Jobgpt

JobGPT - Job Search Automation

Overview

JobGPT connects your AI assistant to a complete job search automation platform via the JobGPT MCP server. It provides 34 tools covering job search, auto-apply, resume generation, application tracking, salary intelligence, and recruiter outreach so you can manage your entire job hunt from your AI coding assistant.

Built by 6figr.com, the platform supports 150+ countries with salary data, job matching, and automated applications.

When to Use This Skill

• You want to search for jobs with filters like titles, locations, salary, remote, and H1B sponsorship
• You want to auto-apply to jobs automatically
• You want to generate a tailored resume for a specific job application
• You want to track your job applications across multiple job hunts
• You want to find recruiters or referrers at target companies and send outreach emails
• You want to import a job from LinkedIn, Greenhouse, Lever, Workday, or any job board URL
• You want to check your salary and compare compensation across roles

Setup

This skill requires the JobGPT MCP server:

1. Create an account - Sign up at 6figr.com/jobgpt-ai
2. Get an API key - Go to 6figr.com/account, scroll to MCP Integrations, and click Generate API Key. The key starts with mcp_.
3. Add the MCP server:
   • Claude Code: claude mcp add jobgpt -t http -u https://mcp.6figr.com/mcp --header "Authorization: <api-key>"
   • Other tools: Add jobgpt-mcp-server as an MCP server with env var JOBGPT_API_KEY set. Install via npx jobgpt-mcp-server.

Set the JOBGPT_API_KEY environment variable when you are running the local npx jobgpt-mcp-server path.

Examples

Find Remote Jobs

"Find remote senior React jobs paying over $150k"

The skill uses search_jobs with title, remote, and salary filters to find matching positions, then presents results with company, title, location, salary range, and key skills.

Auto-Apply to Jobs

"Auto-apply to the top 5 matches from my job hunt"

The skill checks that your resume is uploaded, uses match_jobs to find new matches, saves the selected matches with add_job_to_applications, then triggers apply_to_job for each resulting application. It monitors progress with get_application_stats.

Generate a Tailored Resume

"Generate a tailored resume for this Google application"

The skill calls generate_resume_for_job to create an AI-optimized resume targeting the specific job's requirements, then provides the download link via get_generated_resume.

Import and Apply from a URL

"Apply to this job for me - https://boards.greenhouse.io/company/jobs/12345"

The skill uses import_job_by_url to import the job from any supported platform (LinkedIn, Greenhouse, Lever, Workday), adds it to applications, and optionally triggers auto-apply.

Recruiter Outreach

"Find recruiters for this job and draft an outreach email"

The skill finds recruiters with get_job_recruiters and helps craft a personalized message. The draft is presented to the user for review; send_outreach is only called after explicit user confirmation.

Check Application Stats

"Show my application stats for the last 7 days"

The skill uses get_application_stats for an aggregated overview - total counts by status, auto-apply metrics, and pipeline progress.

Best Practices

• Check credits first - Auto-apply and resume generation consume credits. Use get_credits before batch operations.
• Complete your profile - Run get_profile first and fill in missing fields with update_profile for better job matches.
• Upload a resume before applying - Use list_resumes to check, and upload_resume if needed.
• Use job hunts for ongoing searches - Create a job hunt with create_job_hunt to save filters and get continuous matches.
• Use get_application for saved jobs - If a user asks about a job they've already saved, use get_application instead of get_job.

Troubleshooting

Additional Resources

• JobGPT Platform - Sign up and manage your account
• MCP Server Repo - Source code and setup guides
• Skills Repo - This skill's source
• npm Package - Install via npm

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags jobgpt <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns