: Require frontmatter a YAML parser accepts. Flag an unquoted
:in a description, and a value opening with{,[,*or&.
2. 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.
3. 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...).
4. 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.
- 1mo ago Changed · +11 lines b277de477ac9
- 1mo 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 (116 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 405 once invoked, about $0.0001 per session on Opus 5.5. 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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autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
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revdiff-plan
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re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…