Explore our collection of Agent Skills to enhance your AI workflow.
Creates reusable, agent-executable slash commands to automate repetitive developer workflows.
Enforces concise, high-quality PHPDoc standards to improve code readability without redundant signature duplication.
Conducts comprehensive pull request reviews using a swarm of specialized AI agents to ensure code quality, security, and architectural integrity.
Aggregates and manages a persistent library of successful development patterns and anti-patterns across all your Claude Code projects.
Records and replays HTTP interactions for Python tests to create deterministic, network-independent, and cost-effective test environments.
Manages stacked pull requests and dependent branches using the Graphite (gt) command-line tool.
Generates standardized, Nette-compliant git commit messages using specific tense, casing, and formatting rules.
Verifies development tasks and code changes by collecting verifiable proof including test results, build outputs, and exit codes.
Eliminates flaky tests and race conditions by replacing arbitrary timeouts with smart, deterministic condition polling.
Manages the adaptive learning system and privacy settings to improve AI performance based on user interactions.
Automates a multi-agent design review process immediately following the creation of design documents to ensure technical and product readiness.
Decomposes complex multi-concept queries into independent searchable terms to optimize retrieval accuracy and coverage in RAG pipelines.
Automates the systematic discovery, resolution, and response to pull request feedback from AI bots and human reviewers.
Generates comprehensive GitHub issues with embedded TDD plans and autonomous agent instructions for full pull request lifecycle management.
Automates the code review process by dispatching specialized subagents to verify implementations against requirements before merging.
Manages multiple parallel Claude Code instances across Git worktrees to prevent file conflicts and synchronize architectural decisions.
Builds complex AI workflows using decorator-based patterns for parallel execution, persistence, and human-in-the-loop interactions.
Applies Test-Driven Development principles to create robust, verified process documentation and specialized Claude Code skills.
Standardizes a rigorous, technical-first approach to processing and implementing code review feedback without performative agreement.
Validates and stress-tests AI skill documentation using a TDD-inspired Red-Green-Refactor cycle to ensure agent compliance under pressure.
Enforces high-quality testing standards by identifying and preventing common pitfalls like mock-testing and production code pollution.
Enforces mandatory protocols for skill discovery, brainstorming-first workflows, and task tracking to ensure consistent AI output quality.
Automates the execution of multi-step implementation plans by dispatching fresh subagents for each task followed by iterative code reviews.
Executes multi-step implementation plans in controlled batches with built-in review checkpoints and mandatory verification.
Simplifies the creation, validation, and rendering of secure, reusable web forms using the Nette Framework.
Ensures application reliability by implementing multi-layered validation patterns that make structural bugs and data corruption impossible to reproduce.
Implements high-performance real-time data streaming patterns using SSE, WebSockets, and ReadableStream APIs.
Implements robust asynchronous task processing and job queues using Celery, ARQ, and Redis for Python backends.
Validates and normalizes complex data structures, configuration files, and API inputs using the Nette Schema library.
Writes, reviews, and refactors Python 3.11 code to meet high standards for clarity, robustness, and maintainability.
Scroll for more results...