SkillMatch MCP

Claude-powered job fit analyzer for people who prove their skills through work, not credentials.

What It Does

SkillMatch is an MCP server that gives Claude access to your GitHub portfolio, resume, and job search preferences. It lets Claude:

• Analyze how well you fit a specific job description based on your actual projects and resume
• Build optimized job search queries tailored to your targets and constraints
• Track every application you submit in a local SQLite database
• Pull your public GitHub repos to highlight relevant work during fit analysis

All data stays local on your machine. Nothing is sent anywhere except public GitHub API calls to fetch your own repos.

Setup

1. Install the dependency

cd skillmatch-mcp
pip install -r requirements.txt

2. Add to Claude

Claude Code (CLI):

claude mcp add --scope user skillmatch -- python /path/to/skillmatch-mcp/server.py

Then restart Claude Code.

Claude Desktop:

Open your Claude Desktop configuration file and add SkillMatch to the mcpServers section:

{
  "mcpServers": {
    "skillmatch": {
      "command": "python",
      "args": ["/path/to/skillmatch-mcp/server.py"]
    }
  }
}

Replace /path/to/skillmatch-mcp/server.py with the actual path to server.py on your machine, then restart Claude Desktop.

3. Start using it

After restarting, SkillMatch tools will be available in your conversation. Works in Claude Code and Claude Desktop.

Onboarding

The first time you use SkillMatch, Claude will walk you through a setup conversation. It looks something like this:

You: Help me find a new job.

Claude: I don't have your profile set up yet. Let me ask you a few questions first.

  What is your name?
You: Jordan

Claude: What is your current role?
You: Senior frontend developer

Claude: What roles are you targeting?
You: Staff frontend engineer, senior full-stack engineer

Claude: What is your minimum acceptable salary?
You: 180000

Claude: Are you only looking for remote positions?
You: Yes

Claude: Where are you located?
You: Portland, OR

Claude: Any dealbreakers?
You: No on-call rotations, no defense contractors

Claude: What is your GitHub URL?
You: https://github.com/jordan-dev

Claude: Where is your resume file?
You: C:/Users/jordan/Documents/resume.md

Claude: Got it. Your profile is saved. Let me search for jobs matching your targets.

After onboarding, your profile is saved locally and reused in every future conversation.

Extended Profile (optional)

You can enrich your profile at any time by asking Claude to update it. These fields help Claude reason about nuanced fit:

Field	Type	Example
work_style	object	{async_preferred: true, ic_vs_leadership: "ic", client_facing_tolerance: "none", team_size_preference: "small"}
optimizing_for	array	["comp", "autonomy", "interesting_problems"]
unlisted_skills	array	["MCP protocol implementation", "Windows installer packaging"]
developing_skills	array	["TypeScript", "RAG pipelines"]
dealbreaker_detail	array	[{dealbreaker: "on-call", hardness: "absolute"}, {dealbreaker: "relocation", hardness: "strong_preference"}]
rejection_patterns	array	["roles that sounded like automation but were actually IT support"]

These can be set during initial setup or added later with the update_profile tool.

How It Works

search_jobs builds a search query from your profile and any keywords you provide. Claude then uses that query with its web search capabilities to find real listings. The tool itself does not search the web.

analyze_fit runs a two-step process. First it parses the job description into structured signal (hard requirements, nice-to-haves, red flags, compensation signals, role type) via the Claude API. Then it fetches your portfolio and auto-selects the best resume variant for the detected role type. Claude sees structured signal before raw marketing copy.

parse_jd is the standalone JD parser. Use it independently to pre-process a job description without running the full fit analysis.

log_application, get_applications, and update_application form a job search CRM. Track status (applied, screening, interview, offer, rejected, ghosted), set follow-up dates, and record outcomes.

get_follow_ups shows applications that need attention — where the follow-up date has passed and you're still waiting.

get_application_patterns analyzes your full application history (10+ needed) to find which role types get responses, which skills resonate, and recommends search adjustments.

email_ranked_jobs.py is a standalone Conductor worker script that emails the latest ranked job report. See Email Worker Setup below for configuration.

save_scouted_job saves a job listing found during scouting. It validates the URL to reject search result pages and deduplicates against existing scouted jobs and applications.

get_scouted_jobs returns all scouted listings, optionally filtered to only unranked ones. mark_jobs_ranked marks all unranked jobs as ranked after a ranking report is generated.

add_resume and list_resumes manage multiple resume variants. Each variant targets specific role types (e.g. "AI Engineering" targets ai_engineering and ml_engineering). During fit analysis, the best variant is auto-selected based on the JD's detected role type.

update_profile merges new or changed fields into your existing profile without re-running setup.

get_portfolio and get_resume can be called independently if you want Claude to review just your repos or just your resume.

Quick Start (No File Paths)

For the simplest setup, paste your resume directly — no local files needed:

You: Help me find a job.
Claude: What is your name?
You: Alex
Claude: What roles are you targeting?
You: AI engineer, ML engineer
Claude: Paste your resume or provide a file path.
You: [paste resume text here]
Claude: Profile saved. Let me search for jobs.

Email Worker Setup

email_ranked_jobs.py sends the ranked job report via Gmail. It is designed to run as a Conductor worker on a schedule.

Gmail credentials must be hardcoded directly in the file. Environment variables are not reliable here — depending on how the worker process is launched, env vars may not be inherited, causing silent failures. Open email_ranked_jobs.py and set these three lines at the top:

GMAIL_USER = "you@gmail.com"
GMAIL_APP_PASSWORD = "xxxx xxxx xxxx xxxx"  # Gmail App Password, not your account password
EMAIL_TO = "you@gmail.com"

To generate a Gmail App Password: Google Account → Security → 2-Step Verification → App Passwords. Create one for "Mail".

The script reads data/ranked_jobs.md, caps the email to the top 15 ranked listings, sends it, and then deletes ranked_jobs.md so stale rankings are not recycled on the next run.

File Structure

skillmatch-mcp/
  server.py              # MCP server (stdio JSON-RPC)
  requirements.txt       # python-docx dependency
  CLAUDE.md              # Instructions for Claude
  README.md              # This file
  email_ranked_jobs.py   # Conductor worker: emails ranked job reports
  cowork_monitor.py      # Conductor worker: monitors Cowork VM, auto-recovers
  cowork_tab.png         # Reference image for Cowork tab UI automation
  data/
    .gitkeep             # Keeps the folder in git
    profile.json         # Created on first setup (gitignored)
    applications.db      # Created on first log (gitignored)
    scouted_jobs.json    # Scouted listings (gitignored)
    ranked_jobs.md       # Latest ranked report (gitignored)