Claude Code Workflow Visual: Memory Hierarchy, Skills, Hooks, and Loop
A Reddit user shared a workflow visual that maps out how Claude Code's components fit together: CLAUDE.md, memory hierarchy, skills, hooks, project structure, and the workflow loop.
Memory Layering
The visual clarifies context loading order:
~/.claude/CLAUDE.md→ global memory/CLAUDE.md→ repo context./subfolder/CLAUDE.md→ scoped context
Subfolders append context, not replace it. This explains why sessions can feel overloaded when those files grow large.
Skills
Instead of repeating prompts, define reusable patterns in .claude/skills/. Examples:
.claude/skills/testing/SKILL.md .claude/skills/code-review/SKILL.md
Claude auto-invokes skills when the description matches.
Workflow Loop
The recommended loop:
cd project && claude Plan mode Describe feature Auto accept /compact commit frequently
No single element is groundbreaking, but seeing it all together helps organize Claude Code usage.
The ecosystem is still evolving, and workflows remain personal.
📖 Read the full source: r/ClaudeAI
👀 See Also
Recover deleted Claude Desktop conversations from Chromium cache
Immediately quit Claude Desktop, find the Chromium blockfile cache at %APPDATA%\Claude\Cache\Cache_Data (Windows), then use Python packages ccl_chromium_reader and standard compression libs to extract HTTP response bodies containing your chat UUID.
How OpenCLAW Memory Actually Works: Fixing Agent 'Forgetting'
OpenCLAW agents don't have persistent memory between conversations - they reconstruct context from files like SOUL.md, USER.md, and MEMORY.md each time. Common 'forgetting' issues stem from old sessions, unstructured memory files, and storing important info in chat history instead of permanent files.
Qwen 3.5 122B MoE at 35 t/s on a Single 3090 with ik_llama.cpp MTP
A local stack running Qwen 3.5 122B MoE on a single 3090 at 35 t/s using ik_llama.cpp's fused MoE ops for MTP. Stock llama.cpp showed only +4% improvement; ik's fork yields +20%.
Flow Maps: Learning the Integral of a Diffusion Model for Faster Sampling
Sander Dieleman explains flow maps — neural networks that directly predict the integral of a diffusion model's ODE, enabling faster sampling, reward-based learning, and steerability.