staff-review

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Proactive staff engineer review for technical plans. This skill should be used when Claude detects implementation plans, architecture designs, migration plans, or refactoring proposals to provide senior engineering feedback with a focus on risks, simplicity, and completeness.

MMIV-ML By MMIV-ML schedule Updated 2/2/2026

name: staff-review description: Proactive staff engineer review for technical plans. This skill should be used when Claude detects implementation plans, architecture designs, migration plans, or refactoring proposals to provide senior engineering feedback with a focus on risks, simplicity, and completeness.

Staff Engineer Plan Review

This skill provides a structured framework for reviewing technical plans from a staff engineer perspective, emphasizing balanced pragmatism over perfectionism.

When to Trigger

Activate this skill proactively when detecting:

  • Implementation plans with numbered steps or phases
  • Architecture proposals with component diagrams or system changes
  • Migration plans involving data or system transitions
  • Refactoring proposals affecting multiple files or modules
  • Any plan that modifies critical paths, data models, or public APIs

Indicators: "Here's my plan", "I propose we", "Implementation steps", numbered lists with file modifications, mermaid diagrams, or explicit architecture decisions.

Review Framework

1. Risks & Edge Cases

Examine the plan for:

  • Failure modes: What happens when external services fail? Network timeouts? Disk full?
  • Race conditions: Concurrent access, distributed system timing issues
  • Data integrity: Partial failures, transaction boundaries, data loss scenarios
  • Rollback scenarios: Can changes be reverted? What's the blast radius?
  • Security implications: New attack surfaces, credential handling, input validation
  • Performance cliffs: O(n) becoming O(n^2), memory pressure, connection exhaustion

2. Simplicity & Pragmatism

Challenge the plan on:

  • YAGNI violations: Features or abstractions for hypothetical future needs
  • Over-abstraction: Interfaces with single implementations, premature generalization
  • Unnecessary indirection: Extra layers that don't add value
  • Gold-plating: Perfect solutions when good-enough suffices
  • Scope creep: "While we're at it" additions that expand risk
  • Premature optimization: Performance work without measurements

3. Completeness

Verify the plan includes:

  • Clear success criteria: How do we know it worked?
  • Testing strategy: Unit, integration, manual verification steps
  • Observability: Logging, metrics, alerting for new code paths
  • Documentation updates: API docs, runbooks, architecture diagrams
  • Dependency considerations: Version compatibility, breaking changes
  • Deployment strategy: Feature flags, staged rollout, canary releases

Output Format

Structure feedback in three parts:

Part 1: Inline Comments

Reference specific plan sections with targeted feedback:

> "Step 3: Migrate all user records in a single transaction"
CONCERN: Single transaction for all records risks timeout and locks other operations. Consider batched migration with checkpointing.

Part 2: Categorized Concerns

Group issues by severity:

Critical (blocks approval):

  • Issues that could cause data loss, security vulnerabilities, or major outages

Medium (should address):

  • Missing error handling, incomplete testing, unclear rollback

Minor (nice to have):

  • Style improvements, documentation gaps, minor optimizations

Part 3: Verdict

Provide a clear recommendation:

  • GO: Plan is solid, proceed with implementation
  • GO WITH CONDITIONS: Proceed after addressing specific items
  • REVISE: Significant concerns require plan revision before proceeding

Include brief rationale for the verdict.

Engineering Principles

Apply balanced pragmatism:

  1. Time-to-value matters: A working solution today often beats a perfect solution next month
  2. Prefer reversible decisions: When uncertain, choose options that are easy to change
  3. Question scope: Every additional feature increases risk and delays delivery
  4. Consider operational burden: Elegant code that's hard to debug in production isn't elegant
  5. Match effort to impact: Don't spend a week perfecting code that runs once a year
  6. Trust but verify: Accept reasonable assumptions but flag untested ones

Example Review

Given a plan like:

  1. Add new UserPreferences table with 15 columns
  2. Create generic PreferenceService interface for future preference types
  3. Migrate existing settings from JSON blob to new table
  4. Add REST endpoints for CRUD operations
  5. Update all clients to use new endpoints

A staff review might respond:


Inline Comments:

"Create generic PreferenceService interface for future preference types"

CONCERN: YAGNI - do we have concrete plans for other preference types? A simpler direct implementation avoids interface overhead.

"Migrate existing settings from JSON blob to new table"

QUESTION: What's the migration strategy? One-shot? Dual-write period? Rollback plan if migration fails mid-way?

Categorized Concerns:

Medium:

  • Migration rollback strategy not specified
  • No mention of feature flag for gradual rollout
  • Missing success metrics (latency targets, error rate thresholds)

Minor:

  • 15 columns suggests possible over-normalization; consider if JSON column + indexes suffices
  • Client update coordination not addressed

Verdict: GO WITH CONDITIONS

Address migration rollback strategy and add feature flag for the new endpoints. The interface abstraction can be simplified to direct implementation unless there's a concrete near-term need for other preference types.


Notes

  • Review depth should match plan significance - quick changes need quick reviews
  • When in doubt, ask clarifying questions rather than assuming
  • Acknowledge what the plan does well, not just concerns
  • Frame feedback as collaborative improvement, not criticism
Install via CLI
npx skills add https://github.com/MMIV-ML/fastMONAI --skill staff-review
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