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Agent Skill · 研究检索

lead-qualifier(领先预选赛)

lead-qualifier 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。

lead-qualifier 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 7,774。

总安装 18,079周安装 579GitHub Stars 3下载 4,214核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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-qualifier(领先预选赛)
来源仓库:https://github.com/jmsktm/claude-settings
仓库路径:skills/lead-qualifier
安装命令:
npx skills add https://github.com/jmsktm/claude-settings --skill 'Lead Qualifier'
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/jmsktm/claude-settings --skill 'Lead Qualifier'

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

lead-qualifier 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/lead-qualifier

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jmsktm/claude-settings --skill 'Lead Qualifier…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

lead-qualifier 怎么安装?

在终端执行 npx skills add https://github.com/jmsktm/claude-settings --skill 'Lead Qualifier'。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

lead-qualifier 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

lead-qualifier 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

lead-qualifier 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

lead-qualifier 用的人多吗?

公开数据是累计安装 18,079 次,近一周安装 579 次,下载 4,214 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

lead-qualifier 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
lead-qualifier

领先预选赛 · 本页

18,0793只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Lead Qualifier

Transform raw leads into prioritized opportunities with systematic qualification. This skill applies proven frameworks like BANT, MEDDIC, and CHAMP to evaluate leads objectively, ensuring your sales team focuses on prospects most likely to convert.

Stop wasting time on tire-kickers and unqualified prospects. This skill helps you build scoring models, create qualification criteria, and develop consistent processes for evaluating fit, budget, authority, and timing. The result: shorter sales cycles, higher close rates, and more efficient resource allocation.

Ideal for sales teams, SDRs, account executives, and anyone who needs to separate high-potential opportunities from noise.

Core Workflows

Workflow 1: BANT Qualification

  1. Budget Assessment - Determine if prospect has financial resources
  2. Authority Mapping - Identify decision-makers and influencers
  3. Need Verification - Confirm genuine pain points exist
  4. Timeline Discovery - Understand urgency and buying timeframe
  5. Score Calculation - Weight each factor for overall score
  6. Priority Assignment - Categorize as hot, warm, or nurture
  7. Handoff Decision - Route to appropriate sales resource

Workflow 2: Lead Scoring Model Design

  1. Ideal Customer Profile - Define characteristics of best customers
  2. Demographic Scoring - Points for company size, industry, role
  3. Behavioral Scoring - Points for engagement, content consumption
  4. Firmographic Weighting - Prioritize by company attributes
  5. Engagement Thresholds - Set minimum scores for qualification
  6. Score Decay Rules - Reduce scores for inactivity
  7. Model Validation - Test against historical conversion data

Workflow 3: MEDDIC Deep Qualification

  1. Metrics - Quantify the economic impact of your solution
  2. Economic Buyer - Identify who controls the budget
  3. Decision Criteria - Understand evaluation requirements
  4. Decision Process - Map the buying journey and stakeholders
  5. Identify Pain - Uncover compelling business drivers
  6. Champion - Find internal advocate for your solution
  7. Qualification Report - Document findings for deal strategy

Workflow 4: Automated Qualification Workflow

  1. Form Design - Create qualifying questions for lead capture
  2. Progressive Profiling - Gather info across multiple interactions
  3. Enrichment Integration - Add firmographic data automatically
  4. Scoring Triggers - Auto-score based on behavior and data
  5. Routing Rules - Direct leads to right team/process
  6. SLA Management - Set response time requirements
  7. Handoff Automation - Streamline qualified lead delivery

Quick Reference

ActionCommand/Trigger
Score a lead"Qualify this lead using BANT"
Build scoring model"Create lead scoring model for [product]"
Evaluate fit"Assess lead fit for [ICP criteria]"
MEDDIC analysis"Deep qualify this opportunity"
Priority ranking"Rank these leads by likelihood to close"
Qualification questions"Create discovery questions for [industry]"
Disqualification check"What disqualifies a lead for us?"
Score refinement"Adjust scoring model based on [data]"

Best Practices

  • Define ICP first - Know your ideal customer before qualifying anyone
  • Use multiple frameworks - BANT for quick screen, MEDDIC for complex deals
  • Weight recency - Recent engagement matters more than old activity
  • Include negative criteria - Define what disqualifies as clearly as what qualifies
  • Automate where possible - Let technology handle initial scoring
  • Calibrate regularly - Compare predicted vs. actual conversions monthly
  • Document reasoning - Record why leads are qualified/disqualified
  • Set clear thresholds - Specific scores trigger specific actions
  • Train consistently - Everyone should score leads the same way
  • Measure outcomes - Track which qualified leads actually closed
  • Consider timing - Right solution, wrong time still means nurture
  • Map buying committee - Qualify the deal, not just the contact
  • Listen for pain - No pain = no urgency = no deal
  • Validate budget early - Don't invest in opportunities without resources
  • Create feedback loops - Sales insights should refine marketing's scoring

平台分布

Codex

32.95%
按下载量换算1,389

Claude

32.85%
按下载量换算1,384

Cursor

18.19%
按下载量换算767

Gemini CLI

10.51%
按下载量换算443

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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