omnara-ai/omnara/linkedin-signal-agent

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.yaml as 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> --help and

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

.env file in the current directory, whichever they prefer; offer both. If they paste it, write it to .env yourself 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.

Pick the org and project (npx omnara whoami --json lists orgs, npx omnara projects list --org <org-id> --json their projects): use them if there's one of each, otherwise the defaults from npx omnara config or the project named Default; ask only if there's still no clear choice. Save the choice with npx omnara config --org <org-id> --project <project-id>; later steps need the project ID.

2. Choose what to track

LinkedIn keyword search returns plenty of adjacent hype, so the agent's precision comes from knowing who the user is. Ask about their product, their audience, and what kind of post would make them want to reply, then write three values:

• TEAM_CONTEXT: one paragraph, on a single line, covering what the product

does, who it's for, its main competitors, and what a strong demand signal looks like (for example, "a team describing building this in-house"). This matters more than the queries.

• LINKEDIN_SEARCH_QUERIES: LinkedIn post searches as a JSON array on one

line. Boolean syntax works (up to 5 operators and 500 characters per query), and cost scales per query, so prefer one boolean query over several. Escape inner double quotes, for example ["\"managed agents\" OR \"agent infrastructure\" OR \"durable agents\""]. Use [] to skip keyword search.

• LINKEDIN_TARGET_URLS: company pages or profiles whose last-day posts are

scanned even without a keyword match, such as competitors and category voices, as a JSON array on one line, for example ["https://www.linkedin.com/company/langchain/"]. Use [] to skip tracked feeds.

At least one of the two lists must be nonempty. Show all three to the user (as plain text and lists, not JSON) and adjust until they're happy.

3. Store the Apify token

LinkedIn's API has no public post search, so the agent scrapes through Apify's hosted MCP server running two scrapers that need no LinkedIn account or cookie: harvestapi/linkedin-post-search and harvestapi/linkedin-profile-posts. The user needs a free Apify account (console.apify.com/sign-up, no credit card); the API token is on the API & Integrations page under Settings. Both scrapers are billed per post, and a scan is capped at 200 search posts plus 10 per tracked feed, about $0.50 at most.

If a secret named linkedin-signal-agent-apify-token already exists (npx omnara secrets list --owner-kind project --owner-project-id <project-id> --name linkedin-signal-agent-apify-token --json), reuse it and note its id; if the value changed, the user updates it on the dashboard's Secrets page (the ID stays the same). If only reddit-signal-agent-apify-token exists, it holds the same kind of Apify token; reuse it. Otherwise get APIFY_TOKEN from the user, then create the secret and note its id (sec_…):

set -a && . ./.env && set +a
npx omnara secrets create --owner-kind project --owner-project-id <project-id> \
  --name linkedin-signal-agent-apify-token --material-kind generic \
  --material-value "$APIFY_TOKEN" --json

4. Pick the model

npx omnara grant models list --json: each item has model.provider_config and model.name. Use openai/gpt-6.1-sol on omnara-openrouter if it's granted, otherwise a strong general-purpose model from the list. The choice gives MODEL_PROVIDER_CONFIG and MODEL_NAME.

This agent needs no machine pool: the Apify MCP server does all the fetching.

5. Create the agent

1. Copy agent.yaml from this folder to ./linkedin-signal-agent.yaml in

the current directory. If this folder isn't available locally, download https://raw.githubusercontent.com/omnara-ai/omnara/main/examples/linkedin-signal-agent/agent.yaml instead.

1. In the copy, replace every placeholder:

Then confirm nothing is left: grep -n '{{' linkedin-signal-agent.yaml must print nothing.

1. Check for an existing profile: npx omnara profiles list --name linkedin-signal-agent --json.

• None: npx omnara profiles create --name linkedin-signal-agent --file ./linkedin-signal-agent.yaml --json
• Exists: npx omnara profiles update <agent-profile-id> --file ./linkedin-signal-agent.yaml --json

Note the profile's id and current_config_id from the output.

6. Run a first scan

npx omnara agents launch --profile <agent-profile-id> --config <current-config-id> \
  --message "Run the LinkedIn scan now." --json

Give the user the link to watch it work: https://app.omnara.com/projects/<project-id>/agents/<agent-id>. The scrapes take a few minutes, mostly a quiet stretch of get-actor-run calls while the agent waits. The user can reply in the console to ask for reply drafts or push back on the filtering.

7. Choose where and when to use it

Right after sharing the link, ask in one message where the user wants to talk to the agent and whether it should run a daily scan (suggest weekdays at 9am in their timezone). Wait for the reply; don't assume the console or a schedule. Any combination works:

• Their own app (recommended): inside their product or internal tool, or a

small UI built for it.

• Slack or Discord: the team mentions the bot, and replies in the thread go

to the same agent.

• Only the Omnara console: nothing to set up; every agent launched from the

profile shows up there.

Set up whichever the user picks with `integrations.md` (read it from https://raw.githubusercontent.com/omnara-ai/omnara/main/examples/integrations.md if you don't have the repo). For this agent, name the integrations linkedin-signal-agent-slack and linkedin-signal-agent-discord, call the bot "LinkedIn Signal Agent", and have the user try it with "@LinkedIn Signal Agent run the scan".

If they want a daily scan, create it with --cron and --timezone from their answer:

npx omnara crons create --name linkedin-signal-agent-daily \
  --target-type profile --target-agent-profile-id <agent-profile-id> \
  --cron '0 9 * * 1-5' --timezone America/Los_Angeles \
  --message-template 'Run the daily LinkedIn scan.' --json

If npx omnara crons list --name linkedin-signal-agent-daily --json already shows a trigger, change it with npx omnara crons update <cron-trigger-id> (same flags) instead of creating a second one.

Each firing launches a fresh agent from the profile. It shows up in the console, and the user's app can pick it up through Omnara's SDK or API. To post every digest in one Slack or Discord thread instead, see "Scheduled runs in a channel" in integrations.md.

8. Wrap up

Summarize what you created: the secret, the profile, and any integrations or cron trigger. Remind the user to delete .env or move what's in it somewhere safe. Then tell them:

• Change the queries or anything else: ask a coding agent with this

skill. It reuses the secret and updates the profile in place.

• Remove it: delete the cron trigger (npx omnara crons delete <id>),

remove its integrations, if any (see "Removing" in integrations.md), then run npx omnara profiles delete <agent-profile-id> and npx omnara secrets delete <secret-id> (skip the secret if another agent shares it).