When does the agent push back on a suggestion?

Pushback occurs when a suggestion breaks existing functionality, lacks context, violates YAGNI principles, or conflicts with the human partner's established architectural decisions.

Why does this skill discourage saying 'thank you' or 'great point'?

The skill prioritizes 'technical rigor over social comfort.' Eliminating performative agreement ensures the agent focuses on verification and action rather than polite but unverified compliance.

How does it handle unclear review items?

It implements a 'stop-and-clarify' rule. If any part of a feedback list is unclear, the agent must ask for clarification on those specific items before implementing any part of the request.

What is the 'verification-first' principle?

It is a requirement to check every suggestion against the actual codebase reality and project constraints before acknowledging or starting the implementation.

Technical Code Review Reception

Name: Technical Code Review Reception
Author: CommandCodeAI

byCommandCodeAI

•

Collaboration Tools

Enforces a rigorous technical verification workflow for processing code review feedback while eliminating performative agreement.

About

This skill optimizes how Claude processes and responds to code review feedback by prioritizing technical correctness over social compliance. It establishes a 'verify-before-implementing' workflow that requires the agent to restate requirements, check codebase reality, and push back on technically unsound suggestions. By removing performative politeness and focusing on technical rigor, this skill ensures that feedback—whether from a human partner or an external reviewer—is handled with the skepticism and precision required for high-stakes software development.

Key Features

Context-aware skepticism for external or automated review sources
Elimination of performative phrases and unnecessary gratitude
Structured handling for ambiguous or multi-item feedback sets
Verification-first response pattern for all incoming feedback
Strict YAGNI validation for suggested 'professional' features
4 GitHub stars

Use Cases

Resolving architectural conflicts between human reviewers and AI implementations
Managing complex multi-point feedback from senior developers
Evaluating third-party security audits or automated PR suggestions

About

Key Features

Context-aware skepticism for external or automated review sources
Elimination of performative phrases and unnecessary gratitude
Structured handling for ambiguous or multi-item feedback sets
Verification-first response pattern for all incoming feedback
Strict YAGNI validation for suggested 'professional' features
4 GitHub stars

Use Cases

Resolving architectural conflicts between human reviewers and AI implementations
Managing complex multi-point feedback from senior developers
Evaluating third-party security audits or automated PR suggestions