Remote Jobs Finderv1.6.2

A user initiates a job search by asking for remote roles, triggering a conversational onboarding process. The agent asks about job titles, location, preferences, and optionally requests a resume for personalized ranking, storing these details for future searches.

When the user lacks a valid Remote Rocketship API key, the agent prompts them to retrieve it from their account and paste it in chat. This natural language approach avoids technical commands, ensuring seamless integration for fetching job listings.

After an initial job fetch, the user requests more results (e.g., 'send me 20 more'). The agent increments the page count, uses cached filters, and displays the next batch of jobs, maintaining state to track total counts and pagination limits.

Following a search, the agent offers to set up periodic checks (e.g., hourly or daily) for new matching jobs. If agreed, it creates a cron job to rerun searches and notifies the user only when new opportunities are found, with deduplication.

A user refines their search criteria mid-conversation (e.g., switching to contract roles). The agent updates the stored user profile in memory, ensuring subsequent searches reflect the new preferences without requiring re-onboarding.

Remote Rocketship provides curated job feeds via a paid subscription, with users generating API keys for access. This model ensures revenue through recurring payments while limiting usage via quotas (e.g., 1,000 requests/day).

The skill could offer basic job searches for free, with advanced features like resume-based ranking or higher request limits as paid upgrades. This attracts a broad user base while monetizing power users seeking enhanced matching.

By embedding this skill into broader AI platforms or recruitment tools, Remote Rocketship can earn through partnership fees or revenue sharing. This expands reach to users who prefer conversational interfaces over traditional dashboards.