# Set Up Workflow: Orchestrating Community Management and Content Creation with Claude Code
## What This Is
This guide outlines an advanced workflow for automating community management and social media marketing using Claude Code. It connects community feedback analysis, content creation, and multi-platform publishing into a single, automated process, saving time by eliminating the need to manage many separate tools.
Source: https://www.eesel.ai/blog/claude-code-best-practices
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://www.eesel.ai/blog/claude-code-best-practices) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.
- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files
## Additional Context
- Create a `CLAUDE.md` file in the project root. Populate it with the project's architecture, key libraries, a list of slash commands, and coding standards to establish a persistent context for all subsequent tasks.
- Configure an MCP (Multi-tool Capability Protocol) server to integrate with external APIs. Define functions to fetch data from community platforms (e.g., Discord, Discourse) and to interact with content scheduling tools.
- Develop a headless automation script that uses the MCP server to retrieve insights, draft content using predefined templates, and publish it to target platforms (e.g., Substack, X, Bluesky) by handling authentication and platform-specific formatting.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it