This essay shows how to identify and use alternative career entry points. It covers practical tactics, common pitfalls, and real-world examples across different industries.

• Map your existing skills to adjacent teams' unadvertised needs instead of waiting for your exact dream title to open up (e.g., UX researcher into product strategy, finance analyst into BI).
• Build small independent proof points (like a 3-episode podcast pilot with a few hundred readers) to make it easier for decision-makers to reclassify you into a role than hire from scratch.
• Volunteer or freelance in adjacent industries—each stint counts like three months of targeted experience and can chain into unexpected roles (lawyer editing a nonprofit report leading to corporate communications).
• Treat each side-door attempt as a testable hypothesis with set timelines, adjusting your pitch or niche based on feedback rather than sticking to one approach.

The author argues that modular “Skills”—reusable markdown workflows loaded on demand—outperform standalone AI agents by cutting token bloat and maintenance overhead. A live GEO audit system built with Skills shows how you can turn domain expertise into scalable, service-ready products without managing dozens of agents.

• Claude's "Skills" load modular markdown playbooks on demand instead of baking everything into prompts, citing 53 tokens for passive reference vs. embedding a full prompt every time
• A live GEO audit system built entirely on Skills scrapes visibility across ChatGPT/Gemini, flags gaps like missing Wikipedia entries, and auto-generates client-ready reports without spinning up separate agents
• The whole GEO pipeline is public and forkable, letting anyone productize it without building custom infrastructure
• Documenting expertise once in a markdown file and iterating on it lets teams ship service-ready AI products in days rather than maintaining fleets of bespoke agents

Chrome now lets you save reusable Gemini prompts as “Skills” for one-click access instead of retyping or copy-pasting each time. Saved Skills sync across devices when you’re signed in, and you trigger them with “/” or the plus button in Gemini. They still require confirmation for actions like messaging or calendar entries.

• Chrome's Gemini now lets you save reusable prompts as "Skills," triggered via "/" or the "+" button instead of retyping or pasting them each time
• Skills sync across desktop devices when signed into Chrome
• Skills can operate across multiple open tabs at once, useful for tasks like comparing prices or aggregating research
• Actions like sending messages or adding calendar events still require user confirmation, keeping the same security checks as regular Gemini prompts

The article argues that the Model Context Protocol (MCP) offers a more effective way to connect large language models (LLMs) to services compared to Skills. While Skills can help with knowledge transfer, they create unnecessary complications, especially when they require command line interfaces (CLIs). The author advocates for using MCP to streamline service integration and improve user experience.

• MCP abstracts away implementation details (no local installs, auth is simpler, updates happen seamlessly), while Skills often push that complexity onto the user
• Skills requiring CLI execution break down for LLMs like ChatGPT that can't run command-line tools
• Skills introduce deployment headaches, secret management issues, and context bloat that MCP avoids
• Skills should be reserved for knowledge transfer, while MCP should handle actual service/API execution