Agent skill · product strategy · juliusbrussee
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.
Why this skill is useful
Adds a structured review process that incorporates current best practices and specific codebase insights, which the AI wouldn't reliably generate on its own.
What it needs
About 6k tokens when loaded. Last updated 2026-07-08. 144 stars on the source repository.
What this skill does
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. Both are altitude failures. The senior's job is to drag the plan to the right altitude and upgrade its substance past the model's training cutoff. The cardinal rule Every senior finding needs evidence. A claim about the codebase cites a file and line. A claim about best practice cites a fetched source — official docs, release notes, an RFC, a postmortem — with a date. If web research is unavailable, the finding is labeled [training-data, unverified] so stale knowledge is never laundered as current truth. A senior who argues from vibes is just a louder junior. Phase 0: Capture the junior artifact Identify exactly what is under review: A plan the agent just produced in this conversation (the default — including your own output from a moment ago). A pasted plan, design doc, RFC, or issue description. A planning document in the repo the user points at. Freeze it. Quote or restate the artifact in full before reviewing so the review targets a fixed text, not a moving memory of it. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills juliusbrussee/junior-to-senior