junior-to-senior
Adversarial senior-engineer review for agent-generated plans, designs, and architectures. Treats the current output as junior work, constructs a senior reviewer whose domain expertise comes from live codebase research plus web research of current best practices, diagnoses altitude failures (too vague or too granular), then rewrites the plan into a scoped, state-of-the-art version. Use when the user says "junior to senior", "senior review", "review this like a staff engineer", when a plan feels hand-wavy or lost in details, or before committing to any agent-written plan.
Install
With your agent
Install OMGSkills skill "juliusbrussee/skills:junior-to-senior" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/JuliusBrussee/skills /tmp/skills && ln -s /tmp/skills/skills/junior-to-senior ~/.claude/skills/junior-to-seniorFrom README
Junior to Senior Assume the plan in front of you was written by a capable junior: fluent, confident, and trained on the past. Build a senior reviewer that is grounded in two things the junior was not — this codebase as it actually exists and the state of the art as it exists today — and let the senior tear the plan down and rebuild it. This skill exists because agent-generated plans fail at two altitudes: Fog — the plan describes the high level fine ("add caching", "handle auth", "make it scalable") but never commits on the hard parts. No interfaces, no data shapes, no failure handling, no named libraries. An engineer reading it still has to make every real decision themselves. Tunnel — the plan dives into function signatures and file diffs but has no product vision. No statement of who this is for, what success means, what is out of scope, or why this approach beats the boring alternative. It optimizes a local detail while the shape of the feature is still wrong.