LinkedIn Job Scout

Research open roles on LinkedIn and produce a prioritized shortlist matched to the candidate's profile, level, stack, and preferences.

Prerequisites

• LinkedIn MCP — search_jobs, get_job_details, get_company_profile

Workflow

1. Gather Target Criteria

From the user, establish:

• Target companies (specific names)
• Location (city, country, or "remote")
• Work type (on-site, hybrid, remote)
• Level (junior/mid/senior/staff — infer from YoE if not stated)
• Stack (languages, frameworks, cloud)
• Compensation goals (USD? local currency? range?)

2. Search Jobs

Run parallel searches:

search_jobs(keywords="{company} software engineer", location="{city}", max_pages=2)
search_jobs(keywords="backend software engineer", location="{city}", work_type="remote", max_pages=2)
search_jobs(keywords="{niche_skill} engineer", location="{country}", work_type="remote", max_pages=2)

3. Get Details for Top Matches

For the most promising job IDs:

get_job_details(job_id)

Extract: requirements, YoE asked, stack match, compensation (if listed), team info, English requirement.

4. Assess Fit

For each role, rate as:

• ⭐ Strong — level matches, stack aligns, location works
• Good — most criteria match, minor stretch on one dimension
• Stretch — notably above stated level or missing key requirement

5. Present Shortlist

Use two tables:

Location-based roles:

Remote / USD roles:

Include job ID links as: [{id}](https://www.linkedin.com/jobs/view/{id}/)

5b. Persist to Mercury

Save each shortlisted role to the Mercury database (powers the dashboard's Jobs section):

mercury job save \
  --linkedin-id {id} --title "{Role}" --company "{Company}" \
  --location "{City}" --work-type "{remote|hybrid|onsite}" \
  --comp "{comp if known}" --fit "{strong|good|stretch}" \
  --link "https://www.linkedin.com/jobs/view/{id}/"

Log the scout run:

mercury activity log --skill job-scout --summary "Scouted {N} roles for {query}"

If mercury isn't installed, just present the shortlist tables (below).

6. Flag Caveats

• Diversity-scoped roles ("Vaga para mulheres", "PCD-only") — note eligibility
• Staffing aggregators with high stated comp — flag for legitimacy verification
• Roles requiring significantly more YoE than candidate has
• External ATS vs Easy Apply (affects application friction)

Compensation Reality Check

Most Brazil-remote listings hide compensation. When visible:

• Staffing firms (Crossing Hurdles, Hire Feed, Quik Hire): often inflated or contractor rates — verify
• Legitimate product companies hiring BR-remote in USD: Airbnb, DoorDash, Brex, Uber, Kraken, ClassPass, TRM Labs, Wellhub, Engine, Housecall Pro, Motorola
• $45/hr contracts ≈ $93K/yr; $80-100K = typical mid-senior LatAm remote; $180-230K = verify carefully

Company URN Lookup

search_people(current_company=...) requires numeric URN IDs, not names:

get_company_profile(company_name) → references.about → company_urn.value

Common URNs:

• Airbnb: 309694
• DoorDash: 3205573
• Uber: 1815218
• Amazon: 1586