OpenClaw Job Search Automation Guide — Preferences, Cron Jobs, and Filtering

A Reddit post on r/openclaw provides a practical four-step guide for using OpenClaw to automate job searching. The author emphasizes that specificity in preferences directly determines result quality, and offers example tool choices for fetching job postings.

1. Define Your Job Preferences

The quality of the search depends on how clearly you specify must-haves. The post contrasts a bad example (“I want a good job that pays well and uses AI”) with a good one that includes concrete details:

• Summary: Senior software engineer seeking fully-remote IC role with strong work-life balance and autonomy, at a stable mid-size company (ideally mission-driven in health or climate).
• Must-haves example: Fully remote or hybrid requiring ≤1 day onsite/week within commuting distance of Oakland, CA. Base salary ≥ $185,000.

Tip: Ask OpenClaw to interview you and help flesh out these preferences. The post mentions an example prompt is available in the comments.

2. Create a Cron Job Spec

The spec includes operational instructions (how to get postings, how often to run, which model to use) and analysis/filtering rules based on the preference spec from step 1.

Three categories of tools are covered:

• Browser automation (playwright-cli, agent-browser): open source, but slow, token-heavy, and brittle if target sites block or change layout.
• Ad-hoc scraping (Tavily, Exa): more resilient to page changes, but can't interact with on-page filters (salary, location).
• Structured API (agent-data): dedicated job postings endpoint. Currently limited to LinkedIn Jobs. Note: the author is a co-founder of agent-data and discloses this.

3. Validate the Workflow

Run the entire workflow manually to verify results match expectations. Use OpenClaw to iteratively refine filters as needed.

4. Create the Cron Job

Have OpenClaw generate the cron job, then run at least one additional test using the cron to confirm everything works in production.