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demand-gen-manager需求生成经理

Agent Skill

demand-gen-manager 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

582

周安装

25

GitHub Stars

103

下载量

204
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

3

许可证

MIT

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:demand-gen-manager(需求生成经理)
来源仓库:https://github.com/borghei/claude-skills
仓库路径:skills/demand-gen-manager
安装命令:
npx skills add https://github.com/borghei/claude-skills --skill demand-gen-manager
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/borghei/claude-skills --skill demand-gen-manager

简介

demand-gen-manager 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。

  • 它提供专家级需求生成管理,涵盖线索获取、campaign 管理和漏斗优化,适用于 B2B 销售 pipeline 增长场景。
  • 使用方式包括建立需求漏斗模型、定义 MQL/SAL 标准和优化转化率,支持 A/B 测试和渠道 mix 调整。
  • 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等操作。
  • 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。

SKILL.md

Demand Generation Manager

Expert-level demand generation for pipeline growth.

Core Competencies

  • Lead generation
  • Campaign management
  • Marketing automation
  • Funnel optimization
  • Account-based marketing
  • Channel management
  • Attribution modeling
  • Revenue marketing

Demand Generation Framework

Demand Funnel

AWARENESS
├── Website visitors
├── Social reach
└── Ad impressions
       │
       ▼
INTEREST
├── Content engagement
├── Email subscribers
└── Event registrants
       │
       ▼
CONSIDERATION
├── MQLs (Marketing Qualified Leads)
├── Content downloads
└── Demo requests
       │
       ▼
INTENT
├── SALs (Sales Accepted Leads)
├── Pricing page visits
└── Free trial starts
       │
       ▼
PURCHASE
├── SQLs (Sales Qualified Leads)
├── Opportunities
└── Closed Won

Funnel Metrics

StageMetricBenchmarkTarget
AwarenessVisitors-100K/mo
InterestEngaged10%15%
MQLConversion5%8%
SALAcceptance60%75%
SQLConversion40%50%
WonWin Rate25%30%

Campaign Management

Campaign Planning Template

# Campaign Plan: [Campaign Name]

## Overview
- Campaign Type: [Webinar/Content/Event/ABM]
- Launch Date: [Date]
- Duration: [Weeks]
- Budget: $[Amount]

## Objectives
- Primary: [Metric] - [Target]
- Secondary: [Metric] - [Target]

## Target Audience
- Segment: [Description]
- ICP fit: [Criteria]
- List size: [Number]

## Offer
- Value proposition: [What they get]
- CTA: [Action to take]
- Landing page: [URL]

## Channels
| Channel | Budget | Target | CPA Goal |
|---------|--------|--------|----------|
| Email | $X | Y leads | $Z |
| Paid Social | $X | Y leads | $Z |
| Paid Search | $X | Y leads | $Z |

## Timeline
| Week | Activity |
|------|----------|
| -2 | Creative development |
| -1 | Asset finalization |
| 0 | Launch |
| 1-4 | Optimization |
| 5 | Analysis |

## Assets Required
- [ ] Landing page
- [ ] Email sequence (X emails)
- [ ] Ad creative (X variants)
- [ ] Content piece
- [ ] Follow-up sequence

## Success Metrics
- Leads: [Target]
- MQLs: [Target]
- Pipeline: $[Target]
- Cost per lead: $[Target]

Campaign Types

Content Campaigns:

  • Ebooks/Whitepapers
  • Research reports
  • Webinars
  • Video series

Event Campaigns:

  • Hosted events
  • Conferences
  • Roadshows
  • Virtual events

ABM Campaigns:

  • Account-specific content
  • Executive outreach
  • Custom experiences
  • Multi-touch sequences

Nurture Campaigns:

  • Onboarding sequences
  • Re-engagement
  • Cross-sell/upsell
  • Lifecycle marketing

Lead Scoring

Scoring Model

LEAD SCORE = Demographic Score + Behavioral Score

DEMOGRAPHIC (Fit) - Max 50 points
├── Company size
│   ├── Enterprise (1000+): 15
│   ├── Mid-market (100-999): 10
│   └── SMB (<100): 5
├── Industry
│   ├── Target industry: 10
│   ├── Adjacent industry: 5
│   └── Other: 0
├── Title/Role
│   ├── Decision maker: 15
│   ├── Influencer: 10
│   └── User: 5
└── Geography
    ├── Primary market: 10
    └── Secondary market: 5

BEHAVIORAL (Interest) - Max 50 points
├── Website activity
│   ├── Pricing page: 15
│   ├── Product pages: 5 each (max 15)
│   └── Blog: 2 each (max 10)
├── Content engagement
│   ├── Gated download: 10
│   ├── Webinar attend: 10
│   └── Video view: 5
├── Email engagement
│   ├── Open: 1
│   └── Click: 3
└── Direct request
    └── Demo/Contact: 25

