How We're Solving Context Window Bloat in an AI Agent Skill Ecosystem

Your AI agent just got its 53rd skill installed. Image generation, video creation, social media posting, calendar management — the works. There's just one problem: every single request now carries 25KB of skill descriptions in the system prompt, whether the user needs them or not. That's ~6,200 tokens of overhead before a single word of actual conversation. This post walks through how we found this problem, the four approaches we tried (and why three of them failed), and the architecture we landed on. The Problem: More Skills = Worse Performance We run an AI agent platform where users install "skills" — essentially instruction modules that tell the agent how to use specific tools. Think of them like plugins, but implemented as structured markdown files that get injected into the system prompt. The mechanism is simple: Install skill → SKILL.md stored locally → name + description injected into every request's system prefix → Agent sees full skill list → matches → reads SKILL.md → execute