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lead-research-assistant首席研究助理

Agent Skill

lead-research-assistant 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,004

周安装

41

GitHub Stars

5

下载量

325
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:lead-research-assistant(首席研究助理)
来源仓库:https://github.com/breverdbidder/life-os
仓库路径:skills/lead-research-assistant
安装命令:
npx skills add https://github.com/breverdbidder/life-os --skill lead-research-assistant
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/breverdbidder/life-os --skill lead-research-assistant

简介

系统性寻找潜在客户并分析市场机会,支持行业细分和决策人定位。

  • 适用于产品推广、竞争分析和早期用户招募等场景。lead-research-assistant 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用前需明确产品定位和目标人群,避免无效触达和资源浪费。
  • 输出结果应标注数据来源和联系方式,禁止虚构企业或联系人信息。
  • 建议结合 CRM 工具管理线索,定期更新状态和跟进计划。

SKILL.md

Lead Research Assistant

This skill helps find and qualify potential customers for your product or service through systematic research and analysis.

When to Use This Skill

Use this skill when you need to:

  • Find companies that would benefit from your product
  • Identify decision makers and their contact information
  • Research specific market segments or industries
  • Build targeted prospect lists
  • Plan outreach strategies
  • Qualify leads based on criteria
  • Research competitor customers
  • Find early adopters or beta users

Research Workflow

1. Understand the Product

First, analyze what you're selling:

  • Read product description/repository
  • Understand core value proposition
  • Identify key features and benefits
  • Determine ideal customer profile (ICP)
  • Note technical requirements or integrations

2. Define Target Criteria

Establish search parameters:

  • Industry: Which sectors benefit most?
  • Company size: Startups, SMB, Enterprise?
  • Geography: Location requirements?
  • Technographics: What tech stack do they use?
  • Budget indicators: Funding, revenue, growth stage?
  • Pain points: What problems do they face?

3. Research Strategy

Use multiple research approaches:

Company Discovery:

  • Industry-specific searches
  • Tech stack searches (e.g., "companies using Cursor AI")
  • Recent funding announcements (Crunchbase, TechCrunch)
  • LinkedIn company searches
  • Y Combinator batches
  • Product Hunt launches
  • Conference attendee lists
  • Industry associations

Decision Maker Identification:

  • LinkedIn profile searches
  • Company org charts
  • Apollo.io or similar databases
  • GitHub contributor searches (for dev tools)
  • Twitter/X for thought leaders
  • Industry forum moderators

4. Lead Qualification Scoring

Score each lead on:

  • Fit Score (1-10): How well do they match ICP?
  • Intent Score (1-10): How likely are they to buy?
  • Priority (High/Medium/Low): Overall ranking

Fit Criteria:

  • Uses complementary tools (+2)
  • In target industry (+2)
  • Right company size (+2)
  • Geographic fit (+1)
  • Recent growth indicators (+2)
  • Budget signals (+1)

Intent Criteria:

  • Recent funding (+3)
  • Hiring for relevant roles (+2)
  • Posted about related problems (+3)
  • Active in community (+1)
  • Recent tech stack changes (+1)

5. Information Gathering

For each qualified lead, collect:

Company Information:

  • Company name and website
  • Industry and sector
  • Employee count
  • Location (HQ and offices)
  • Funding status and amount
  • Tech stack
  • Recent news or milestones

Decision Maker Details:

  • Name and title
  • LinkedIn profile URL
  • Email (if available)
  • Twitter/X handle
  • Recent activity or posts
  • Shared connections

Context & Insights:

  • Why they're a good fit
  • Specific pain points your product solves
  • Recent company changes or needs
  • Mutual connections or warm intro paths
  • Relevant content they've shared

6. Outreach Strategy

For each lead, suggest:

Messaging Approach:

  • Personalization hooks (recent posts, company news)
  • Value proposition specific to their situation
  • Relevant case studies or social proof
  • Call-to-action (demo, trial, conversation)

Conversation Starters:

  • Reference specific pain points
  • Mention mutual connections
  • Comment on their recent work
  • Share relevant insights
  • Offer immediate value

Timing Considerations:

  • Best time to reach out
  • Seasonal factors
  • Company fiscal calendar
  • Product launch cycles

Output Format

Structure lead research as:

# Lead Research Results

## Overview
- Total leads found: [X]
- High priority: [Y]
- Medium priority: [Z]
- Industries covered: [list]

## High Priority Leads

### 1. [Company Name]
**Fit Score**: 9/10 | **Intent Score**: 8/10 | **Priority**: HIGH

**Company Details**:
- Industry: [industry]
- Size: [employees]
- Location: [city, country]
- Funding: [amount/stage]
- Website: [url]

**Why Good Fit**:
- [Specific reason 1]
- [Specific reason 2]
- [Specific reason 3]

**Decision Maker**:
- Name: [Full Name]
- Title: [Job Title]
- LinkedIn: [URL]
- Email: [if found]

**Outreach Strategy**:
- **Hook**: [Personalization element]
- **Value Prop**: [Specific benefit for them]
- **CTA**: [Suggested ask]

**Conversation Starter**:
"Hi [Name], noticed [specific observation]. We help companies like [theirs] [specific value]. Would [specific outcome] be valuable for your team?"

