· AI  · 2 min read

Notes on Using Claude: Tips & Architecture

Key takeaways and best practices for using Claude and multi-agent architectures, including prompt design, skills, and error handling.

Key takeaways and best practices for using Claude and multi-agent architectures, including prompt design, skills, and error handling.

AI landscape is quickly moving, as well as Claude is rapidly evolving.

This is a note to catch up as to how better we can use Claude.

Since these could be quickly updated, I will go with bullet point rather than sentences.

Take away bullet points:

  • description What + When + Negative trigger

  • negative tigger: prefer “Do NOT use for…”

  • skills description: MAX 1024 characters

  • SKILL.md length: MAX 5000 words

  • Subagents does NOT share the memory with the coordinator; It’s in an isolated context, only information is given explicitly in the propmpt

  • Do NOT rely on prompt; You have to enforce mission or secrurity critical rules via hooks and prerequisite gates

  • Ref Agent SDK Python repo + examples

  • With MCP, when selecting a tool, descriptions are the primary trigger for Clause to decide which ones to pick; better descriptions are the key

  • For each subagent, scope the number of tools by giving only 4 - 5 MCP tools for the task; otherwise decision making process would degrade

  • instruction hierachy:

    • ~/.claude/CLAUDE.md: top/user level
    • .claude/CLAUDE.md: project level
    • yourproject/subfolder/CLAUDE.md: directory level
  • be mindful, ”~/.claude/CLAUDE.md” cannot be shared via github repo automatically

  • path-specific rules: rather than directory or project base, it can be applied for specific file or path

globs:
  - "**/*.test.tsx"

instructions: |
  Use React Testing Library.
  Avoid enzyme.
  • when reviewing code, independent session is better than doing it continuously in the same session where it wrote the code
  • be explicit: “be conservative” or “give me high-confidence finding” does not decrease low quality or false positive
  • give it 2 - 4 example: this wouls increase ouput quality
  • do not summarize long context, isorate important facts and findings and attach it every prompt; longer the context, blurer their attenstion
  • key facts and datas should be at the begenning, not the middle
  • be mindful error validation; give explicit, concreate steps to propagate error, else risk skipping the errors
  • design and architect for a human escaration process; when a chat agent is dealing with a customer, do not rely on ai’s self-report or some subjective criterior to trigger human agent; rely on an explicit, objective trigger like “request human agent” button, or “policy”, or “infinite loop”

References:

I want to become a Claude architect yohey-w / multi-agent-shogun Copare Shogun Skills Architecture with Clause Official Skills Guide (Japenese)

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