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 rem-initgit 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/rem-init)<a href="https://agentmods.dev/skills/openonion/connectonion/rem-init"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-init/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/rem-init"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-init.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 | $0.00022 | $0.00312 |
| Opus 5.5 | $0.00009 | $0.00125 |
| Sonnet 5.5 | $0.00004 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
rem-init 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 8d 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
Initialize REM
Use the notebook root supplied by the task in every command. Read CLI.md
for source CLI syntax and recovery; resolve it beside this Skill. co rem --help
and co rem init --help are the command contract. Do not recreate the command's
mapping or investigation logic in a long prompt.
Run co rem --root '<absolute-notebook-root>' init --days 180. It maps connected
sources, saves their private evidence, then uses the configured runner to write
the owner's page and investigate eligible people, queued projects, related
organizations and installed skills, recent first. It announces the model-work
estimate. The configured weekly investigation budget (35% by default) is a
target, not a stopping point. Finish the selected investigation even when
it takes more, unless the configured
weekly safety floor, runner failure, or interruption stops it. Do not pass
--no-investigate for a normal first run.
Read the result and co rem --root '<absolute-notebook-root>' logs. Check that
the owner's page and selected pages have useful findings and citations, not
only empty frames. Follow the returned Next: command and retry failed pages
with co rem investigate or co rem projects write. Report source gaps and
unfinished pages. Never copy private source bodies into a public document.
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.
- 8d ago Changed 13b18d009bfd
- 13d ago Changed 28c6662dde92
- 14d ago Changed · +1 lines · -5 tokens per session f2ef9e8dbaea
- 15d ago Changed · -31 lines · -19 tokens per session 575bfc880895
- 17d ago First seen · 58 lines · 46 tokens per session scan A 9354e992a625
rem-init is a skill published in the GitHub repository openonion/connectonion (1,497 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 312 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 2026-09-30.
Other skills, from other repositories
awm-rsi-toolkit
Run AWM (Agent Workflow Memory, Wang et al. 2024) on an agent - induce reusable workflows from its successful trajectories, online over a training stream or offline from supplied experiences, then freeze the workflow memory and measure it on a published Benchmark.
docx
Create, read, edit, review, redline, comment on, merge, audit, render, and verify .docx Word documents, including layout-sensitive and OOXML-level work.
update-setup
A one-time setup wizard for creating a personalised upgrade guide for a workspace. It checks for an existing guide, identifies the current version and installation clues, and requires confirmation of the installation method before writing a new guide.
skill-creator
Create, edit, improve, tidy, review, audit, or restructure memmy-agent skills and SKILL.md files.
computer-history
Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.
memmy-memory
Use the shared Memmy memory service to retrieve relevant prior context and persist durable Agent turns, facts, decisions, preferences, procedures, and follow-ups.