Job Search Skill Priority hierarchy : See shared/references/priority hierarchy.md for conflict resolution. Automated daily job search using browser automation. Quick Start /proficiently:job search Run daily search with default terms from matching rules /proficiently:job search AI infrastructure Search with specific keywords File Structure Data Directory Resolve the data directory using shared/references/data directory.md . Workflow Step 0: Check Prerequisites Resolve the data directory, then check prerequisites per shared/references/prerequisites.md . Resume and preferences are both required. Step 1: Load Context Read these files: DATA DIR/resume/ (candidate profile) DATA DIR/preferences.md (preferences) DATA DIR/job history.md (to avoid duplicates) DATA DIR/linkedin contacts.csv (if it exists — for network matching) Extract search terms from: 1. $ARGUMENTS if provided 2. Target roles from preferences Step 2: Browser Search Use Claude in Chrome MCP tools per shared/references/browser setup.md , navigating to https://hiring.cafe. For each search term, enter the query and apply relevant filters (date posted, location, etc.). Extracting results — IMPORTANT: Do NOT use get page text on hiring.cafe or any large job listing page. It returns the entire page content and will blow out the context window. Instead, extract job listings using javascript tool to pull only structured data: If that selector doesn't match, take a screenshot to understand the page structure, then write a targeted JS selector for the specific site. The goal is to extract just the listing rows (title, company, location, salary) — never the full page. As a fallback, use read page (NOT get page text ) and scan for listing elements. Note: Hiring.cafe is just our search tool. Don't share hiring.cafe links with the user — you'll resolve direct employer URLs for the top matches in Step 5. Step 3: Evaluate Jobs Score each job against the candidate's resume and preferences using the criteria in shared/references/fit scoring.md . Step 4: Save History Append ALL jobs to DATA DIR/job history.md : Step 5: Resolve Employer URLs & Save Top Postings For each High fit job: 1. Click through the hiring.cafe listing to reach the actual employer careers page 2. Capture the direct employer URL for the job posting 3. Extract the job description using javascript tool to pull the posting content (e.g. document.querySelector('[class ="description"], [class ="content"], article, main')?.innerText ). Do NOT use get page text — employer pages often have huge footers, navs, and related listings that bloat the output and can blow out the context window. 4. Save to DATA DIR/jobs/[company slug] [date]/posting.md with the employer URL at the top For Medium fit jobs, try to resolve the employer URL but don't save the full posting. If you can't resolve the direct link for a job, note the company name so the user can find it themselves. Never show hiring.cafe URLs to the user. Step 6: Present Results Show only NEW High/Medium fits not in previous history. If LinkedIn contacts were loaded, cross reference each result's company name against the "Company" column in the CSV. Use fuzzy matching (e.g. "Google" matches "Google LLC", "Alphabet/Google"). If there's a match, include the contact's name and title. Omit the "Network" line if there are no contacts at that company. Step 7: Next Steps After presenting results, tell the user: To apply now (tailors resume, writes cover letter if needed, fills the form): /proficiently:apply [job URL] To tailor a resume only: /proficiently:tailor resume [job URL] To write a cover letter only: /proficiently:cover letter [job URL] IMPORTANT : Do NOT attempt to tailor resumes, write cover letters, or fill applications yourself. Those are separate skills with their own workflows. If the user asks to do any of these for a job, direct them to use the appropriate skill command. Also include at the end of results: Step 8: Learn from Feedback If user provides feedback, update DATA DIR/preferences.md : "No agencies" → add to dealbreakers "Prefer AI companies" → add to nice to haves "Minimum $350k" → update salary threshold Response Format Structure user facing output with these sections: 1. Top Matches — table or list of High/Medium fits with company, role, fit rating, salary, location, network contacts, and direct URL 2. Next Steps — suggest /proficiently:tailor resume and /proficiently:cover letter for top matches Permissions Required Add to ~/.claude/settings.json :

job-search