---

[Repeat for each high-priority lead]

Research Sources

Free Sources

  • LinkedIn (company pages, people search)
  • GitHub (contributor activity, org repositories)
  • Twitter/X (company mentions, decision maker posts)
  • Crunchbase (basic funding data)
  • Product Hunt (new product launches)
  • HackerNews (who's hiring, show HN)
  • Company blogs and press pages
  • Industry publications

Paid Sources (if available)

  • Apollo.io (B2B contact database)
  • ZoomInfo (company & contact data)
  • LinkedIn Sales Navigator
  • Crunchbase Pro
  • BuiltWith (tech stack data)
  • SimilarWeb (traffic data)

Domain-Specific Strategies

For Developer Tools

Target:

  • Engineering managers on LinkedIn
  • GitHub users in relevant ecosystems
  • Stack Overflow contributors
  • DevTool Twitter communities
  • Engineering blogs
  • Tech conference speakers

For B2B SaaS

Target:

  • Department heads (VP Sales, VP Eng, etc.)
  • Companies in growth stage
  • Recent funding announcements
  • Job posts indicating expansion
  • LinkedIn groups for industry

For Real Estate Tech

Target:

  • Real estate investors and fund managers
  • Property management companies
  • Real estate brokerages
  • REITs and institutional investors
  • Real estate tech adopters
  • Industry conference attendees

For AI/ML Tools

Target:

  • Companies hiring ML engineers
  • Y Combinator AI companies
  • Users of complementary AI tools
  • AI research labs
  • Companies with AI in job descriptions
  • AI Twitter/X community

Quality Standards

Ensure each lead entry:

  • ✅ Has verifiable company information
  • ✅ Includes decision maker name and title
  • ✅ Explains why they're a good fit
  • ✅ Provides concrete outreach strategy
  • ✅ Includes at least one contact method
  • ✅ Has accurate fit/intent scoring
  • ✅ Contains actionable next steps

Avoid:

  • ❌ Generic "spray and pray" lists
  • ❌ Outdated contact information
  • ❌ Unclear value propositions
  • ❌ Missing decision maker details
  • ❌ No personalization hooks

Examples

Example 1: Developer Tool

Product: AI-powered foreclosure auction analysis platform

Research Strategy:

  • Search: "real estate investment companies Brevard County"
  • Search: "property investors Florida foreclosures"
  • LinkedIn: Real estate fund managers in Florida
  • Search: "real estate wholesalers central Florida"

Sample Lead:

  • Company: Sunshine State Capital
  • Decision Maker: John Smith, Managing Partner
  • Fit: 9/10 - Active foreclosure investor, 50+ properties
  • Hook: Recent LinkedIn post about deal analysis challenges
  • Value Prop: Automated lien discovery saves 5-10 hours per auction

Example 2: SaaS Tool

Product: Project management for AI developers

Research Strategy:

  • Y Combinator W25 AI companies
  • Companies using Cursor AI (via job posts)
  • "AI startup" + "hiring" searches
  • GitHub organizations with AI repos

Sample Lead:

  • Company: VectorAI Labs
  • Decision Maker: Sarah Chen, CTO
  • Fit: 8/10 - 15 engineers, AI-first company, using Cursor
  • Hook: Just raised Series A, hiring 10 engineers
  • Value Prop: Built specifically for AI development workflows

Tips for Success

  1. Start Broad, Then Narrow: Cast wide net initially, then filter rigorously
  2. Verify Information: Double-check contact details and job titles
  3. Find Warm Paths: Look for mutual connections or shared communities
  4. Timely Research: Prioritize leads with recent trigger events
  5. Batch Similar Leads: Group by industry or use case for efficient outreach
  6. Track Results: Note which research methods yield best leads
  7. Refresh Regularly: Update lead list as situations change
  8. Respect Privacy: Use publicly available information ethically

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.61%
按下载量换算109

Claude

29.79%
按下载量换算97

Cursor

17.69%
按下载量换算57

Gemini CLI

9.58%
按下载量换算31

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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