lead-research-assistant

skillcreatorai/ai-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/skillcreatorai/ai-agent-skills --skill lead-research-assistant
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summary

This skill helps you identify and qualify potential leads for your business by analyzing your product/service, understanding your ideal customer profile, and providing actionable outreach strategies.

skill.md

Lead Research Assistant

This skill helps you identify and qualify potential leads for your business by analyzing your product/service, understanding your ideal customer profile, and providing actionable outreach strategies.

When to Use This Skill

  • Finding potential customers or clients for your product/service
  • Building a list of companies to reach out to for partnerships
  • Identifying target accounts for sales outreach
  • Researching companies that match your ideal customer profile
  • Preparing for business development activities

What This Skill Does

  1. Understands Your Business: Analyzes your product/service, value proposition, and target market
  2. Identifies Target Companies: Finds companies that match your ideal customer profile based on:
    • Industry and sector
    • Company size and location
    • Technology stack and tools they use
    • Growth stage and funding
    • Pain points your product solves
  3. Prioritizes Leads: Ranks companies based on fit score and relevance
  4. Provides Contact Strategies: Suggests how to approach each lead with personalized messaging
  5. Enriches Data: Gathers relevant information about decision-makers and company context

How to Use

Basic Usage

Simply describe your product/service and what you're looking for:

I'm building [product description]. Find me 10 companies in [location/industry] 
that would be good leads for this.

With Your Codebase

For even better results, run this from your product's source code directory:

Look at what I'm building in this repository and identify the top 10 companies 
in [location/industry] that would benefit from this product.

Advanced Usage

For more targeted research:

My product: [description]
Ideal customer profile:
- Industry: [industry]
- Company size: [size range]
- Location: [location]
- Current pain points: [pain points]
- Technologies they use: [tech stack]

Find me 20 qualified leads with contact strategies for each.

Instructions

When a user requests lead research:

  1. Understand the Product/Service

    • If in a code directory, analyze the codebase to understand the product
    • Ask clarifying questions about the value proposition
    • Identify key features and benefits
    • Understand what problems it solves
  2. Define Ideal Customer Profile

    • Determine target industries and sectors
    • Identify company size ranges
    • Consider geographic preferences
    • Understand relevant pain points
    • Note any technology requirements
  3. Research and Identify Leads

    • Search for companies matching the criteria
    • Look for signals of need (job postings, tech stack, recent news)
    • Consider growth indicators (funding, expansion, hiring)
    • Identify companies with complementary products/services
    • Check for budget indicators
  4. Prioritize and Score

    • Create a fit score (1-10) for each lead
    • Consider factors like:
      • Alignment with ICP
      • Signals of immediate need
      • Budget availability
      • Competitive landscape
      • Timing indicators
  5. Provide Actionable Output

    For each lead, provide:

    • Company Name and website
    • Why They're a Good Fit: Specific reasons based on their business
    • Priority Score: 1-10 with explanation
    • Decision Maker: Role/title to target (e.g., "VP of Engineering")
    • Contact Strategy: Personalized approach suggestions
    • Value Proposition: How your product solves their specific problem
    • Conversation Starters: Specific points to mention in outreach
    • LinkedIn URL: If available, for easy connection
  6. Format the Output

    Present results in a clear, scannable format:

    # Lead Research Results
    
    ## Summary
    - Total leads found: [X]
    - High priority (8-10): [X]
    - Medium priority (5-7): [X]
    - Average fit score: [X]
    
    ---
    
    ## Lead 1: [Company Name]
    
    **Website**: [URL]
    **Priority Score**: [X/10]
    **Industry**: [Industry]
    **Size**: [Employee count/revenue range]
    
    **Why They're a Good Fit**:
    [2-3 specific reasons based on their business]
    
    **Target Decision Maker**: [Role/Title]
    **LinkedIn**: [URL if available]
    
    **Value Proposition for Them**:
    [Specific benefit for this company]
    
    **Outreach Strategy**:
    [Personalized approach - mention specific pain points, recent company news, or relevant context]
    
    **Conversation Starters**:
    - [Specific point 1]
    - [Specific point 2]
    
    ---
    
    [Repeat for each lead]
    
  7. Offer Next Steps

    • Suggest saving results to a CSV for CRM import
    • Offer to draft personalized outreach messages
    • Recommend prioritization based on timing
    • Suggest follow-up research for top leads

Examples

Example 1: From Lenny's Newsletter

User: "I'm building a tool that masks sensitive data in AI coding assistant queries. Find potential leads."

Output: Creates a prioritized list of companies that:

  • Use AI coding assistants (Copilot, Cursor, etc.)
  • Handle sensitive data (fintech, healthcare, legal)
  • Have evidence in their GitHub repos of using coding agents
  • May have accidentally exposed sensitive data in code
  • Includes LinkedIn URLs of relevant decision-makers

Example 2: Local Business

User: "I run a consulting practice for remote team productivity. Find me 10 companies in the Bay Area that recently went remote."

Output: Identifies companies that:

  • Recently posted remote job listings
  • Announced remote-first policies
  • Are hiring distributed teams
  • Show signs of remote work challenges
  • Provides personalized outreach strategies for each

Tips for Best Results

  • Be specific about your product and its unique value
  • Run from your codebase if applicable for automatic context
  • Provide context about your ideal customer profile
  • Specify constraints like industry, location, or company size
  • Request follow-up research on promising leads for deeper insights

Related Use Cases

  • Drafting personalized outreach emails after identifying leads
  • Building a CRM-ready CSV of qualified prospects
  • Researching specific companies in detail
  • Analyzing competitor customer bases
  • Identifying partnership opportunities
how to use lead-research-assistant

How to use lead-research-assistant on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add lead-research-assistant
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/skillcreatorai/ai-agent-skills --skill lead-research-assistant

The skills CLI fetches lead-research-assistant from GitHub repository skillcreatorai/ai-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lead-research-assistant

Reload or restart Cursor to activate lead-research-assistant. Access the skill through slash commands (e.g., /lead-research-assistant) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.728 reviews
  • Pratham Ware· Dec 24, 2024

    lead-research-assistant fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 15, 2024

    Registry listing for lead-research-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Dhruvi Jain· Oct 6, 2024

    lead-research-assistant reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Sep 25, 2024

    I recommend lead-research-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Piyush G· Sep 21, 2024

    lead-research-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • James Torres· Sep 5, 2024

    lead-research-assistant fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sophia White· Sep 1, 2024

    We added lead-research-assistant from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Emma Jain· Aug 24, 2024

    We added lead-research-assistant from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nia Martinez· Aug 20, 2024

    lead-research-assistant fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ganesh Mohane· Aug 16, 2024

    Useful defaults in lead-research-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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