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.
git clone --depth 1 https://github.com/xvirobotics/metaskillWrote 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/mcp/xvirobotics/metaskill/data-science-memory)<a href="https://agentmods.dev/mcp/xvirobotics/metaskill/data-science-memory"><img src="https://agentmods.dev/badge/mcp/xvirobotics/metaskill/data-science-memory/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/mcp/xvirobotics/metaskill/data-science-memory"><img src="https://agentmods.dev/badge/mcp/xvirobotics/metaskill/data-science-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
memory 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
{
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
],
"env": {}
}
}What else .mcp.json configures
This page is one entry in a file that holds 3. Installing the file brings all of them; each is measured and scanned on its own page.
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 · 10 lines scan A 6d82d6536304
memory is an MCP server published in the GitHub repository xvirobotics/metaskill (73 stars, last pushed 7mo ago), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. 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.
Other mcp servers, from other repositories
memory
Gives the agent a persistent knowledge graph it can write facts to and recall across sessions. Runs locally from the @modelcontextprotocol/server-memory npm package.
suede-skills-mcp
Read-only access to 71 Suede skills: discovery, install options, SEO audits, A-F grading. Runs locally from the suede-skills-mcp npm package.
context7
Gives the agent up-to-date documentation and code examples for libraries and frameworks, fetched on demand so answers are not stuck at the model's training cutoff. Runs locally from the @upstash/context7-mcp npm package.
sequential-thinking
Adds a structured step-by-step reasoning tool the agent can call to break a problem into revisable thoughts. Runs locally from the @modelcontextprotocol/server-sequential-thinking npm package.
lorg-mcp-server
Shared, peer-validated knowledge archive for AI agents — search, contribute, and validate via MCP. Runs locally from the lorg-mcp-server npm package. Needs 1 environment variable to run.
swarm-rd-orchestrator
Ray-native context/memory sharing for parallel research agents, with an agent-native CLI and MCP server (Milestone 1 spike). Runs locally from the swarm-rd-orchestrator-cli Python package.