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.
Install / Use
npx skills add anthropics/knowledge-work-plugins --skill compose-outreachInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
CommunicationSupported Platforms
Our assessment of compose-outreach
compose-outreach scores 93/100 on our quality scale, 48th of 170 Communication skills we index (top 29%).
Its SKILL.md is 5.3 KB long, well organised into 18 sections with 2 code examples: a solid amount of guidance for an agent.
With 25,526 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated yesterday, so compose-outreach is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
compose-outreach compared with similar skills
All 4 of these similar skills score higher than compose-outreach; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| compose-outreach (this skill)by anthropics | 93 | 25.5k | 1d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.4k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.8k | today | CLAUDE.md |
| rufloby ruvnet | 100 | 73.3k | 1d ago | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.1k | today | CLAUDE.md |
Frequently asked questions
- How do I install compose-outreach?
- Run
npx skills add anthropics/knowledge-work-plugins --skill compose-outreach. The install tabs above show the steps for each supported agent. - Which AI agents does compose-outreach work with?
- It is written for Zed, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is compose-outreach safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is compose-outreach still maintained?
- The repository was last updated yesterday, so compose-outreach is actively maintained.
Skill content
View source on GitHubname: compose-outreach description: "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."
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:
- Recency — happened in the last 7–14 days
- Specificity — a concrete action they took, not a general trend
- 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):
- Do not draft outreach from thin air. Outreach grounded in fabricated signals is worse than no outreach.
- Run web search first — this becomes your primary personalization source. Look for recent news, LinkedIn posts, conference talks, company announcements.
- 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
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
