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 2233admin/reverse-skill-evolver --skill competition-k8s-control-planegit clone --depth 1 https://github.com/2233admin/reverse-skill-evolverWrote 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/2233admin/reverse-skill-evolver/competition-k8s-control-plane)<a href="https://agentmods.dev/skills/2233admin/reverse-skill-evolver/competition-k8s-control-plane"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-k8s-control-plane/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/2233admin/reverse-skill-evolver/competition-k8s-control-plane"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-k8s-control-plane.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 | $0.00117 | $0.00677 |
| Opus 5.5 | $0.00047 | $0.00271 |
| Sonnet 5.5 | $0.00023 | $0.00135 |
| Haiku 4.5 | $0.00012 | $0.00068 |
Grade A, and why
competition-k8s-control-plane 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.
This is a copy
100% identical to competition-k8s-control-plane — 0 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition K8s Control Plane
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the decisive path runs through Kubernetes control-plane state, API permissions, or controller behavior rather than a single container's runtime alone.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Separate manifest intent from live cluster state: API objects, mutations, controllers, secrets, and resulting workloads.
- Identify the active principal first: service account, kubeconfig identity, node credential, webhook, or controller.
- Map the smallest control-plane edge to its workload effect.
- Keep RBAC, service accounts, owner references, namespace boundaries, and secret consumers in compact evidence blocks.
- Reproduce the smallest cluster action that yields the decisive workload or secret effect.
Workflow
1. Map The API Trust Path
- Record namespaces, service accounts, Roles, ClusterRoles, bindings, admission hooks, controllers, and the resources they can mutate.
- Distinguish read access, create access, patch access, exec access, and secret access.
- Keep principal, verb, resource, namespace, and resulting object in one chain.
2. Trace Mutation To Workload State
- Show how an API action becomes a pod, volume mount, secret exposure, env injection, job run, or controller-created artifact.
- Compare checked-in YAML against live objects after defaulting, admission mutation, or controller reconciliation.
- Distinguish pod-runtime behavior from cluster-level mutation logic.
3. Reduce To The Decisive Cluster Path
- Compress the result to the smallest chain: principal -> API permission -> mutated object -> resulting workload, secret, or route effect.
- Keep kube objects, live describes, and consumed secret or config paths tied to the same namespace and controller.
- If the problem narrows down to one container's mount or runtime deviation, switch back to the tighter container-runtime skill.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 52 lines · 117 tokens per session scan A 18fa637d9926
competition-k8s-control-plane is a skill published in the GitHub repository 2233admin/reverse-skill-evolver (23 stars, last pushed today), licensed MIT. It adds 117 tokens to every session and 677 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to competition-k8s-control-plane, differing in 0 lines, and is treated as a copy.
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