Estás usando Claude Code mal: así lo usan los ingenieros de Anthropic
This video explains how Anthropic's own engineers use Claude Code through a skills-based system rather than isolated prompts. The core idea is to build reusable, modular skill folders that package instructions, examples, and scripts for repeated tasks. Four principles guide this approach: create skills for repeated tasks, save complete processes not just instructions, keep skills small and specific, and continuously improve skills based on corrections.
Summary
The video presents four principles derived from how Anthropic's engineers actually use Claude Code, contrasting this approach with the common habit of writing new prompts from scratch each time.
The first principle establishes that repeated tasks should not depend on new prompts each time. Anthropic created 'skills'—organized folders that package procedural knowledge—so Claude doesn't start from scratch. A skill can contain instructions, examples, reference files, documentation, rules, and executable scripts. The presenter illustrates this with a visual presentation skill that encodes the entire workflow, so instead of writing a long prompt each time, the user simply says 'turn this document into a presentation.'
The second principle is that a skill should contain the entire process, not just a single long instruction. Anthropic's Skill Creator tool can help build skills, but a good skill has three layers: a description (telling Claude when to activate the skill), instructions (what steps to follow once activated), and resources or tools (reference files, templates, scripts). The presenter cites an Anthropic example where engineers noticed Claude repeatedly writing the same Python script to style slides, so they saved it inside the skill to avoid redundant regeneration.
The third principle warns against creating one massive skill that tries to do everything. Instead, skills should be small, specific, and composable. A 'content creator' mega-skill is hard to debug and maintain, whereas separate skills for researching ideas, analyzing transcripts, writing scripts, and creating hooks each have a clear purpose. This modularity means improvements to one skill benefit all contexts where it's used, and skills can be chained together as needed.
The fourth principle is iterative improvement: after every use of a skill, the user should ask whether corrections made in the chat should be permanently incorporated into the skill itself. Anthropic's stated goal is for Claude after 30 days of use to be significantly better than on day one. The presenter recommends asking Claude to review recent conversations and identify which repeated corrections should become permanent rules in the skill, turning the system into one that compounds in quality over time.
Key Insights
- Anthropic engineers explicitly stopped building increasingly complex agents and shifted to building 'skills' instead—organized folders that package composable procedural knowledge for Claude to reuse across tasks.
- Anthropic observed that Claude was repeatedly generating the same Python script to style slides across conversations, so engineers saved the script inside the skill itself so future Claude instances could simply run it rather than regenerate it.
- A skill's description layer is what Claude checks to determine when to activate the skill, and if written well enough, Claude can auto-activate the skill without the user explicitly invoking it—for example, detecting that 'convert this document into slides' warrants the presentation skill.
- Anthropic's documentation emphasizes that skills should not be overloaded with context; a mega-skill is harder to debug because when something fails, it's unclear which layer caused the problem, whereas small specific skills isolate failures and allow targeted improvements.
- Anthropic frames the goal of the skills system as Claude after 30 days of working with a user being significantly better than Claude on day one—not because Claude learns autonomously, but because corrections are systematically written back into skills as permanent rules.
Topics
Transcript
[0:00] If every time you open clot you start writing a new prop from scratch, you 're probably using it worse than you think. And that's not my opinion. When you listen to Antropic's own engineers explain how they work with Cloud, you realize that they don't think in terms of isolated prompts, they think in terms of systems, processes, and reusable skills. In fact, they explain it quite clearly in an Antropic talk. It's not about building increasingly complex agents, but about building skills that give the cloud concrete experience to perform [0:30] certain tasks better. We will demonstrate why we stopped building agents and started developing skills instead. So in this video we're going to organize all of…
Full transcript available for MurmurCast members
Sign Up to AccessMore from Migue Baena IA
NotebookLM en 2026, lo que realmente merece la pena
This video explores NotebookLM 2.0's powerful features for 2026, demonstrating how to use curated sources to generate reports, presentations, Excel files, and vertical videos. The tool works best as a structured context-based system rather than a general chatbot, transforming sources into actionable insights and multiple formats while requiring critical review of outputs.
🚨 6 skills de Claude que necesitas probar
The speaker identifies six essential Claude skills developed after testing over 100 options, focusing on workflow automation, planning, task management, code review, context preservation, and cross-session memory. These skills significantly improve project development speed, reduce errors, and eliminate repetitive work.
Aprende el 99% de Claude Code en menos de 25 minutos
This video tutorial explains how to use Claude Code, an AI agent that works within project folders to create, test, and iterate on projects directly from your computer. It covers the complete workflow from initial setup through publishing projects online, including how to use connectors, skills, GitHub, and Vercel.
El método MIT para aprender cualquier tema en 90 minutos con NotebookLM
An MIT student developed a method called "context accumulation" to learn complex topics in 90 minutes using NotebookLM by uploading multiple sources, asking strategic questions to identify core concepts, and generating difficult questions to test deep understanding rather than surface-level memorization.
¿Una app completa sin saber programar? Mira esto 👀
Repll agent is an AI tool that enables non-programmers to create complete applications by describing their ideas in natural language. Unlike single-assistant AI tools, it uses multiple AI agents working in parallel—one coding, one testing, one designing, and one handling deployment—to transform concepts into functional products faster.