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Build targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data.
Passed 16 of 26 test cases with the skill, and 16 of 26 without it.
and 2 more in the full report
3 fixed13 passed either way7 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are looking for CMOs and Marketing Directors at European Fintech startups that recently raised Series A funding. Can you research publicly available prospect data to build a segmented lead list, outline economic and technical buyer roles, provide personalization context for outreach, and export the results in a structured CRM table format?
| Checked | Without skill | With skill |
|---|---|---|
| The response explicitly defines Ideal Customer Profile parameters including tech stack and company size. | Missed | Passed |
| The response provides a search strategy outlining job-title patterns, location filters, seniority level, and keyword criteria. | Missed | Passed |
| The response maps out decision-maker personas including primary buyer, influencers, economic buyer, and technical buyer. | Missed | Passed |
The instructions your model reads when the skill loads.
Find and qualify prospects on LinkedIn without expensive subscriptions, using only publicly available information.
references/output-template.md — full prospect-list Markdown structure and section layoutreferences/campaigns.md — segmented campaign playbooks (new-in-role, account-based, event follow-up)references/tools-and-export.md — contact-finding methods, tool stack, CSV/CRM export formatsreferences/examples.md — trigger phrases, example request, best practicesWork only with publicly available information and respect LinkedIn's Terms of Service. Encourage official LinkedIn tools where appropriate. Aggregate public data for legitimate business development only.
Define the Ideal Customer Profile (ICP). Capture industry/sector, company size (employees, revenue), geography, tech stack, and funding stage.
Identify decision-maker personas. Map the primary buyer, influencers, the economic buyer (budget holder), and the technical buyer (evaluation team).
Build the search strategy. Set job-title patterns, company criteria, location filters, seniority level, and description keywords. Choose a list-building approach: account-based, role-based, industry-based, event-based (job changes, funding rounds), or geography-based.
Collect and enrich data (public info only). Compile name, title, company, size, location, industry, public activity, and shared connections. Verify company details, find contact information, add personalization context, and score lead quality. See references/tools-and-export.md for discovery and verification methods.
Prioritize and segment. Tag hot leads (recent job changes), warm leads (shared connections), and cold leads (no prior contact). Group by account for campaigns.
Deliver the prospect list. Format the output per references/output-template.md, attach relevant campaign playbooks from references/campaigns.md, and provide export-ready data per references/tools-and-export.md.
Deliver a targeted, researched set of qualified prospects with personalization context — not just a list of names. See references/examples.md for trigger phrases and best practices.
test cases passed, vs 16 / 26 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.