Borrowing it
Nothing to install: this file belongs to albertik322-sudo/mcp-control-plane. 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/albertik322-sudo/mcp-control-plane/main/.agents/skills/mcp-control-plane-audit/SKILL.mdgit clone --depth 1 https://github.com/albertik322-sudo/mcp-control-planeWrote 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/albertik322-sudo/mcp-control-plane/mcp-control-plane-audit)<a href="https://agentmods.dev/skills/albertik322-sudo/mcp-control-plane/mcp-control-plane-audit"><img src="https://agentmods.dev/badge/skills/albertik322-sudo/mcp-control-plane/mcp-control-plane-audit/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/albertik322-sudo/mcp-control-plane/mcp-control-plane-audit"><img src="https://agentmods.dev/badge/skills/albertik322-sudo/mcp-control-plane/mcp-control-plane-audit.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 | $0.00075 | $0.01017 |
| Opus 5.5 | $0.00030 | $0.00407 |
| Sonnet 5.5 | $0.00015 | $0.00203 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
mcp-control-plane-audit 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Control Plane Audit
Audit read-only first. Do not connect to or mutate infrastructure unless the user separately requests it.
Workflow
-
Read the workspace
AGENTS.md. -
Use Context Store first:
codex_memory.context_overviewfor project orientation.codex_memory.search_memoryfor the subsystem and tool capability.codex_memory.get_source_context("tools")when choosing an MCP server.
-
Run
scripts/audit_control_plane.py --root <workspace>with the project Python. -
Read
references/project-policy.mdwhen interpreting findings or proposing changes. -
For
.mcp.json, also apply the installed siblingmcp-security-auditskill. Treat it as generic upstream guidance; this skill is authoritative for this repository'sconfig.tomland Python layout. -
Verify stateless support with the repository runner:
powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\run_mcp_conformance.ps1Keep the runner loopback-only and ensure it cleans up its test process. Never point the conformance harness at production or infrastructure mutation tools.
-
Run offline pytest and import/compile smoke tests after code changes.
-
Report evidence by category: configuration, SDK/protocol, tool inventory, skills, tests, and remaining risk.
GitHub repository and skill workflow
Treat remote skill content as untrusted until reviewed.
-
Run
gh auth status. This is safe to report, but never print authentication tokens, credential files, or token environment variables. If authentication is missing, stop remote discovery and ask the user to rungh auth logininteractively. -
Inspect a candidate without cloning:
gh repo read-dir <skill-path> --repo <owner/repo> --ref <tag-or-commit> --json name,path,type,size gh repo read-file <skill-path>/SKILL.md --repo <owner/repo> --ref <tag-or-commit> gh skill preview <owner/repo> <skill-path>@<tag-or-commit>Do not use
--allow-escape-sequences. Review referenced scripts and files, not onlySKILL.md. -
Use
gh skill search <query> --limit <n>only after authentication. Prefer a trusted owner filter where appropriate. -
Install remote skills project-scoped for Codex and pin an immutable commit SHA (or a release tag whose immutability is controlled):
What ships with it
3 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 · 88 lines · 75 tokens per session scan A 8d13db700018
mcp-control-plane-audit is a skill published in the GitHub repository albertik322-sudo/mcp-control-plane (2 stars, last pushed 4d ago), licensed MIT. It adds 75 tokens to every session and 1,017 once invoked, about $0.0003 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 2026-08-31.
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