What makes a good AGENTS.md?

· Source: Ben's Bites · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

The article details the optimal use of AGENTS.md and CLAUDE.md files, which are pre-loaded instruction sets for AI models, functioning similarly to system prompts. It clarifies that while CLAUDE.md is specific to Claude products, AGENTS.md works across various tools like Codex/Droid/Pi, and they can be symlinked. Contrary to earlier beliefs, including detailed tech stacks or file maps in these instruction files is detrimental, increasing cost by 20% and hurting performance. Instead, these files should contain user preferences and nudges for agent behavior, such as using specific web search tools, defining planning file locations, or requesting video recordings of output. The article also highlights the utility of conditional blocks for dynamic instruction loading and notes that skill frontmatter is automatically pre-loaded, making explicit mention unnecessary. These instructions also apply to ChatGPT/Claude Desktop app personalization settings.

Key takeaway

For AI Engineers or Prompt Engineers configuring agent behavior, you should streamline your AGENTS.md or CLAUDE.md files. Focus on clear, concise preferences and behavioral nudges, such as specifying tools or output formats, rather than detailing technical environments. This approach will reduce token usage, improve agent performance, and ensure your agents adapt more effectively to different project requirements without unnecessary overhead.

Key insights

Optimize AI agent behavior and cost by providing concise, preference-based instructions rather than detailed technical maps.

Principles

Method

Configure AGENTS.md/CLAUDE.md with user preferences and behavioral nudges. Utilize conditional blocks for context-specific instructions. Do not include detailed tech stacks or skill lists, as these are often automatically handled or inferred.

In practice

Topics

Code references

Best for: AI Engineer, Prompt Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ben's Bites.