pytorch-lightning
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
608 skills found
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.
Expert code reviewer for Rust projects. Performs comprehensive quality, security, performance, and architectural analysis using Bazel and project-specific conventions.
Reference for all MCP tools exposed by the CCOS server, enabling capability discovery, session management, and governed RTFS execution for autonomous agent workflows.
Architect multi-agent systems to overcome context limits, using patterns like supervisor, swarm, and hierarchical models to manage complex workflows.
Expert Solana Anchor development: build programs, manage PDAs, implement SPL tokens, handle security audits, and perform fuzz testing with Trident.
Structured, template-driven workflow for end-to-end feature development including coding, automated testing, verification, and session-based improvement.
Designer's eye QA: detects and automates fixes for visual inconsistencies, spacing, hierarchy, and UI polish issues. Iteratively verifies with before/after screenshots.
Diagnoses and resolves common Flutter runtime and layout errors such as RenderFlex overflow, unbounded constraints, and state management issues.
Automated security skill for identifying and validating XSS vulnerabilities, including Reflected, Stored, and DOM-based attacks across various contexts.
A security scanner for Claude Skills to detect malicious code, data exfiltration risks, and unauthorized system access before installation.
Break down complex development requests into sequenced, actionable tasks for multi-agent delegation in Claude Code environments.
Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.