MQL THRESHOLD: 60 points

Lead Lifecycle

NEW → ENGAGED → MQL → SAL → SQL → OPPORTUNITY → CUSTOMER

Definitions:
- NEW: Known contact, no engagement
- ENGAGED: Has taken meaningful action
- MQL: Meets score threshold
- SAL: Sales accepted, working
- SQL: Sales qualified, opportunity
- OPPORTUNITY: Active deal
- CUSTOMER: Closed won

Marketing Automation

Automation Workflows

Lead Nurture:

Trigger: Downloads ebook
├── Day 0: Thank you email + resource
├── Day 3: Related content
├── Day 7: Case study
├── Day 14: Webinar invite
└── Day 21: Demo offer

Exit conditions:
- Requests demo
- Unsubscribes
- Becomes MQL

Re-engagement:

Trigger: No activity 60 days
├── Day 0: "We miss you" + new content
├── Day 7: Product update
├── Day 14: Special offer
└── Day 21: Final attempt

Exit conditions:
- Engages with any content
- Unsubscribes

Email Performance Benchmarks

MetricPoorAverageGoodExcellent
Open Rate<15%15-20%20-25%>25%
Click Rate<1%1-2%2-4%>4%
CTR<5%5-10%10-15%>15%
Unsubscribe>1%0.5-1%0.2-0.5%<0.2%

Account-Based Marketing

ABM Program Structure

TIER 1: Strategic (1:1)
├── Account count: 10-25
├── Investment: High
├── Personalization: Full custom
└── Tactics: Executive events, custom content

TIER 2: Scale (1:Few)
├── Account count: 50-200
├── Investment: Medium
├── Personalization: Segment-based
└── Tactics: Industry content, targeted ads

TIER 3: Programmatic (1:Many)
├── Account count: 500+
├── Investment: Low per account
├── Personalization: Light
└── Tactics: Intent-based targeting

ABM Metrics

MetricDefinitionTarget
Account Engagement% of accounts engaged70%+
Contact Coverage% of buying committee reached50%+
Pipeline Created$ from target accounts$X
Deal VelocityDays in pipelineReduce by 20%

Attribution & Reporting

Attribution Models

First Touch: 100% to first interaction Last Touch: 100% to last interaction before conversion Linear: Equal credit across all touches Time Decay: More credit to recent touches W-Shaped: 30% first, 30% MQL, 30% opportunity, 10% other

Demand Gen Dashboard

┌─────────────────────────────────────────────────────────────┐
│              Demand Gen Performance - [Month]                │
├─────────────────────────────────────────────────────────────┤
│  Leads         MQLs           Pipeline        Revenue        │
│  5,432         823            $2.4M           $580K          │
│  +15% MoM      +22% MoM       +18% MoM        +12% MoM       │
├─────────────────────────────────────────────────────────────┤
│  Channel Performance                                         │
│  Email: 2,100 leads ($15 CPL)                               │
│  Paid Search: 1,500 leads ($45 CPL)                         │
│  Social: 1,200 leads ($38 CPL)                              │
│  Organic: 632 leads ($0 CPL)                                │
├─────────────────────────────────────────────────────────────┤
│  Campaign Performance                                        │
│  Q4 Webinar Series: 425 MQLs, $890K pipeline                │
│  Ebook Campaign: 312 MQLs, $650K pipeline                   │
└─────────────────────────────────────────────────────────────┘

Reference Materials

  • references/campaign_playbook.md - Campaign execution guide
  • references/lead_scoring.md - Scoring methodology
  • references/automation.md - Marketing automation patterns
  • references/abm_guide.md - ABM program guide

Scripts

# Campaign performance analyzer
python scripts/campaign_analyzer.py --campaign "Q4 Webinar"

# Lead scoring calculator
python scripts/lead_scoring.py --leads leads.csv --model scoring_model.yaml

# Attribution reporter
python scripts/attribution.py --model w-shaped --period Q4

# Funnel analyzer
python scripts/funnel_analyzer.py --data funnel_data.csv

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能力概览

能力 1

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能力 2

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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

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能力 5

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

Claude Code

26.78%
按下载量换算55

OpenCode

24.24%
按下载量换算49

Gemini CLI

16.55%
按下载量换算34

Antigravity

11.99%
按下载量换算24

github-copilot

6.54%
按下载量换算13

Cursor

3.1%
按下载量换算6

安全审计

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Snyk

通过

权限和风险

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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。

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