I sit down with Ras Mic to break down how AI agents actually work and why most people are using them wrong. Ras Mic explains the mechanics of context windows, makes the case that agent md files are largely unnecessary, and shares his step-by-step methodology for building custom skills that make agents dramatically more productive. Whether you're coding with Claude Code or automating workflows with OpenClaw, this episode gives you the foundational knowledge to stop wasting tokens and start getting real results from your AI tools.
Timestamps
00:00 – Intro
00:42 – The Models Are Good Now
01:20 – How Context Windows Actually Work
04:55 – The Power of Skills
09:17 – How to create Skills
16:35 – Skill Maxxing
19:05 – What you need too build a project
20:40 – Recursively Building and Improving Skills
29:23 – Context Window Management and Token Efficiency
33:02 – Closing Thoughts
Key Points
The models (Opus 4.6, GPT 5.4) are exceptionally good now — the differentiator is the context and harness you build around them.
Agent md and claude md files get loaded into context on every single turn, burning tokens and degrading performance as the context window fills up. 95% of users can skip them entirely.
Skills use progressive disclosure: only the name and description sit in context until the agent determines it needs the full file, saving thousands of tokens per conversation.
The best way to create a skill is to walk through the workflow with the agent step by step, achieve a successful run, and then have the agent write the skill based on that real context.
Recursively refine skills by feeding failures back into the agent and having it update the skill file so the same mistake is avoided going forward.
Scale for productivity by starting with one agent and building up workflows before adding sub-agents — start simple, then expand.
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