Research a Company for Sourcing Targets
Type a company name and get a sourcing-ready intelligence report -- real named engineers with verified profiles, tech stack fingerprint, per-person outreach angles, and LinkedIn Boolean search strings.
Overview
Starting to source from a new company means opening LinkedIn, running a keyword search, clicking through 10-15 profiles, and writing generic messages with no real hook. This MCP template replaces that 20-30 minute research loop with a 60-second company scan. Type a company name and get back a sourcing-ready intelligence report: real named engineers pulled from GitHub contributor credits, engineering blog bylines, conference talks, and public profiles -- each with a verified LinkedIn URL, specific outreach angle, and evidence of what they actually built.
How it works
Provide a company name -- and optionally a target role type and seniority level. The skill researches GitHub contributor data, engineering blog bylines, conference speaker lists, and release notes to find real, named engineers. Every name is cross-referenced against LinkedIn and GitHub to confirm current employment before being surfaced. The report includes a full tech stack fingerprint, 4-6 warm targets with per-person outreach angles, and LinkedIn Boolean search strings tuned to the role and level. You go to LinkedIn already knowing exactly who you are looking for.
Who this is for
Built for technical sourcers and recruiters who source engineers from specific target companies. Most useful when you are starting a new search and want a targeted list before going to LinkedIn -- especially for roles where outreach quality matters and generic messages get ignored.
Suggested prompt
Scan [Company Name] for IC4 backend engineers
Frequently asked questions
What sources does this pull from to find real engineers?
GitHub contributor data, engineering blog bylines, conference talk speaker lists, release notes with contributor credits, and podcast appearances. The skill cross-references every name found against LinkedIn and GitHub to confirm current employment and real identity before surfacing them. If it cannot find real names, it falls back to LinkedIn Boolean search strings instead of fabricating profiles.
Can I target a specific role type or seniority level?
Yes -- and you should. Providing a role type (for example, backend engineer, SRE, applied AI engineer, or engineering manager) and a level (IC3, IC4, IC5, M3, M4) sharpens every step of the research. Different roles leave different public footprints, so the skill changes where it looks, not just what it labels. An IC4 SRE at a CNCF-active company will have a rich public trail; an IC3 backend product engineer at a startup may not.
What does the output include?
A full tech stack fingerprint (languages, frameworks, infra, data, AI/ML, tooling), 4-6 named warm targets with GitHub handles, LinkedIn URLs or search strings, a specific outreach angle per person, and LinkedIn Boolean search strings tuned to the target role and level. It also includes a sourcing context section covering what makes engineers at this company compelling candidates and what a new role could offer them.
What if the company has no public engineering presence?
The skill is transparent about this. If a company's engineers have no visible public footprint -- common with early-stage startups or companies with no public OSS -- it will say so clearly, explain which sources were tried, and deliver strong LinkedIn Boolean search strings instead of surfacing fabricated profiles.