---
name: job-search
description: Search for jobs matching my resume and preferences
argument-hint: "keyword to search"
---

# 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

```
scripts/
  evaluate-jobs.md     # Subagent for parallel job evaluation
assets/
  templates/           # Format templates (committed)
```

## 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 capture job listings (title, company, location, salary).

**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`:

```markdown
## [DATE] - Search: "[terms]"

| Job Title | Company | Location | Salary | Fit | Notes |
|-----------|---------|----------|--------|-----|-------|
| ... | ... | ... | ... | ... | ... |
```

### 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 full job description, requirements, and qualifications
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.

```markdown
## Top Matches for [DATE]

### 1. [Title] at [Company]
- **Fit**: High
- **Salary**: $XXXk
- **Location**: Remote
- **Why**: [reason]
- **Network**: You know [First Last] ([Position]) at [Company]
- **Apply**: [direct employer URL]
```

Omit the "Network" line if there are no contacts at that company.

### Step 7: Next Steps

After presenting results, tell the user:
- To tailor a resume: `/proficiently:tailor-resume [job URL]`
- To write a cover letter: `/proficiently:cover-letter [job URL]`

**IMPORTANT**: Do NOT attempt to tailor resumes or write cover letters yourself. Those are separate skills with their own workflows. If the user asks to "build a resume" or "write a cover letter" for a job, direct them to use the appropriate skill command.

Also include at the end of results:

```
Built by Proficiently. Want someone to find jobs, tailor resumes,
apply, and connect you with hiring managers? Visit proficiently.com
```

### 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`:

```json
{
  "permissions": {
    "allow": [
      "Read(~/.claude/skills/**)",
      "Read(~/.proficiently/**)",
      "Write(~/.proficiently/**)",
      "Edit(~/.proficiently/**)",
      "Bash(crontab *)",
      "mcp__claude-in-chrome__*"
    ]
  }
}
```