OpenClaw Skills Explained: Why 95% of Agents Don't Need a CLAUDE.md File

Key Takeaways

• Skills use progressive disclosure — only the skill's name and description are loaded into the agent's context window. The full skill instructions only load when the agent decides it needs that specific skill. This is drastically more token-efficient than putting everything in CLAUDE.md.
• CLAUDE.md files add their full token count to every single turn — at 1,000 lines, that's roughly 7,000 tokens per message. This is only justified for truly proprietary company workflows that must be referenced on every single action.
• Build skills by doing the workflow WITH the agent first — identify the task, run it live in a session, correct the agent step by step until you get a clean successful run, then ask it to write the skill from that working context. Never write or download skills cold.
• Never download third-party skills blindly — beyond the security risk of running untrusted code, downloaded skills lack the context of your specific workflows, model behavior, and edge cases. They will fail in non-obvious ways.
• The models are already excellent — Claude Opus 4.6 and GPT 5.4 are both capable of high-quality work. The bottleneck is almost always context design: what you give the agent to work with, not the model's raw ability.

How Skills Progressive Disclosure Works

A skill file has three parts: a name, a description, and a body of instructions. When you run an OpenClaw agent, only the name and description of each skill enter the context window — not the full body. When you tell the agent to do something that matches a skill's description, the agent loads the full skill body and follows its steps. This means you can have dozens of skills registered without paying their token cost unless the agent actually invokes them.

Contrast this with CLAUDE.md: its full contents are added to the context on every single message exchange. For large files, this becomes a significant cost multiplier and can push the agent toward its context limit faster in long sessions.

The Right Way to Build a Skill

1. Identify a recurring workflow you do with your agent.
2. Run the workflow step by step in a live session, correcting the agent at each stage.
3. Repeat until you have a clean, successful end-to-end run.
4. Tell the agent: "Review what you just did and create a skill for it."
5. The agent writes the skill with real context from an actual working run — not generic guesses.

Related on OpenClawDatabase

• OpenClaw Skills Guide — how to write, install, and manage skills
• OpenClaw Skills Database — curated registry of community skills
• OpenClaw Email Integration — set up your agent's own inbox, like the sponsor-filter workflow described in this video
• OpenClaw Security Hardening — why third-party skill safety matters
• Claude Code Workflow: IdeaBrowser MCP + Paper UI — a complementary look at Claude Code in a real product workflow