Job Search

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

# Job Search Skill

> **Priority hierarchy**: See `references/priority-hierarchy.md` for conflict resolution.

Automated daily job search using browser automation.

## File Structure

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

## Data Directory

Resolve the data directory using `references/data-directory.md`.

---

## Workflow

### Step 0: Check Prerequisites

Resolve the data directory, then check prerequisites per `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 `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:

```javascript
// Extract visible job listing data from the page
Array.from(document.querySelectorAll('[class*="job"], [class*="listing"], [class*="card"], tr, [role="listitem"]'))
  .slice(0, 50)
  .map(el => el.innerText.trim())
  .filter(t => t.length > 20 && t.length < 500)
  .join('\n---\n')
```

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 `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 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.

```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 apply now (tailors resume, writes cover letter if needed, fills the form): use the apply skill
- To tailor a resume only: use the tailor-resume skill
- To write a cover letter only: use the cover-letter skill

**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.

### 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 tailoring the resume and writing a cover letter for top matches