Compose Outreach

作者 anthropicsae1513ea94dc無授權條款27K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Generate personalized outreach messages using Common Room signals. Triggers on 'draft outreach to [person]', 'write an email to [name]', 'compose a message for [contact]', or any outreach drafting request.

AI 產生的概覽

依據 Common Room 訊號,產生電子郵件、電話話術與 LinkedIn 訊息三種個人化外聯草稿。

功能
此技能會針對目標公司或聯絡人產出三種標示清楚的外聯草稿:含主旨的電子郵件、包含開場、價值銜接與請求的電話話術,以及不超過 300 個字元的 LinkedIn 訊息。它會從 Common Room 訊號中找出個人化切入點,必要時以網路搜尋或 Spark 補充資訊,並附上簡短說明解釋所用的訊號。當訊號資料稀少時,它不會憑空起草,而是向使用者詢問背景資訊。
適用情境
適用於被要求向特定個人或公司起草外聯內容、寫信給指定聯絡人,或為潛在客戶撰寫訊息的情況。適合需要以互動與意向訊號為依據進行個人化的業務或社群驅動型外聯。
執行需求
需要 Common Room MCP 工具進行訊號查詢,可選用 Spark 補充資訊與網路搜尋。它會引用格式指南與公司背景檔案。不附帶指令碼,僅為指示。

Compose Outreach

Generate three personalized outreach formats — email, call script, and LinkedIn message — grounded in Common Room signals for a specific company or contact.

Outreach Process

Step 1: Look Up the Target

Use Common Room MCP tools to find and retrieve data for the target (company and/or specific contact). Pull:

  • Recent product activity and engagement signals
  • Community activity (posts, questions, reactions)
  • 3rd-party intent signals (job postings, news, funding)
  • Relationship history (prior contact, meetings, email opens)

If the user specified a person, run contact-level research. If only a company was given, identify the best contact to target based on title, engagement, and role.

Step 2: Web Search for External Hooks (If CR Signals Are Thin)

If CR returned strong signals (recent activity, engagement, product usage), those should drive personalization — skip web search. If CR signals are thin or the prospect has little CR activity, run a web search for external hooks:

What to search:

  • "[company name]" funding OR acquisition OR launch OR announcement — last 30 days
  • "[contact full name]" "[company name]" — look for recent articles, interviews, LinkedIn posts, or conference talks

Prioritize external hooks that are:

  • Very recent (< 2 weeks) — the prospect is likely still thinking about it
  • Publicly visible — they know you could have seen it
  • Change-signaling — growth, new role, new product, new market

If the user explicitly asks for web search or external hooks, run it regardless of CR signal richness.

Step 3: Spark Enrichment (If Available)

If Spark is available, run enrichment on the target contact to get persona classification, background, and influence signals. Use this to calibrate tone and message angle.

Step 4: Identify the Best Hooks

From the signal data, identify the 1–3 strongest personalization hooks. Rank by:

  1. Recency — happened in the last 7–14 days
  2. Specificity — a concrete action they took, not a general trend
  3. Relevance — connects directly to a value your product delivers

Good hooks: posted a question in the community about X, just hired 5 engineers, recently started using [feature], company just raised Series B, trial nearing expiration, champion just changed jobs.

Bad hooks: "I noticed you're a customer" or generic industry trends.

Step 5: Generate All Three Formats

Use the strongest hooks to write all three formats. Each format has different constraints and conventions — follow the format-specific guidelines in references/outreach-formats-guide.md.

Always produce all three, clearly labeled.

When the user's company context is available (see references/my-company-context.md), ground the value bridge and pitch in the user's specific product and positioning.

Step 6: Annotate Your Choices

After the three drafts, include a brief note (2–4 sentences) explaining:

  • Which signals were used and why they were chosen
  • Any assumptions made (e.g., inferred call objective)
  • Alternative angles if the primary hook doesn't land

Output Format

## Outreach for [Name / Company]
### 📧 Email
**Subject:** [Subject line]
[Email body — 3–5 sentences]
---
### 📞 Call Script
**Opening:**[Opening line — conversational, 1–2 sentences]
**Value Bridge:**[Why you're calling and why now — 2–3 sentences tied to a signal]
**Ask:**[Single, low-friction ask — e.g., 15-minute call, specific question]
---
### 💼 LinkedIn Message
[Under 300 characters. Warm, personal, no pitch.]
---
### Signal Notes[2–4 sentences: which signals were used, why, and any alternative angles]

When Signal Data Is Sparse

If Common Room returns minimal data on the target (e.g., just name, title, tags — no activity, no scores, no Spark):

  1. Do not draft outreach from thin air. Outreach grounded in fabricated signals is worse than no outreach.
  2. Run web search first — this becomes your primary personalization source. Look for recent news, LinkedIn posts, conference talks, company announcements.
  3. If web search also returns little, present what you have honestly and ask the user for context:
## Outreach for [Name / Company] — Limited Data
**What I found:**[Only the real data from CR and web search]
**I don't have enough signal to draft personalized outreach yet.** To write something strong, I'd need:- Recent activity or engagement signals- Context you have from prior conversations- A specific reason for reaching out now
Can you share any of the above?

Quality Standards

  • Every message must reference something specific — generic outreach is not acceptable output
  • Match tone to context: warm and conversational for inbound/community signals; more formal for cold/executive outreach
  • The LinkedIn message must be under 300 characters — no exceptions
  • The call script must be speakable naturally — read it aloud mentally to check rhythm
  • Never fabricate signals — only reference data retrieved from Common Room or web search

Reference Files

  • references/outreach-formats-guide.md — detailed format rules, examples, and tone guidelines for each channel

來源與署名

來源:anthropics/knowledge-work-plugins位於partner-built/common-room/skills/compose-outreach提交ae1513e

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