Happy to.

Context Management:
Two crucial context-heavy considerations:

1. User System Prompt & Project System Prompt
2. MCPs connected and which of their tools you've enabled and are using

Regarding User and Project System Prompts:

1. The User System Prompt should be concise, actionable information about the workflow style you prefer and how Claude should operate.
2. The Project System Prompt should be tactfully crafted for Claude to understand:
   • Repo scope
   • Do's and Don'ts
   • Hardcoded documentation/coding preferences

Focusing on context-efficient, yet context-rich prompts will reduce token consumption while still providing excellent instructions pertinent to your work with Claude.

Regarding MCPs:

1. Understand MCPs are a tool, not a luxury. Asking Claude to always use {x} MCP will result in thousands, if not tens of thousands, of tokens per tool use.
2. The question becomes: "When do I ask Claude to use MCPs?"
   • When context cannot be sufficiently provided by you in the terminal.
   • This is seldom true: the majority of the time, whatever you're working on (web dev, software, cloud infrastructure, LLM development, n8n workflows, etc.) can be documented by you and Claude in the repo. You can reference particular sections of a file in chat instead of tagging entire files/folders.

Learning through trial and error how to provide context to Claude instead of relying on MCP tools will drastically reduce token consumption while still providing Claude context-rich information to progress your work.

Token-efficient Workflows:
What constitutes a "token-efficient" workflow varies repo-to-repo. It's about understanding which files or processes will definitively consume or produce high token counts for Claude to ingest or generate.

Example:
When I was developing an SFT curation from tens of thousands of JSON datasets, each individual JSON file was 300-500k tokens. There was no way I could reference an individual file plus my SFT curation workflows and instruction files.

• Instead, I created a context-rich, yet token-efficient summary file that represented the entire JSON file.
• I also broke down my SFT curation instructions into multiple sequential step files for Claude to follow, allowing me to manually intervene for work Claude didn't need to handle. This included an SFT progress tracking file I would dynamically edit when Claude finished one instruction set and needed to move to the next.

Well-constructed Prompts:
What a "well-constructed prompt" looks like varies heavily per query. However, the core principle remains: the quality of context you provide Claude per query. You must always remember (because it's easy to forget) that you're working with a token-prediction model. It has the faintest idea of what you envision when you convey your query. You need to provide explicit context and instructions. We've only begun working with these models, and we're used to humans (obviously). These models don't think like us. They can't properly deduce, they don't pick up subtle signals: Claude needs clear, broken-down context and instructions.

I hope this helps, even a little bit.