arcade-mcp is a Python framework for creating Model Context Protocol servers and the tools they expose to AI agents. Developers use it to connect agents to internal APIs, custom authentication providers, and specialized services, with support for authorization, evaluation, and deployment. The catalogue entries provide rules, skills, and integrations for working with this framework.
Borrowing it
Nothing to install: this file belongs to ArcadeAI/arcade-mcp. 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/ArcadeAI/arcade-mcp/main/.claude/skills/open-pr/SKILL.mdgit clone --depth 1 https://github.com/ArcadeAI/arcade-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/arcadeai/arcade-mcp/open-pr)<a href="https://agentmods.dev/skills/arcadeai/arcade-mcp/open-pr"><img src="https://agentmods.dev/badge/skills/arcadeai/arcade-mcp/open-pr/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/arcadeai/arcade-mcp/open-pr"><img src="https://agentmods.dev/badge/skills/arcadeai/arcade-mcp/open-pr.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 | $0.00068 | $0.02398 |
| Opus 5.5 | $0.00027 | $0.00959 |
| Sonnet 5.5 | $0.00014 | $0.00480 |
| Haiku 4.5 | $0.00007 | $0.00240 |
Grade A, and why
open-pr 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 23d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open a PR
Deliver a focused PR with an accurate description and evidence supporting its intended behavior. The author owns the job, behavior, approach decisions, and outcome, and stays responsible for AI-assisted work; line-by-line recall is not a prerequisite.
Follow CLAUDE.md and .github/PULL_REQUEST_TEMPLATE.md. Link issues and design discussions rather than reconstructing them in the PR.
Use the GitHub and issue-tracker tools the user chose. Discover available operations before assuming a CLI, connector, or credentials exist. Keep local git operations local. If a needed remote operation is unavailable, finish local preparation and report the exact missing capability; do not silently switch tools or claim the operation succeeded.
1. Establish the actual state
- Read
CLAUDE.mdand the PR template. Inspect the working tree, staged changes, branch, upstream, remotes, and any existing PR. Confirm the destination repository and base; use the existing PR's base ormainunless the task specifies otherwise. Fetch the base before assessing the diff. - Review both the committed diff against the merge base and pending changes. Identify the intended scope, related issue, and consequential choices. Preserve unrelated work; stage explicit files or hunks. Do not reset, squash, rebase, or force-push someone else's work to tidy the PR.
- Apply PR scope discipline. Resolve an actual scope problem rather than expanding the description to explain unrelated work.
- Reuse a matching open PR. A closed or merged PR is not an update target. If the branch is detached or is the base branch, create a task branch. Resolve ambiguity that could send work to the wrong repository, base, or PR before publishing.
- Find the tracking issue (GitHub issue or Linear) in task context, branch, commits, or existing PR and verify it describes this work. If none exists, file one first (or stop and ask your human to if you cannot). Do not borrow an unrelated or closed issue.
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.
- 23d ago First seen · 83 lines · 68 tokens per session scan A 870934e85ee6
open-pr is a skill published in the GitHub repository ArcadeAI/arcade-mcp (1,064 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 2,398 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 2026-10-06.
Other skills, from other repositories
pr-analysis
Analyse a GitHub pull request by fetching its metadata, diff, linked issues and CI status.
atelier
Two-agent post-generation quality gate for vibe-coded applications. Deterministic, gradeable ruleset enforcing UI/UX design-system craft and backend architectural soundness.
code-review
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use…
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
pr
Draft a PR body from OMC's paper trail — the smallest visual that proves the change, evidence consumed from the verify protocol (never re-collected), reversibility from the ADR test on the diff, glossary language throughout.
merge-batch
Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.