Week 14, 2026 Claude Skills, Code Internals and Buddhism

What I learned

Claude Code Source Code Leak

  • The Claude Code source code leaked via an npm map file this week, exposing internal structure and prompt-handling logic
  • I believe this level of transparency, even if accidental, accelerates the arms race in agentic coding tools. The developer tooling space is about to get very competitive

Claude Agent Skills

  • Learned about Anthropic’s Agent Skills as an open standard this week. This is the most significant Claude platform release since MCP and it shifts Claude from a chatbot toward a composable autonomous agent runtime.
  • I read Skills Explained which clarifies how Skills differ from prompts, Projects, MCP servers and subagents. Skills are reusable, deployable units of agent capability
  • Extending Claude’s capabilities with Skills and MCP explains how the two complement each other: MCP for data access, Skills for encoded workflows
  • Building Agents with Skills and Improving skill-creator round out the launch with practical guidance on measuring and refining agent skill performance
  • Anthropic published claude-plugins-official with a skill-creator plugin. The open standard positioning is smart: it invites community contribution while keeping Anthropic at the center
  • Skills was launched back in October 2025. I have been running almost six months late. I knew skills are like a specific system prompt in a markdown file, but that I used to come across bits and pieces. When I started to learn this deliberately to teach to others, I realized how powerful and flexible this system is.

Claude Code Ecosystem and Learning

AI Agents and Frameworks

  • I explored CrewAI’s documentation and architecture to understand how it structures agent workflows
  • I discovered CatchMe, described as the missing layer of personal agent. It attempts to build a persistent, personalised context layer on top of existing agents. Still early but the direction is right
  • I watched Andrej Karpathy’s “Skill Issue” talk on Code Agents, AutoResearch and the Loopy Era of AI. In my view this is required watching for anyone building in the agent space
  • I spent time with the Claude Cowork tutorial by Design Lead Jenny Wen. 40 minutes of structured walkthroughs that clarified how Cowork schedules and chains agent tasks
  • Claire Vo’s talk on how OpenClaw’s AI agents run her business, family and life is one of the most honest and practical accounts of actually living with autonomous agents I have come across
  • I also watched the Claude Cowork Full Course for Beginners which is a solid on-ramp for teams just starting with multi-agent orchestration
  • I explored Factory.ai’s documentation including their Vibe Coding Skill and Droid VPS setup guides. Factory is positioning itself as the developer-first alternative to Claude Code for agent-driven dev workflows.

Observability and Monitoring

System Design and Open Source

Developer Tools and Open Source

  • I explored Simon Willison’s browser-based HTML tools, particularly the GIF optimizer, SVG renderer and terminal-to-HTML converter
  • I read Simon Willison’s note on a Python Vulnerability Lookup tool he built as a single-page HTML app powered by Pyodide. The pattern of shipping useful security tooling as a static page is underrated
  • Google released Gemma 4 open models. The HN thread had good technical depth on the architecture changes. The open-weights race is still very much on

Supply Chain and Security

Industry and Community

Buddhism

  • After 1.5 years of reasearch finally Vikas sir posted a video on Buddhism. In this 5hrs 45 mins long video, I got to know many new things about Buddha’s life and teachings. How there is an alternate theory that a water crisis led to his sanyas and later getting enlightened and spreading the message of Buddhism. Here is a PDF prepared using NotebookLM.

Unable to display PDF file? Download instead.

* Here is the complete video:

What I built

What I failed

  • Planned to make progress on the AWS Solutions Architect Professional certification this week but work priorities consumed the time I had set aside.
  • Did not able to give time to ragwatch.

Random Thoughts & Quotes

  • “LLM = CPU (data: tokens not bytes, dynamics: statistical and vague not deterministic and precise). Agent = operating system kernel.” — Andrej Karpathy

  • “Note that the main issues that people currently unknowingly face with local models mostly revolve around the harness and some intricacies around model chat templates and prompt construction. From typing the task in the client to the actual result, there is a long chain of components that are not only fragile but also developed by different parties.” — Georgi Gerganov on why local models still struggle with coding agents