Structuring Claude Code Projects with CLAUDE.md, Skills, and MCP

Workflow Improvements for Claude Code
A developer on r/ClaudeAI shared practical approaches to structuring Claude Code projects that have improved their workflow. The key methods focus on systematic approaches rather than isolated prompts.
Plan Mode First
Starting in plan mode instead of immediate execution makes a significant difference. By writing the goal clearly and letting Claude break it into steps first, you can catch gaps early. Reviewing the plan before running anything saves time and results in cleaner output with fewer edits.
CLAUDE.md as Project Memory
Using a CLAUDE.md file properly serves as long-term project memory. The developer recommends including:
- Project structure
- Coding style preferences
- Common commands
- Naming conventions
- Constraints
Once this file is solid, you stop repeating context and outputs become more consistent across sessions.
Reusable Skills
Skills are powerful for recurring tasks. If you often ask Claude to:
- Format output in a specific way
- Review code with certain rules
- Summarize data using a fixed structure
You can package that logic once and reuse it. This removes friction and keeps quality stable.
MCP for Tool Integration
MCP (Model Context Protocol) is another layer worth exploring. Connecting Claude to tools like GitHub, Notion, or local CLI scripts changes how you think about it. Instead of copying data back and forth, you operate across tools directly from the terminal, making automation feel practical.
The developer notes that the biggest mindset shift is that Claude Code works best when you design small systems around it, not isolated prompts.
📖 Read the full source: r/ClaudeAI
👀 See Also

Access GPT-5.4 via Codex subscription in OpenClaw
A Reddit post details how to configure OpenClaw to use GPT-5.4 through an OpenAI Codex subscription by modifying the openclaw.json configuration file and restarting the gateway.

Optimizing Qwen 3.6 27B/35B on RTX 3090: Flags, Quantization, and Auto-Routing
A user shares his llama-server flags for Qwen 3.6 27B and 35B GGUF models on an RTX 3090 (24GB), reporting slow speeds for the 35B and unreliable code output from the 27B. The post asks for better quant, flag tuning, and auto model switching.

OpenClaw Memory Management: Complete Guide

Four Common Setup Mistakes That Make People Quit OpenClaw
A Reddit user reports seeing over 50 people quit OpenClaw due to four specific setup issues: missing SOUL.md files, excessive API costs from using Opus model for everything, installing too many skills at once, and creating multiple agents before the first one works properly.