junior-to-senior

Reviews agent-generated plans against codebase reality and current best practices, then rewrites them.

Updated Jun 12, 2026
One-click install
npx skills add https://github.com/taylorelley/skills --skill junior-to-senior-taylorelley
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: junior-to-senior
Source: https://github.com/taylorelley/skills/tree/main/skills/engineering/junior-to-senior
Command: npx skills add https://github.com/taylorelley/skills --skill junior-to-senior-taylorelley

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Agent-generated plans often fail at two altitudes: they are either too vague ("add caching", "handle auth") or lost in low-level detail with no product vision. This Skill subjects a plan to an adversarial senior-engineer review grounded in the actual codebase and current best practices, then rewrites it into a scoped, evidence-backed version. ## Core Features & Use Cases - Evidence-based adversarial review: Every finding cites a file and line from the repository or a dated external source, with blocker/major/minor severity levels. - Altitude diagnosis: Classifies plans as fog (too vague), tunnel (too granular), or mixed using structured checklists and a vague-word blacklist. - Plan promotion: Produces a rewritten plan with goals, decisions with rationale, sequencing, rollback strategy, and open questions for a human. - Use Case: After an AI assistant drafts a migration plan, invoke this Skill to verify the plan against pinned dependency versions, check for deprecated APIs via web research, and receive a hardened v2 plan with a delta summary. ## Quick Start Review this plan like a staff engineer and promote it to a senior-level version.

Frequently Asked Questions about junior-to-senior

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get a senior-level review of an AI-generated plan?

Invoke the review on the plan the agent just produced, a pasted design doc, or a planning document in the repo. The Skill freezes the artifact, researches the codebase and current best practices, then returns findings and a rewritten plan.

How does the review verify claims about best practices?

Every best-practice claim must cite a fetched primary source such as official docs, changelogs, or release notes with a date. If web access is unavailable, claims are labeled as unverified training data rather than presented as current truth.

What is the difference between fog and tunnel plan failures?

Fog means the plan names components but never commits to interfaces, data shapes, or failure handling, so an engineer cannot start tomorrow. Tunnel means the plan dives into function signatures but lacks goals, success criteria, non-goals, and justification over alternatives.

Can the review run without web access?

Yes. It runs full codebase research against lockfiles, conventions, and prior decisions, skips web research, and tags every best-practice claim as unverified training data so stale knowledge is never presented as current.

When should I not use adversarial plan review?

Do not use it to invent product direction; genuine product choices are returned as open questions for a human. It also should not manufacture findings — a plan with no blockers or majors is reported as holding.