Use OpenClaw to shortlist candidates and draft replies
A recruiter used OpenClaw to watch GitHub, rank resumes, and prepare WhatsApp messages for human approval.
- What they did
- They set up OpenClaw on a separate machine, such as a Mac Mini. A scheduled 6 AM browser job checked selected GitHub repositories, collected contributor data, kept only people with recent activity, and sent a Telegram update. For hiring, they placed PDF resumes and a job description into the workspace so the agent could score the files and produce a ranked CSV with short explanations. For messaging, it wrote a reply and passed it to the recruiter on WhatsApp, waiting for a yes before sending.
- How it went
- The guide puts OpenClaw’s monthly API spend at about $10–$30 for light usage and $100–$200 when used heavily. It did not include any measured sourcing performance. It also warned that LinkedIn scraping is brittle, and that setting up a clean first flow usually takes most of a day.
- Tips
- Switch on sandboxing, approval checks, and workspace limits before feeding it candidate information. Keep the final send step gated so nothing goes out until a person approves it.
Try it yourself with OpenClaw
I’m using [OpenClaw or similar agent] on [device]. At [time], scan these GitHub repos or topics: [list]. Find contributors with recent activity who match [skills, location, or other criteria], then send me a brief digest in [Telegram/email/Slack]. Next, compare the PDF resumes in [folder] with this job description: [paste JD], and return a ranked CSV with a short reason for each person. Last, draft a WhatsApp reply for [candidate or contact] using [context], but stop for my approval before any message is sent. Do not send unless I clearly say yes.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
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