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Compose Outreach

OrganizationPopular
anthropics
compose-outreach

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.

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill namecompose-outreach
Stars
25.9K
Forks
3K
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Compose Outreach AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/anthropics/knowledge-work-plugins/tree/main/partner-built/common-room/skills/compose-outreach
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/partner-built/common-room/skills/compose-outreach .claude/skills/compose-outreach
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compose Outreach in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Compose Outreach on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Compose Outreach is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Compose Outreach AI skill do?

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.

Why use Compose Outreach on TypingMind?

Because you install it once and use it with any model. Compose Outreach is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Compose Outreach in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/partner-built/common-room/skills/compose-outreach. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Compose Outreach?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Compose Outreach?

As many as you like. As long as a model supports skills, you can use Compose Outreach with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Compose Outreach AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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