Resolvers: The Routing Table for Intelligence

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In "Thin Harness, Fat Skills", I introduced five definitions for building agent systems that actually work. Skills got all the attention. People bookmarked the skill-as-method-call pattern, the diarization concept, the thin harness architecture. Good. Those matter. But the one that got almost no attention is the one that matters most. Resolvers. And the reason they got ignored is the same reason they're so important: they're invisible when they work, and catastrophic when they don't. A resolver is a routing table for context. When task type X appears, load document Y first. That's it. One sentence. But that one sentence is the difference between an agent that compounds intelligence and an agent that slowly forgets what it knows. This is the story of how I learned that the hard way. The 20,000-line confession My CLAUDE.md was 20,000 lines. I'm not proud of this. Every quirk, every pattern, every lesson I'd ever encountered with Claude Code, every convention for my codebase, every edge case I'd been burned by. I kept adding. The file kept growing. It felt productive. It felt like I was making the model smarter. I wasn't. I was drowning it. The model's attention degraded. Responses got slower and less precise. Claude Code literally told me to cut it back. That's when you know you've gone too far — the AI is telling you to stop talking. The instinct is natural. You want the model to know everything. So you cram everything into the system prompt, the instructions file, the context window. You're trying to make the model omniscient by proximity. It doesn't work. You can't make someone smarter by shouting louder. You make them smarter by giving them the right book at the right moment. The fix was about 200 lines. A numbered decision tree. Pointers to documents. When the model needs to file something, it walks the tree: Is it a person? → /people/ directory A company? → /companies/ directory A policy analysis? → /civic/ directory Twenty thousand lines of knowledge, accessib