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Lead Research Assistant Cn

作者 Guohongbin · GitHub ↗ · v1.0.2
cross-platform ⚠ suspicious
720
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在 OpenClaw 中安装
/install lead-research-assistant-cn
功能描述
销售线索研究助手 | Sales Lead Research Assistant. 识别高质量销售线索 | Identify high-quality sales leads. 分析目标公司、提供联系策略 | Analyze target companies, provide contact strategies...
使用说明 (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
安全使用建议
This skill appears coherent for lead research, but consider the following before installing/using it: - Privacy & PII: The skill will collect names and LinkedIn URLs (personal data). Ensure you have a lawful basis to collect and use these contacts and review GDPR/CCPA implications where relevant. - LinkedIn / Site scraping: The SKILL.md implies gathering LinkedIn profiles and other web signals; scraping may violate site terms of service. Prefer using official APIs or licensed data providers and avoid automated scraping without permission. - Running in code repositories: The skill suggests analyzing a local codebase. Only run it in repositories you control and that don't contain secrets, API keys, or sensitive customer data (check for .env, config, or private keys first). - Missing external integrations: If you expect richer enrichment (email addresses, verified contact data), you'll likely need to provide API keys for data providers or CRMs; the skill does not request these, so plan how you will supply and secure them. - Review outputs before outreach: Validate contact data and messaging to avoid incorrect or harmful outreach. If you want a stronger assurance, ask the publisher for: (1) the skill source or author identity, (2) a clear list of external services it will access, and (3) recommended credentials/permissions and how they are handled.
功能分析
Type: OpenClaw Skill Name: lead-research-assistant-cn Version: 1.0.2 The skill instructs the AI agent to "analyze the codebase" if run within a code directory. While this capability is presented as necessary for understanding the product to generate leads, it grants the agent broad access to local files. This represents a significant vulnerability risk (e.g., data exposure, potential for RCE if the agent's analysis involves executing untrusted code snippets) that could be exploited if the agent's execution environment is not securely sandboxed, or through subsequent prompt injection, even though the skill itself does not explicitly instruct malicious actions like exfiltration or persistence.
能力评估
Purpose & Capability
Name/description (sales lead research) match the SKILL.md steps: understanding a product, finding companies, scoring leads, and producing outreach strategies. No environment variables, binaries, or installs are requested that would be out of scope for lead research.
Instruction Scope
Instructions tell the agent to analyze a codebase if run inside one and to research public signals (job postings, tech stack, news, LinkedIn). Collecting decision-maker names and LinkedIn URLs is expected for this task, but it involves handling personal data and may imply web scraping or use of third-party APIs (which are not further specified). The skill does not instruct the agent to read unrelated system files, but running it in a repository could surface sensitive files if the user invokes it there.
Install Mechanism
No install spec or code files are present (instruction-only). This minimizes filesystem and supply-chain risk.
Credentials
The skill requests no environment variables or credentials. This is proportionate to an instruction-only research assistant; however, practical enrichment (LinkedIn, CRMs, paid data providers) may require credentials that the skill does not request nor document.
Persistence & Privilege
always is false and the skill does not request system-wide changes or persistent presence. It does not attempt to modify other skills or agent configuration.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install lead-research-assistant-cn
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /lead-research-assistant-cn 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.2
- No changes detected in this version. - No updates to features or documentation.
v1.0.1
- Improved bilingual description and trigger keywords for easier discovery. - Aligned metadata to reference the upstream source repository. - No core logic changes; usage instructions and examples remain the same.
v1.0.0
翻译自 ComposioHQ awesome-claude-skills
元数据
Slug lead-research-assistant-cn
版本 1.0.2
许可证
累计安装 1
当前安装数 1
历史版本数 3
常见问题

Lead Research Assistant Cn 是什么?

销售线索研究助手 | Sales Lead Research Assistant. 识别高质量销售线索 | Identify high-quality sales leads. 分析目标公司、提供联系策略 | Analyze target companies, provide contact strategies... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 720 次。

如何安装 Lead Research Assistant Cn?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install lead-research-assistant-cn」即可一键安装,无需额外配置。

Lead Research Assistant Cn 是免费的吗?

是的,Lead Research Assistant Cn 完全免费(开源免费),可自由下载、安装和使用。

Lead Research Assistant Cn 支持哪些平台?

Lead Research Assistant Cn 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Lead Research Assistant Cn?

由 Guohongbin(@guohongbin-git)开发并维护,当前版本 v1.0.2。

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