ConnectOnion is an open-source, template-first toolkit for building, debugging, deploying, and operating AI agents. Developers use its command-line tools and Python runtime to create agents, add tools, connect services, deploy them, and make them callable by other agents, while the catalogue entries are related agents, skills, and instructions.
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 openonion/connectonion --skill review-prgit clone --depth 1 https://github.com/openonion/connectonionWrote 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/openonion/connectonion/review-pr)<a href="https://agentmods.dev/skills/openonion/connectonion/review-pr"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/review-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/openonion/connectonion/review-pr"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00027 | $0.00276 |
| Opus 5 | $0.00014 | $0.00138 |
| Sonnet 5 | $0.00005 | $0.00055 |
| Haiku 4.5 | $0.00003 | $0.00028 |
Grade A, and why
review-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 9d 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
PR Review Skill
Review a GitHub pull request for code quality, correctness, and best practices.
Instructions
-
Get PR information:
- If no PR number is given, list open PRs.
- With a PR number, read its metadata, checks, discussion, and complete diff.
-
Analyze the changes:
- What does the PR do?
- Does the code follow project conventions?
- Are there correctness, error-handling, compatibility, or security bugs?
- Do tests cover the important failure modes?
-
Provide an evidence-based review:
- Put concrete findings first, ordered by severity.
- Include file and line references.
- Distinguish blockers from optional suggestions.
- Approve only after tests and CI support the conclusion.
Review Checklist
- Code correctness
- Project conventions
- Error handling
- Compatibility and performance
- Test coverage
- Security considerations
- Documentation impact
Reading or submitting a GitHub review must use the agent's normal approval and authentication policy; this skill grants no shell or network permissions.
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.
- 9d ago First seen · 40 lines · 27 tokens per session scan A 1a45fad07d68
review-pr is a skill published in the GitHub repository openonion/connectonion (1,480 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 276 once invoked, about $0.0001 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
review
Review code changes, pull requests, patches, or a scoped code area for actionable correctness, security, compatibility, and test risks with file and line evidence. Use for review or audit requests; do not use for general proofreading, feature implementation, or debugging a reported failure when the user wants a fix.
debug
Diagnose a concrete failure, regression, crash, hang, flaky test, or incorrect runtime behavior by reproducing it, testing hypotheses, and identifying the evidence-backed root cause. Use when symptoms or failing output exist; do not use for feature implementation without a failure, general code review, or a conceptual…
simplify
Reduce unnecessary code complexity, duplication, indirection, or abstraction while preserving observable behavior and validating equivalence. Use for simplify, cleanup, or behavior-preserving refactor requests; do not use for adding features, diagnosing an unexplained failure, broad code review without a…
Codex
Guides operator-owned delegation to Codex when the task fits a bounded coding lane, review pass, or worktree-isolated implementation track.
OpenCode
Frames OpenCode as an operator-controlled coding lane for bounded implementation or review work inside a real repository.
Himalaya Email
Work with IMAP email from the terminal using Himalaya for inbox reads, draft review, and carefully confirmed outbound sends.