Nous Research's Hermes Agent Now Teaches Itself Your Entire Codebase

Hermes Agent, the open-source self-improving agent from Nous Research, just shipped a new /learn command that lets the agent ingest entire directories of source material and distill them into a reusable, verifiable skill. Feed it a folder of API docs, a codebase, a pile of PDFs, or internal config files, and it produces a structured SKILL.md document the agent can call on demand in any future session. No fine-tuning. No prompt stuffing. Just a clean, inspectable file on disk.

The problem it's actually solving

Every team that runs an AI agent on internal tooling hits the same wall: the agent doesn't know your stack. It doesn't know your internal API conventions, your deployment runbooks, or the quirks in your third-party vendor's SDK. The usual workarounds are either dumping everything into the system prompt (expensive, fragile) or re-explaining context every session (tedious, lossy).

Hermes's core differentiator has always been a closed learning loop: while solving problems with tools, it writes reusable skill documents and curates a persistent memory file so the agent quite literally gets more capable the longer it runs. The /learn command extends that loop to external knowledge , you bring the source material, the agent distills it.

How skills actually work

Skills are on-demand knowledge documents the agent can load when needed. They follow a progressive disclosure pattern to minimize token usage and are compatible with the agentskills.io open standard. That last part matters: Hermes follows the agentskills.io open standard , the same SKILL.md format Claude Code, Codex CLI, OpenClaw, OpenCode, and friends use.

The progressive disclosure model is worth understanding. Instead of loading every skill into every prompt, the agent uses a three-level hierarchy: