Coding standards, frameworks, refactoring, and code review (15.8K skills)
Implement and manage Raft, a method for keeping distributed servers in agreement. Covers leader election, log replication, node membership changes, and recovery after network disruptions to maintain consistent state across the cluster.
Turn specifications into pseudocode, a language-independent outline of program logic. Define data structures, handle edge cases, analyze time and memory requirements, and document design patterns and optimization opportunities before implementation.
Improve existing code through repeated cycles of testing, implementation, and refactoring. The skill covers performance bottlenecks, error recovery, code coverage, and documentation, helping developers refine behavior and structure while keeping tests passing.
Create starter code and configurations for components, APIs, and projects using existing project conventions. Templates include relevant types, error handling, documentation, and testing scaffolds, with comments and extension points to guide customization.
Plan system architecture by comparing technical options, component relationships, and requirements such as performance, security, and growth. Produce architecture diagrams, data flow diagrams, decision records, and technology comparisons that explain the reasoning behind a proposed design.
Turn specifications and algorithm outlines into a system architecture. Define components, interfaces, data structures, and deployment plans, then document technology choices, security measures, failure handling, and plans for supporting increased usage.
Manage GitHub pull requests from creation through review and merge. Using GitHub CLI, the skill prepares descriptions, coordinates reviewers, tracks automated checks, helps resolve conflicts, and selects merge approaches suited to the branch history.
Design and implement backend APIs, the interfaces applications use to exchange data. Cover authentication, input validation, error handling, tests, and documentation, while consulting stored implementation patterns and recording results for reuse in later development tasks.
Manage applications in the Flow Nexus marketplace, from finding and publishing apps to deploying templates with configuration settings. The skill also supports version management, usage analytics, and reviews of application quality and security.
Implement distributed data synchronization with CRDTs, data structures designed to merge concurrent updates. Covers counters, sets, shared values, and sequences, with incremental updates and tracking of dependencies between operations.
Review GitHub pull requests with specialized AI agents that check security, performance, architecture, and coding conventions. Generate actionable comments with suggested fixes, and connect review findings to automated approval or change requests.
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.
Coordinate gossip protocols, where nodes pass information among peers, for distributed systems. The skill covers update propagation, state synchronization, conflicting changes, failure detection, and tuning communication so nodes eventually reach a consistent state.
Coordinate groups of AI agents across multiple GitHub repositories. Analyze dependencies, organize shared updates and refactoring, run tests, and create linked pull requests so related projects can be changed together.
Review a codebase for readability, performance, security, and architectural issues. The skill organizes findings into actionable recommendations, helping teams identify refactoring work, dependency risks, and areas that need better tests.
Organize new features, complex implementations, and architecture changes into the SPARC stages: specification, pseudocode, architecture, refinement, and completion. Define requirements and acceptance criteria, consult existing patterns, and record lessons for future work.
Organize complex work across specialized agents with claude-flow. Start and stop agents, check their status and metrics, and manage groups by role, using shared memory to coordinate tasks such as development, reviews, and security checks.
Initialize a group of collaborating AI agents by choosing how they connect, allocating resources, and setting up communication. Establish shared-memory rules so agents record progress, share outputs, check dependencies, and signal completion.
Implement features, design APIs, and improve existing code with an emphasis on readability and maintenance. The workflow covers requirements, interface design, tests written before implementation, performance improvements, and error handling.
Coordinate multiple AI agents for feature development or refactoring across several files. The skill guides team setup, task assignment, status checks, and recording successful approaches for use in later work.
Analyze slow development workflows and agent coordination using execution times, resource use, dependencies, and communication patterns. Identify bottlenecks, prioritize causes, and build an improvement plan covering parallel work, caching, or resource allocation.
Organize GitHub work through task-specific modes for pull request reviews, issue tracking, releases, and branch management. Coordinate repository synchronization and CI/CD, the workflows that test and deploy changes, with support for multiple agents.
Design and implement ways for multiple agents or distributed computers to agree on decisions. Covers weighted voting, task allocation, coordination, and protocols that handle faulty or malicious participants, with tools for mathematical analysis.
Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.