641 skills · sorted by relevance
Build features or fix bugs by writing one failing test, then the smallest implementation that passes it. Tests check behavior through public interfaces, the entry points callers use, so they remain useful when internal code changes.
Review code changes from a chosen Git reference against two separate criteria: documented coding standards and the original requirements. Independent reviewers check each criterion, then present findings separately so convention problems and requirement gaps remain visible.
Build and review React and Next.js interfaces for keyboard use and screen readers, tools that read content aloud. Apply practical patterns for form labels, error messages, accessible controls, and focus movement.
Review Flutter and Dart code using a checklist that adapts to the project's state management library. Check interface structure, asynchronous operations, performance, tests, accessibility, and security to identify issues across the application.
Check code changes before committing with security scans, tests, linting, and an independent agent review. Compare failures against the previous baseline, then use a separate agent to fix reported problems and repeat verification.
Review Spec Kit code changes against relevant design documents, tests for expected and unwanted behavior, evidence that bug fixes prevent recurrence, and repository terminology. Findings identify the document sections behind design mismatches.
Review code changes before merging across correctness, readability, architecture, security, and performance. Get findings that distinguish required fixes from optional suggestions, with concrete ways to simplify code and checks on test coverage.
Prepare completed changes for a GitHub pull request with organized commits, a clear title and description, verification evidence, screenshots for interface changes, and explicit replies to review comments through merge readiness.
Sort open GitHub pull requests by change size, automated check results, conflicts, and inactivity. Review selected changes in detail, draft constructive feedback, and post comments after the user approves them.
Review code for security flaws, bugs, performance problems, maintainability, and test quality. Produce structured feedback with issue locations, likely impact, suggested fixes, positive observations, and a recommendation on whether changes are ready to merge.
Review GitHub pull requests, or proposed code changes, with specialized AI agents covering security, performance, architecture, style, and accessibility. Organize findings, suggest fixes, and post feedback through configurable review workflows.
Coordinate large migrations, refactors, and bulk renames by splitting them into independent tasks. After plan approval, 5–30 agents work in separate Git worktrees, isolated working directories, then review, test, and open a pull request for each task.
Review code changes in a diff, pull request, branch, or file path to identify bugs and cleanup opportunities. Findings include file locations and concrete failure scenarios, with options to apply fixes or post comments.
Plan and write tests for changes to agent behavior, favoring integration tests that check components together. Identify key logic changes and user-visible behavior to cover, reuse existing helpers, and keep unit tests in dedicated files.
Assess whether a code change is small enough to review using line-count guidance. For larger changes, examine the actual differences, dependencies, and calling code to suggest reviewable stages and the smallest coherent first step.
Review code that manages the conversation history and extra information sent to Codex models. Apply rules for incremental history, size limits, cache stability, manual review of large additions, and required implementation structures.
Review code changes with several independent reviewers working in parallel, then receive organized findings. Request a quick or thorough review, choose to hand off fixes, or run without interaction when another workflow or CI job initiates the review.
Review changed code against project rules and report missing tests, unused code, typing problems, and style violations. It also checks API design and data isolation, helping developers identify issues after implementation.
Guide AI coding work toward simple implementations, focused edits, and explicit assumptions. Use it when writing, reviewing, or refactoring code to keep changes tied to the request and define success through tests or other concrete checks.
Review code for security, performance, quality, and ease of maintenance. Receive an overall assessment and categorized findings, with file locations, severity, impact, and code examples for addressing critical issues.
Review code for security, performance, quality, and ease of maintenance. Receive an overall assessment and categorized findings, with file locations, severity, impact, and code examples for addressing critical issues.
Review code for security, performance, quality, and ease of maintenance. Get an overall assessment and prioritized findings with file locations, severity, explanations of impact, and example fixes to guide improvements or Pull Request reviews.
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.
Review code changes through a structured check of requirements, design, correctness, security, performance, and tests. Produce specific, constructive feedback that separates required fixes from optional suggestions, and build shared review guidelines for a development team.