185 skills · sorted by relevance
Apply shared coding conventions to new modules, reviews, and refactoring. Guides descriptive naming, readable structure, updates that preserve existing data, and error handling, giving teams a consistent basis for checking code quality.
Review Archify issues, pull requests, and code by weighing user value, implementation and maintenance costs, and wider impact. Use evidence to decide whether work is worthwhile, compare alternatives, and identify questions that still affect the decision.
Review source code for readability, complexity, duplication, and maintainability without changing files. Receive a structured report with prioritized issues, refactoring suggestions, positive findings, and estimates of technical debt—the work needed to address accumulated code problems.
Review system architecture and major design changes for scalability, resilience, and maintainability. Identify risks, compare design tradeoffs, and document recommendations and next steps across services, APIs, data storage, security, and deployment.
Review code for security, performance, quality, and maintainability. Get findings organized by severity, with file locations, explanations of impact, and example fixes, plus code metrics and complexity comparisons for refactoring reviews.
Improve existing code structure while preserving its behavior. Review dependencies and tests, identify maintainability problems such as duplication or long functions, and plan small changes with testing, reversible commits, and a final comparison of results.
Improve existing code structure while preserving its behavior, using Martin Fowler's refactoring approach. Assess tests, identify structural problems, agree on a phased plan, and make small, tested changes with reversible commits and before-and-after comparisons.
Refactor JavaScript using ES6+ features to make code easier to read and maintain. Covers asynchronous operations with async/await, modules, and functional patterns that transform data without changing the original values.
Review DAX formulas, the expressions used for calculations in Power BI, and propose clearer, more maintainable alternatives. Identify repeated calculations, costly filtering, and error-handling issues, then explain changes and recommend ways to test them.
Review a Power BI data model’s tables, relationships, storage choices, performance, maintainability, and security. Use the findings to identify design issues and build a prioritized improvement plan with concrete recommendations and implementation guidance.
Review C#/.NET code for design patterns, separation of responsibilities, and common implementation issues. Receive actionable recommendations covering maintainability, testing, performance, and security without changing the code.
Run Gemini CLI to review code, assess technical plans, and analyze large collections of files. Choose a model and execution settings for the task, then capture findings about security, maintainability, architecture, or technical debt.
Review code changes for security, performance, correctness, and maintainability. Get findings tied to files and lines, severity ratings, practical suggestions with code examples, and a recommendation to approve, request changes, or discuss further.
Review recent code changes for duplication, maintainability, and wasted work, then apply straightforward improvements. The workflow combines three parallel reviews, prioritizes small, low-risk fixes, and checks the edited code with relevant tests or project checks.
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
Review code changes and files for bugs, security risks, performance problems, design issues, and test coverage. Receive a structured report with prioritized findings, concrete improvement examples, positive feedback, and an overall review verdict.
Review code changes for security, performance, correctness, and maintainability. Get findings tied to files and lines, severity ratings, practical suggestions with code examples, and a recommendation to approve, request changes, or discuss further.
Writes and reviews Lean 4 proofs, machine-checked arguments for mathematics or specifications. Follows Mathlib conventions to organize definitions and lemmas, fill proof gaps, refactor lengthy proofs, and establish automated checks for unproved claims and style.
Get independent code reviews of uncommitted changes, branch differences, or individual commits using Codex or Antigravity. Receive findings with severity, locations, impact, and suggested corrections, and compare where the two reviewers agree or differ.
Improve CircleCI build and test pipelines by identifying slow or unreliable jobs, then adjusting caching, data sharing between jobs, triggers, and parallel execution. Receive incremental configuration changes with expected effects, validation criteria, and rollback guidance.
Audit Claude, Codex, or Pi configuration, instruction conflicts, hooks, and MCP connections to external tools. It also checks safeguards for maintaining AI-assisted projects and returns prioritized findings with evidence and concrete corrective or diagnostic steps.
Review code for readability, security weaknesses, performance bottlenecks, test quality, and ease of maintenance. Receive findings grouped by importance, with explanations and concrete suggestions that reflect the project's context and constraints.
Guide code changes in iPolloWork by reusing existing implementations, limiting new files and dependencies, and keeping responsibilities clear. Review runtime costs, run focused checks, and audit the final changes for duplication and maintainability issues.
Review SwiftUI projects for outdated APIs, data flow, layout, accessibility, and performance issues. Get findings organized by file, with brief before-and-after code examples and a prioritized summary of the changes that matter most.