linkedin-research-post

LinkedIn Research Post

You help the user research a topic and publish a LinkedIn post backed by multi-source research. The full pipeline: Research -> Analyze -> Draft -> Review -> Publish.

Prerequisites - VERIFY BEFORE STARTING

Before launching any agents, run these checks:

1. AnySite MCP canary check: Call mcp__anysite-mcp__duckduckgo_search with query "test" and count 1. If it returns results, AnySite is connected. If it errors, tell the user:

• Claude Desktop: Customize > Connectors > AnySite > Connect
• CLI: claude mcp add anysite -s user -- npx -y @anthropic/anysite-mcp
• Signup: anysite.io - promo code BAYRAMMCP for 30 days free

DO NOT launch research agents until this check passes.

2. Voice profile: Check for voice/style in this order:

1. SOUL.md in project root or home directory
2. user-profile.md in project root
3. Memory files via recall
4. If nothing found, ask the user: "How would you describe your writing style in 1 sentence?"

3. Unipile MCP (optional - check only at publish time):

• Signup: unipile.com - 7-day free trial, no credit card required
• Config: claude mcp add-json unipile '{"type":"http","url":"https://developer.unipile.com/mcp","headers":{"X-API-KEY":"YOUR_API_KEY"}}'

Workflow

Step 1: Get the Topic

If the topic was provided via $ARGUMENTS, use it directly.

Otherwise ask: "What topic should your LinkedIn post be about?"

If too broad, narrow it:

• "What specific angle or insight do you want to share?"
• "Who is your audience on LinkedIn?"

Step 2: Research (2-3 min)

Launch 3 parallel sub-agents. Each agent prompt MUST include the exact MCP tool names below. Do NOT say "use AnySite MCP" - agents don't know what that means.

CRITICAL: Include these exact instructions in each agent prompt:

Sub-agent 1 - LinkedIn perspective:

Research "[TOPIC]" on LinkedIn.

You MUST use these exact MCP tools (not WebSearch, not WebFetch):
- mcp__anysite-mcp__search_linkedin_posts(keywords="[TOPIC]", count=10, date_posted="past-week")
- mcp__anysite-mcp__get_linkedin_post_comments(urn="[post_urn]", count=5) for top posts

Find the 5 most engaged posts. For each note:
- Author name and headline
- Post text (first 200 chars)
- Reaction count and comment count
- The angle/take they used

Summarize: what angles get traction, what's overdone, what's missing.

PRIORITY: Try mcp__anysite-mcp tools first. If they fail, fall back to WebSearch/WebFetch.
At the end of your report, note which tools you actually used (MCP vs fallback).