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 skill-verifygit 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/skill-verify)<a href="https://agentmods.dev/skills/openonion/connectonion/skill-verify"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/skill-verify/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/skill-verify"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/skill-verify.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 | $0.00016 | $0.01634 |
| Opus 5.5 | $0.00006 | $0.00654 |
| Sonnet 5.5 | $0.00003 | $0.00327 |
| Haiku 4.5 | $0.00002 | $0.00163 |
Grade A, and why
Skill: Verify skill 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: verify description: Verify that a code change actually does what it's supposed to by running the app and observing behavior. Use when asked to verify a PR, confirm a fix works, test a change manually, check that a feature works, or validate local changes before pushing.
Verification is runtime observation. You build the app, run it, drive it to where the changed code executes, and capture what you see. That capture is your evidence. Nothing else is.
Don't run tests. Don't typecheck. CI ran both before you got here — green checks on the PR mean they passed. Running them again proves you can run CI. Not as a warm-up, not "just to be sure," not as a regression sweep after. The time goes to running the app instead.
Don't import-and-call. import { foo } from './src/...' then
console.log(foo(x)) is a unit test you wrote. The function did what
the function does — you knew that from reading it. The app never ran.
Whatever calls foo in the real codebase ends at a CLI, a socket, or
a window. Go there.
Find the change
Establish the full range first — a branch may be many commits:
git log --oneline @{u}.. # count commits
git diff @{u}.. --stat # full range, not HEAD~1
gh pr diff # if in a PR context
State the commit count in your report. Large diff truncating? Redirect:
git diff @{u}.. > /tmp/d then Read it. No diff at all → say so, stop.
The diff is ground truth. The PR description is a claim about it. Read both. If they disagree, that's a finding.
Surface
The surface is where a user — human or programmatic — meets the change. That's where you observe.
| Change reaches | Surface | You |
|---|---|---|
| CLI / TUI | terminal | type the command, capture the pane — example |
| Server / API | socket | send the request, capture the response — example |
| GUI | pixels | drive it under xvfb/Playwright, screenshot |
| Library | package boundary | sample code through the public export — import pkg, not import ./src/... |
| Prompt / agent config | the agent | run the agent, capture its behavior |
| CI workflow | Actions | dispatch it, read the run |
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 · 168 lines · 16 tokens per session scan A 103d2b602305
Skill: Verify skill is a skill published in the GitHub repository openonion/connectonion (1,500 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 1,634 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
benchmark-design
Design and calibrate a multi-Case capability Benchmark and establish a traceable Formal Baseline, or import a published Benchmark package.
agent-evaluation
Run one specified Test Agent on one specified Benchmark Case exactly once, privately score that execution, and return one protocol result.
agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
verify-changes
Verify code changes by running the project's typecheck, build, lint, and targeted tests, then fix and re-run until clean. Use after editing any source file.
install-verification
Use when verifying that a generated agent package can be installed, discovered by runtimes, and checked without private dependencies.
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.