Screen job listings daily against resume and salary data
A hundred roles a month and call backs on 10% of them? Task: The user employs a custom agent to review LinkedIn job listings daily, cross-referencing levels.fyi for salary and role leveling data. The agent assesses fit against the user's resume, generating 5-10 potential positions per day. Human judgment filters these down to 0-2 applications, averaging 0.5 per day, ensuring a highly targeted approach rather than mass spamming.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: The source does not specify technical prerequisites, API costs, or integration constraints for the custom agent. The workflow relies on manual human judgment for final selection, which is time-intensive. The reported metrics are anecdotal from a single user during a layoff period and may not generalize to other job markets or roles..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
A hundred roles a month and call backs on 10% of them?
Workflow
The user employs a custom agent to review LinkedIn job listings daily, cross-referencing levels.fyi for salary and role leveling data. The agent assesses fit against the user's resume, generating 5-10 potential positions per day. Human judgment filters these down to 0-2 applications, averaging 0.5 per day, ensuring a highly targeted approach rather than mass spamming.
Reported result
The user reports a callback rate of approximately 10% due to the targeted strategy. The interview pipeline is described as full, requiring significant time for preparation and participation. One specific interview with Anthropic required up to 8 hours for a take-home assignment, which the user completed fully due to high motivation and role fit.
Human role
The original post does not say how a person stayed involved.
Limitations
The source does not specify technical prerequisites, API costs, or integration constraints for the custom agent. The workflow relies on manual human judgment for final selection, which is time-intensive. The reported metrics are anecdotal from a single user during a layoff period and may not generalize to other job markets or roles.
Try it
Act as my job search assistant. I will provide my resume and target salary range. Use a custom agent or script to scrape LinkedIn for new postings matching my keywords. Cross-reference each role with levels.fyi data to estimate compensation and level. Filter for roles where the estimated salary meets my minimum and the job description aligns with my resume skills. Present a shortlist of 5-10 candidates daily. I will manually select 0-2 to apply. Do not auto-submit applications. Before starting, confirm my resume file path and target salary. If levels.fyi data is missing for a role, flag it for manual review. Output the shortlist in a table with columns: Role, Company, Estimated Salary, Match Score, and Link. Start with a dry run on 3 sample roles to verify the filtering logic before processing the full daily batch.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.