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帮助用户智能挖掘高价值潜在客户并生成多渠道触达方案
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "lead-intelligence" 技能： 1. 下载 https://raw.githubusercontent.com/affaan-m/ECC/main/skills/lead-intelligence/SKILL.md 2. 保存为 ~/.claude/skills/lead-intelligence/SKILL.md 3. 装好后重载技能，告诉我可以用了
请帮我为一款面向中型电商公司的客服自动化 SaaS 筛选 50 位高价值潜在客户，优先选择北美市场的运营负责人、客服负责人或数字化负责人，并按匹配度排序，说明入选原因。
一份按优先级排序的潜在客户名单，包含职位、公司、匹配理由和推荐跟进顺序。
基于以下潜在客户信息，分别生成适合邮件、LinkedIn 私信和 X 私信的首次触达内容。语气要专业、简洁、自然，并结合对方近期动态和公司背景做个性化表达，避免过度推销。
针对不同渠道分别生成的个性化触达文案，并体现相关背景信号与沟通语气差异。
请分析我与这批目标客户之间可能存在的暖介绍路径，优先考虑共同联系人、共同公司经历、投资关系或公开互动记录，并为每位客户给出最可行的切入方式。
每位目标客户的暖介绍机会清单，以及推荐的最佳破冰路径与原因。
Agent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.
web_search_exa)X_BEARER_TOKEN, plus write-context credentials such as X_CONSUMER_KEY, X_CONSUMER_SECRET, X_ACCESS_TOKEN, X_ACCESS_TOKEN_SECRET)┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐
│ 1. Signal │────>│ 2. Mutual │────>│ 3. Warm Path │────>│ 4. Enrich │────>│ 5. Outreach │
│ Scoring │ │ Ranking │ │ Discovery │ │ │ │ Draft │
└─────────────┘ └──────────────┘ └─────────────────┘ └──────────────┘ └─────────────────┘
Do not draft outbound from generic sales copy.
Run brand-voice first whenever the user's voice matters. Reuse its VOICE PROFILE instead of re-deriving style ad hoc inside this skill.
If live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo/site material available.
Search for high-signal people in target verticals. Assign a weight to each based on:
| Signal | Weight | Source |
|---|---|---|
| Role/title alignment | 30% | Exa, LinkedIn |
| Industry match | 25% | Exa company search |
| Recent activity on topic | 20% | X API search, Exa |
| Follower count / influence | 10% | X API |
| Location proximity | 10% | Exa, LinkedIn |
| Engagement with your content | 5% | X API interactions |
# Step 1: Define target parameters
target_verticals = ["prediction markets", "AI tooling", "developer tools"]
target_roles = ["founder", "CEO", "CTO", "VP Engineering", "investor", "partner"]
target_locations = ["San Francisco", "New York", "London", "remote"]
# Step 2: Exa deep search for people
for vertical in target_verticals:
results = web_search_exa(
query=f"{vertical} {role} founder CEO",
category="company",
numResults=20
)
# Score each result
# Step 3: X API search for active voices
x_search = search_recent_tweets(
query="prediction markets OR AI tooling OR developer tools",
max_results=100
)
# Extract and score unique authors
For each scored target, analyze the user's social graph to find the warmest path.
social-graph-ranker model to score bridge value| Factor | Weight |
|---|---|
| Number of connections to targets | 40% — highest weight, most connections = highest rank |
| Mutual's current role/company | 20% — decision maker vs individual contributor |
…
通过双评审智能体对结果进行对抗式校验，提升输出发布前的可靠性
用自然语言自动完成 Apollo 线索挖掘、联系人丰富与外联名单构建。