Claude Code folder structure cheat sheet from Reddit user

A Reddit user shared a cheat sheet for Claude Code's folder structure after repeatedly getting tripped up by documentation that required jumping between multiple pages. The cheat sheet covers practical implementation details that caused real time-wasting issues.
Key folder structure details
- Skills belong in
.claude/skills/, not a top-levelskills/folder - Each skill needs its own directory with an
SKILL.mdfile inside it - Subagents live in
.claude/agents/, not a standaloneagents/folder at the root
Hook configuration specifics
- For PostToolUse hooks, the matcher needs to be
"Edit|MultiEdit|Write"— using just"Write"misses edits - SessionStart and SessionEnd are real hook events (contrary to some community discussions)
Installation and documentation notes
npm installis no longer the recommended install path- The native installer is:
curl -fsSL https://claude.ai/install.sh | bash - Documentation updates happen quietly, so the cheat sheet may contain inaccuracies as docs evolve
The cheat sheet is currently available as an image, though the creator mentioned potentially converting it to a proper Markdown file if there's sufficient interest. The image format was chosen for faster initial creation.
📖 Read the full source: r/ClaudeAI
👀 See Also

Stop Asking Which AI Model to Use: Route Tasks to Haiku, Sonnet, and Opus Tiers
Use at least three models by task type: Haiku-tier for reading/summarizing, Sonnet-tier for writing code, and Opus-tier only for multi-file refactors and debugging. One user's setup routes 40% to cheap models, 35% to mid, 25% to frontier, costing ~$30-40/month.

Practical Guide to Self-Hosting Your First LLM
A Reddit post outlines reasons for self-hosting LLMs including privacy for sensitive data, cost predictability for agent workloads, performance improvements by removing API roundtrips, and customization through fine-tuning methods like LoRA and QLoRA.

OpenClaw Ollama Cloud: Three-Layer Fix for Missing Models and Doctor Delete Bug
A clean install of OpenClaw with Ollama Cloud models failed: only kimi-k2.5 worked, configs disappeared. Root cause: providers list missing, name field required, and 'openclaw doctor --fix' deletes your provider block.

12GB VRAM Benchmarks: Running Qwen 3.6 and Gemma 4 Models on a RTX 4070 Super
A Reddit user shares detailed speed benchmarks for Qwen3.6-35B-A3B, Qwen3.6-27B, Gemma 4 26B, and Gemma 4 31B on a 12GB RTX 4070 Super using llama.cpp with optimized settings.