Making AI Coding Agents More Efficient: My Local Toolkit

Giving AI coding agents more tools doesn’t necessarily make them more efficient.

After using OpenCode and Claude Code heavily, I started focusing less on adding capabilities and more on controlling the information flowing into the agent.

That means:

• searching before reading entire files
• choosing between text, structural, and semantic search
• reducing noisy command output
• keeping sessions focused
• defining explicit stopping rules

I’ve documented the toolkit I currently use, including Headroom, RTK, Caveman, Serena, rg, fd, ast-grep, jq, yq, and others, along with how I define their responsibilities in AGENTS.md.

The principle behind all of it is simple:

Give the agent the smallest amount of information necessary to make the next correct decision.

Full post:
https://ammar-najjar.com/blog/local-ai-coding-toolkit/