Job Hunter

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name: job-hunter description: Comprehensive job search assistant for finding, evaluating, and applying to job opportunities. Use when a user needs help with job hunting, job searching, finding openings, evaluating job fit, preparing applications, writing cover letters, interview preparation, salary research, or tracking applications. Supports multi-source job search across LinkedIn, Indeed, Glassdoor, and more with automated fit scoring against a candidate profile.

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Job Hunter

End-to-end job search assistant — from finding opportunities to landing interviews.

Quick Start

1. Set up candidate profile

Create a profile JSON for the user. Use the template at {baseDir}/references/profile-template.json as a starting point. Ask the user about:

Target roles and seniority level

Key skills and tools

Location preferences (cities + remote)

Salary expectations

Dealbreakers and excluded companies

Preferred industries/domains

Save as profile.json in the workspace.

2. Search for jobs

Use the web_search tool with multiple queries to cast a wide net:

site:linkedin.com/jobs "[role]" "[city]"
site:indeed.com "[role]" "[city]"  
site:glassdoor.com/job "[role]" "[city]"
"[role]" "[city]" hiring 2025 2026

Expand keywords — don't just search one title. See {baseDir}/references/search-strategies.md for keyword expansion patterns.

Alternative: run the search script if Brave API is available:

{baseDir}/scripts/search_jobs.sh "CX Manager" --location "Amsterdam" --days 7

3. Evaluate fit

For each job found, run fit analysis:

python3 {baseDir}/scripts/analyze_fit.py --profile profile.json --jobs jobs.json --threshold 50

Or evaluate manually using this framework:

Skill match (40%): Does user have 60%+ of required skills?

Seniority match (25%): Right level — not over/under qualified?

Location match (15%): Compatible location or remote?

Domain match (10%): Preferred industry/domain?

Red flags (10%): Excluded companies? Dealbreakers?

Score: 🟢 75+ great | 🟡 55-74 good | 🟠 40-54 stretch | 🔴 <40 skip

4. Present results

For each job, present:

Role & Company with direct link

Fit score with color indicator

Why it's a match (top 3 skill matches)

Gaps to address (missing skills to highlight as "eager to learn")

Salary estimate if available

Recommendation: Apply / Maybe / Skip

Application Support

Cover letters

Read {baseDir}/references/cover-letter-guide.md for structure and tone guidelines. Generate tailored cover letters that:

Reference specific company details (not generic)

Map user's experience to top 2-3 job requirements

Include quantified achievements

Stay under 350 words

Interview prep

Read {baseDir}/references/interview-prep.md for complete preparation framework. Help with:

Company research summaries

STAR stories for key requirements

Tailored "tell me about yourself" script

Salary negotiation talking points

Questions to ask the interviewer

Salary research

bash {baseDir}/scripts/salary_research.sh "Job Title" "Location"

Cross-reference 3+ sources. In the Netherlands: factor in 8% holiday allowance, possible 13th month, pension.

Daily Brief Format

When running as a scheduled job search brief:

1. New opportunities — jobs found in last 24h with fit scores and direct links 2. Application status — updates on pending applications 3. Action items — what to apply to today, follow-ups due 4. Market intel — industry trends, salary movements, hiring patterns

Tracking

Maintain a job tracker with:

Company, role, date found, source URL

Fit score and recommendation

Status: new → applied → screening → interview → offer/rejected/ghosted

Applied/skipped with reason

Contact info and follow-up dates

Tips for Agents

Never apply on behalf of the user — present opportunities, let them decide

Don't overwhelm — 3-5 quality matches beat 20 mediocre ones

Track excluded companies — never suggest the same company twice after rejection

Be honest about fit — stretches are okay to flag, but don't oversell poor matches

Respect dealbreakers — if user said no customer service, don't suggest it even if "it's a great company"

Update the profile — as you learn user preferences, refine the profile

Celebrate wins — applied to a job? Got an interview? Acknowledge it

1. Set up candidate profile

Create a profile JSON for the user. Use the template at {baseDir}/references/profile-template.json as a starting point. Ask the user about:

Target roles and seniority level

Key skills and tools

Location preferences (cities + remote)

Salary expectations

Dealbreakers and excluded companies

Preferred industries/domains

Save as profile.json in the workspace.

2. Search for jobs

Use the web_search tool with multiple queries to cast a wide net:

site:linkedin.com/jobs "[role]" "[city]"
site:indeed.com "[role]" "[city]"  
site:glassdoor.com/job "[role]" "[city]"
"[role]" "[city]" hiring 2025 2026

Expand keywords — don't just search one title. See {baseDir}/references/search-strategies.md for keyword expansion patterns.

Alternative: run the search script if Brave API is available:

{baseDir}/scripts/search_jobs.sh "CX Manager" --location "Amsterdam" --days 7

3. Evaluate fit

For each job found, run fit analysis:

python3 {baseDir}/scripts/analyze_fit.py --profile profile.json --jobs jobs.json --threshold 50

Or evaluate manually using this framework:

Skill match (40%): Does user have 60%+ of required skills?

Seniority match (25%): Right level — not over/under qualified?

Location match (15%): Compatible location or remote?

Domain match (10%): Preferred industry/domain?

Red flags (10%): Excluded companies? Dealbreakers?

Score: 🟢 75+ great | 🟡 55-74 good | 🟠 40-54 stretch | 🔴 <40 skip

4. Present results

For each job, present:

Role & Company with direct link

Fit score with color indicator

Why it's a match (top 3 skill matches)

Gaps to address (missing skills to highlight as "eager to learn")

Salary estimate if available

Recommendation: Apply / Maybe / Skip