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-enrichgit 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-enrich)<a href="https://agentmods.dev/skills/openonion/connectonion/rem-enrich"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-enrich/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-enrich"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-enrich.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 | $0.00031 | $0.01378 |
| Opus 5.5 | $0.00012 | $0.00551 |
| Sonnet 5.5 | $0.00006 | $0.00276 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
rem-enrich 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 today.
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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrich one page
Input: a written page, its reading ledger and search coverage.
Output: the page with new cited facts, and a lead list in your final reply.
Follow the page kind's rem-page-* rules and rem-investigate's quote and
evidence rules unchanged.
Enrich is discovery. The page was written from what init happened to read; your job is to find what else exists and where it points. Finding a direction to investigate is worth more than polishing a sentence.
1. Find the leads
Read every cited original whole, then list what it points to that the page has not followed. A lead is anything that could hold more:
- A link: a website, LinkedIn or GitHub profile, a shared document, a repository, a calendar or booking page, a product or company site. Open it.
- An attachment not yet read: a contract, invoice, CV, deck, screenshot.
- A name: a person, company, project, product or place mentioned but without a page or not linked from this one.
- A thread whose start or end the page does not have:
Re:with no original, a request with no reply, a reply quoting earlier mail. - An older period: first contact earlier than the page's First contact; mail before the window init read.
- Another channel: the same person in sessions, GitHub issues, WhatsApp,
another mailbox or address. The subject's agent mailboxes and aliases
(
<name>@mail.openonion.ai,0x…@mail.openonion.ai, a relay or handoff address) are their channels too: search mail from and to each one as you would their own address. - A number or date that implies another record: an invoice number, an order, a meeting, a deadline someone must have acted on.
- The reading ledger itself: every kind of source it lists that the page cites none of (sessions, GitHub items, attachments, another address) is a lead; open at least the ones about this subject in each kind.
2. Follow them
Most promising first: what could change the lead, Open threads or Facts.
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.
- today First seen · 106 lines · 31 tokens per session scan A cabb3e97e4d3
rem-enrich is a skill published in the GitHub repository openonion/connectonion (1,497 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 1,378 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 2026-10-17.
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