Hiring for Claude Skills and MCP: The New Skill Stack
The roles, skills, and 90-day staffing plan teams need to make Claude Skills and MCP servers work in production. What to hire, train, and retire.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
The roles, skills, and 90-day staffing plan teams need to make Claude Skills and MCP servers work in production. What to hire, train, and retire.
Grow Claude Skills and MCP servers from one team to many without chaos — shared catalogs, ownership, versioning, and platform patterns that scale.
Honest trade-offs for Claude Skills and MCP servers — when an agent wins, when a plain prompt or script beats it, and how to decide before you build.
Guardrails leadership needs before scaling Claude Skills and MCP servers — least privilege, human-in-the-loop, audit, and prompt-injection defense.
Habits, norms, and change management that turn a few Claude power users into an org-wide capability — the human side of Skills and MCP adoption.
Where Claude Skills and MCP server savings actually come from — token math, break-even thinking, and the hidden costs engineering leaders must model.
A staged playbook to move an existing workflow onto Claude agents with Skills and MCP: mapping, shadow mode, canary rollout, and instant rollback.
Build an eval loop for Claude agents on Skills and MCP: outcome and process metrics, real datasets, calibrated LLM judges, and release gates.
Harden Claude agents on Skills and MCP: sandboxing, least privilege, secret handling, and layered prompt-injection defense that contains the blast radius.