LinkedIn Research Agent

Overview

Built a LinkedIn sourcing workflow agent that turns role requests into structured search operations: it parses hiring intent, generates Boolean query logic, executes LinkedIn People search in an MCP-controlled browser, and returns plain profile URLs for downstream recruiting or research pipelines.

The project is designed around reproducible, instruction-driven agent workflows with optional enrichment mode that extracts public profile metadata (name, role, company, headline, location, company link, and public contact fields) from individual profile URLs.

Why This Project

Manual LinkedIn sourcing is repetitive and hard to standardize across searches. This project converts that process into a deterministic agent flow so output quality is consistent: query construction follows clear rules, search navigation is scripted, and results can be saved to timestamped JSON runs.

It also separates discovery from enrichment: Comet navigation collects candidate URLs efficiently, while Chrome DevTools-based extraction handles deeper profile parsing when needed.

How a Search Runs

Key Capabilities

• Boolean Query Builder

  • Converts user intent into compact LinkedIn-friendly Boolean strings.
  • Supports includes, excludes, title phrases, and location intent.

• People Search Navigation

  • Applies LinkedIn search + People filter through MCP browser actions.
  • Collects profile URLs from result pages in strict plain-text format.

• Optional Profile Extraction

  • Given profile URLs, extracts structured public fields for analysis.
  • Returns empty values for missing fields instead of guessing.

• Run Artifacts

  • Saves timestamped JSON outputs in a dedicated runs/ directory.
  • Keeps collected data separate from source instructions and skills.

Status

The project is working and extensible, with clear skill-based modules for query generation, search-page navigation, and profile extraction.