Omnara is an open-source platform for running managed AI agents, handling their execution and state while developers choose the models, tools, machines, and user experience. Developers use it to build internal or customer-facing agent applications, while teams can interact with agents through its dashboard or Slack connector. The catalogue skill supports workflows built around Omnara.
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 omnara-ai/omnara --skill linkedin-signal-agentgit clone --depth 1 https://github.com/omnara-ai/omnaraWrote 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/omnara-ai/omnara/linkedin-signal-agent)<a href="https://agentmods.dev/skills/omnara-ai/omnara/linkedin-signal-agent"><img src="https://agentmods.dev/badge/skills/omnara-ai/omnara/linkedin-signal-agent/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/omnara-ai/omnara/linkedin-signal-agent"><img src="https://agentmods.dev/badge/skills/omnara-ai/omnara/linkedin-signal-agent.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 | $0.00063 | $0.02617 |
| Opus 5.5 | $0.00025 | $0.01047 |
| Sonnet 5.5 | $0.00013 | $0.00523 |
| Haiku 4.5 | $0.00006 | $0.00262 |
Grade A, and why
linkedin-signal-agent 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 3d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy the LinkedIn Signal Agent
You are setting up an Omnara agent for the user from agent.yaml in this
folder. The goal: an agent profile the user has watched run once, reachable
wherever the user wants it, and optionally running on a daily schedule.
The steps below are the usual path, with the exact commands. Skip anything that's already done (for example, the token and profile exist and the user only wants new queries), and follow the user's lead if they want a different order.
- Start from
agent.yamlas written. If the user wants something different (the instruction, tools, model, or how they reach the agent), change it and tell them what you changed. - If a command fails,
npx omnara <command> --helpand docs.omnara.com have the details. - Stop only where a step says to ask the user. Ask in a sentence or two of plain language, together with the next step's question if one comes right after, then end your turn and wait for the reply.
- Where a step gives a default, use it without asking and say in a line what you picked, so the user can change it.
- When a step needs a token, the user can paste it in the chat or add it to a
.envfile in the current directory, whichever they prefer; offer both. If they paste it, write it to.envyourself so the commands work as written.
The commands use the Omnara CLI (npx omnara); add --json to read IDs.
They're a reference, not a requirement: the Omnara MCP tools, the
REST API, and the SDK work
too, and the flags map directly to API fields.
1. Connect to Omnara
Run npx omnara whoami. If you aren't logged in, run npx omnara login without
piping its output, and keep it running: it waits until the user approves. It
opens the approval page in the user's browser and prints the page's link and a
code. Show the user both right away, since you may be on a different machine
than their browser, and ask them to approve once the code matches. If the
request expires, run it again. If login says the account has no organization
yet, have the user create one at the link it prints. Then npx omnara whoami
should succeed.
What ships with it
2 files 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.
- 3d ago Changed · -133 lines 989d6b89f9f3
- 8d ago Changed · +98 lines 95af27309b7b
- 9d ago Changed · +3 lines 7c455bf89f52
- 14d ago First seen · 241 lines · 63 tokens per session scan A 55ddbdeb1fc2
linkedin-signal-agent is a skill published in the GitHub repository omnara-ai/omnara (2,928 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 2,617 once invoked, about $0.0003 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 2026-10-03.
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