Can this skill analyze my local files?

Yes, when run from within a repository, it can analyze your codebase to understand your product's unique value proposition and target market automatically.

How does it prioritize potential leads?

It uses a 1-10 scoring system based on factors like alignment with your Ideal Customer Profile (ICP), growth signals, technology stack, and market timing.

Does it provide contact information for decision-makers?

It identifies specific target roles (such as VP of Engineering or Head of Product) and provides LinkedIn URLs where available to facilitate direct outreach.

Can I export the research results to my CRM?

Yes, you can instruct Claude to format the final research output as a CSV or markdown table, making it easy to import into tools like Salesforce, HubSpot, or Pipedrive.

Lead Research Assistant

Name: Lead Research Assistant
Author: davepoon

bydavepoon

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2,257

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Automatización de Marketing

Identifies and qualifies high-priority business leads by analyzing your product context and providing personalized outreach strategies.

The Lead Research Assistant is a specialized Claude Code skill designed to streamline the sales and business development process. By analyzing your project's codebase or a specific product description, it identifies target companies that match your ideal customer profile, ranks them based on relevance, and provides actionable contact strategies. Whether you're a developer looking for early adopters or a sales professional building a pipeline, this skill enriches lead data with specific value propositions and conversation starters to improve your outreach success rate.

Características Principales

01Data enrichment including tech stack and decision-maker roles

02Personalized outreach messaging and contact strategies

03Prioritized lead scoring based on fit and relevance

042,257 GitHub stars

05Context-aware lead discovery via codebase analysis

06Detailed Ideal Customer Profile (ICP) definition

Casos de Uso

01Building a list of strategic partners based on complementary technology stacks

02Researching target accounts for targeted B2B marketing campaigns

03Identifying potential enterprise clients for a newly developed software tool

Características Principales

01Data enrichment including tech stack and decision-maker roles

02Personalized outreach messaging and contact strategies

03Prioritized lead scoring based on fit and relevance

042,257 GitHub stars

05Context-aware lead discovery via codebase analysis

06Detailed Ideal Customer Profile (ICP) definition

Casos de Uso

01Building a list of strategic partners based on complementary technology stacks

02Researching target accounts for targeted B2B marketing campaigns

03Identifying potential enterprise clients for a newly developed software tool