Results for “pe”

Routes quality reviews to specialized critic agents based on file type or flags, covering peer review, code review, and manuscript polish.

Creates persona-targeted, step-by-step CodeTour walkthroughs that link to real files and line numbers for onboarding, architecture, PR review, and more.

Conducts multi-axis code review across correctness, readability, architecture, security, and performance before merging any change.

Provides Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Provides Vue.js best practices and workflow guidance, including architecture planning, component design, reactivity patterns, and performance optimization for Vue 3 projects.

Provides a structured code review checklist covering security, performance, correctness, maintainability, testing, accessibility, and documentation, with a three-pass review process and guidance on severity levels and anti-patterns.

Review GitHub PRs and GitLab MRs with structured analysis including blast radius, security scanning, test coverage delta, breaking change detection, and performance impact.

Review code for AEM Edge Delivery Services projects, validating code quality, performance, accessibility, and adherence to EDS best practices during self-review or pull request review.

Runs a structured code review using four parallel subagents covering style, correctness, security, and performance, then synthesizes findings by severity and gives an approve/needs-changes recommendation.

Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy, flagging issues with easing, duration, performance, and accessibility.

Launches four parallel read-only sub-agents to review a git diff or file scope for regressions, security risks, performance issues, and contract gaps, then aggregates findings into a prioritized fix path.

Analyzes game AI systems in a codebase, covering behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering, perception, difficulty adaptation, NPC dialogue, and AI debugging tools for Unity, Unreal, and Godot projects.

Write, review, or improve SwiftUI code following best practices for state management, view composition, performance, macOS-specific APIs, and iOS 26+ Liquid Glass adoption. Use when building new SwiftUI features, refactoring existing views, reviewing code quality, or adopting modern SwiftUI patterns.

Review pull requests following Sentry engineering practices, covering security, performance, testing, and design.

Provides code review best practices, checklists, and example feedback for quality, security, performance, and maintainability.

Guides technical verification of code review feedback before implementation, emphasizing reasoned pushback over performative agreement.

Rewrites unprofessional code comments into clear ones and performs non-semantic cleanup without altering logic or behavior.

Classifies code review findings to choose the smallest correct response, preventing one-patch-per-comment churn.

Provides a structured protocol for receiving and evaluating code review feedback, emphasizing technical verification over performative agreement.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Runs independent AI CLI reviewers over phase plans and merges their feedback into a REVIEWS.md file.

Guides writing idiomatic Rust with ownership, error handling, traits, concurrency, and best practices for safe, performant code.

Enforce idiomatic Rust patterns for ownership, error handling, enums, traits, concurrency, and best practices to build safe, performant applications.

Designs, reviews, and troubleshoots Apex triggers using a single-trigger-per-object handler pattern with recursion guards and activation bypass.

Runs an adversarial code review with two blind judges analyzing the same code from opposing perspectives to find flaws before production.

Performs structured code reviews of git branches, tracking findings in a living document and optionally running deep architectural analysis with subagents.

Executes implementation plans by dispatching a fresh subagent per task and running two-stage reviews for spec compliance and code quality.

Runs a multi-perspective review of a pull request, including code quality, tests, and potential silent failures, then reports findings ranked by severity.

Enforces a principal-engineer self-review checklist on every code block before output, covering correctness, performance, security, naming, and testability.