Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/getcargohq/cargo-skillsnpx agentmods add skills/getcargohq/cargo-skills/cargo-mcpWrote 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/getcargohq/cargo-skills/cargo-mcp)<a href="https://agentmods.dev/skills/getcargohq/cargo-skills/cargo-mcp"><img src="https://agentmods.dev/badge/skills/getcargohq/cargo-skills/cargo-mcp/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/getcargohq/cargo-skills/cargo-mcp"><img src="https://agentmods.dev/badge/skills/getcargohq/cargo-skills/cargo-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00212 | $0.02303 |
| Opus 5 | $0.00106 | $0.01151 |
| Sonnet 5 | $0.00042 | $0.00461 |
| Haiku 4.5 | $0.00021 | $0.00230 |
Grade A, and why
cargo-mcp 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 7d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cargo — the hosted MCP server
Cargo has two surfaces. The rest of this bundle documents the CLI. This one
documents https://mcp.getcargo.io/mcp, and, more usefully, when to reach for
which.
Three different things here are called MCP. This skill is the hosted server Cargo runs, which you point a client at. Publishing a curated server out of your own workspace (
ai mcp-server create, thencargo-ai mcpover stdio) and attaching somebody else's server to a Cargo agent (release update-draft --mcp-clients) are bothcargo-ai. Check which one the user means before answering: the words are identical and the answers share nothing.
Which surface
| The job | Surface |
|---|---|
| Run one action, or one action over many records | either; MCP if it is already connected |
| Find what Cargo can do, and what it costs | either (search_actions is the MCP half) |
| Read records off a model | either |
| Warehouse SQL, aggregates, joins | CLI (cargo-storage) |
| Build or edit a multi-step workflow, tool, or play | CLI (cargo-orchestration) |
| Workspace as code, plan and deploy | CLI (cargo-cdk) |
| Provision mailboxes, warm up, send | CLI (cargo-mailbox-management) |
| Segments, connectors, content libraries, alerts, hosting, billing admin | CLI |
| No shell at all (ChatGPT, Claude Desktop, claude.ai, n8n) | MCP, and say plainly what is out of reach |
The rule underneath the table: MCP is the runtime, the CLI is the platform. Thirteen tools cover discovering an action, running it, watching it finish, and reading data back. Everything that builds something reusable is CLI only. An agent holding both should prefer the CLI for anything the user will want to re-run or version, and MCP for one-shot execution inside a conversation.
When the job routes to the CLI, this is the whole bootstrap:
What ships with it
1 file 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.
- 7d ago Changed · +2 lines 042dbdf6e554
- 11d ago First seen · 197 lines · 212 tokens per session scan A cafca976dc1b
cargo-mcp is a skill published in the GitHub repository getcargohq/cargo-skills (17 stars, last pushed today), licensed MIT. It adds 212 tokens to every session and 2,303 once invoked, about $0.0011 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.
Other skills, from other repositories
everme-memory
Use EverMe cloud memory proactively when the user refers to previous conversations, earlier decisions, "last time", "remember when", existing project conventions, or previously solved errors, and save durable user preferences, habits, and decisions the moment they are stated. Do not repeat a search when the host…
memory-tools
Use EverMe memory proactively when the user refers to previous conversations, earlier decisions, "last time", "remember when", existing project conventions, or previously solved errors, and save durable user preferences, habits, and decisions the moment they are stated. Do not repeat a search when a non-empty block…
memory-recall
Session-start primer that tells Kimi how EverMe's automatic cross-session memory works and how to treat the injected recall/profile context.
gtm-engineering
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,'…
everme-memory
Persistent memory for Codex sessions with native lifecycle recall and save. Use EverMe proactively when the user refers to previous conversations, earlier decisions, "last time", "remember when", existing project conventions, or previously solved errors, or asks to continue prior work; save durable user preferences…
ai-cold-outreach
When the user wants to build an AI-powered outreach system, write cold emails, improve deliverability, or scale personalized outreach. Also use when the user mentions 'cold email,' 'cold outreach,' 'outreach automation,' 'Instantly,' 'Smartlead,' 'Clay,' 'email sequences,' 'deliverability,' 'personalization at scale,'…