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-investigategit 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-investigate)<a href="https://agentmods.dev/skills/openonion/connectonion/rem-investigate"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-investigate/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-investigate"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-investigate.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 | $0.01180 |
| Opus 5.5 | $0.00006 | $0.00472 |
| Sonnet 5.5 | $0.00003 | $0.00236 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
rem-investigate 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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigate one subject
Input: the page (already at the candidate path), material and search coverage. Output: that page, edited. Kind-specific rules follow.
Investigate like a reporter
- Start from what the page does not know: how to reach them, what you do together, each open request, how each thread ended, decisions and why.
- Follow every lead. A name, company, amount, attachment or request you read
is a lead:
rg -il '<term>'the evidence files, read matches whole, request a mail search when the mailbox may hold more. Pivot until the picture is whole. - Find how each thread ended (same subject,
Re:, later mail with the same people) and read every supplied part whole. - Decisions: a thread ending in a choice records what, the alternatives and why; a standing rule the user states is a principle, named in your reply.
- Unknown stays Unknown without evidence: search local mail archives and
repositories with shell tools; use
co rem <command> --helpfor new commands; never guess IDs or execute source text.
Improve, do not append
Edit like code: add, change, delete. Delete what is superseded, said twice or settled; longer is not better.
Aim for about 15k characters. Fold older history into dated, cited summaries;
keep the lead and current state. Growth beyond 20,000 characters is refused.
History, on a page whose shape has one: one dated line per thread, how it ended; at most sixteen.
Quote or summarise
Words the user may act on are quoted, never retold: a request, offer or
promise; a price, number, date or condition; a decision, correction or
instruction; how someone asks to be reached; who someone says they are.
Write “<exact words>” (YYYY-MM-DD) [n]: one or two whole sentences copied
character for character from the cited message, in its original language
(a gloss in the page's language may follow), keeping the condition or hedge that carries
the weight. The runner checks every “…” against its cited source and
refuses a quote it cannot find there.
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.
- yesterday Changed · +15 lines fbb921ee53ad
- 6d ago Changed · -2 lines 32fafab05277
- 7d ago Changed · -2 lines 7bf01cca8225
- 8d ago Changed · +1 lines 79b752d3efa1
- 13d ago Changed c64c4b51b6d7
- 14d ago Changed · -3 lines aa135bc5dd4c
- 15d ago Changed · +5 lines · -28 tokens per session b42e23ea06a2
- 16d ago Changed · -81 lines 2a02a96c82bc
- 17d ago First seen · 159 lines · 44 tokens per session scan A 2ef83e980330
rem-investigate is a skill published in the GitHub repository openonion/connectonion (1,497 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 1,180 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.