Total 31,514 skills, AI & Machine Learning has 5098 skills
Showing 12 of 5098 skills
After the task execution is completed, prompt the user to open a new Agent to review the uncommitted git code. Athletes should not act as referees; proceed with the wrap-up only after the review is approved.
CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel performance.
Workflow Checkpoint Basic Capabilities (Focus on Save and Resume): Record checkpoint progress and resume context in GitHub Issues. Applicable to any workflow stage, supporting automatic triggering and high-frequency manual calls. Keywords: save, resume, checkpoint, issue.
Expert prompt engineering for LLM applications including prompt design, optimization, RAG systems, agent architectures, and AI product development.
Expert ML engineering covering model development, MLOps, feature engineering, model deployment, and production ML systems.
Systematically reduce the AI detection rate to below 30%, and add a human touch through a three-round review process (content, style, details). Use this skill when users mention phrases such as "too AI-like", "sounds written by AI", "reduce AI detection rate", "more human-like", "more natural", or "colloquial"
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design. Uses RFdiffusion for backbone generation, ProteinMPNN for sequence design, ESMFold/AlphaFold2 for validation. Use when asked to design protein binders, therapeutic proteins, or engineer protein function.
Create new agent skills following the Agent Skills specification. Use when asked to "create a skill", "add a new skill", "write a skill", "make a skill", "build a skill", or scaffold a new skill with SKILL.md. Guides through requirements, writing, registration, and verification.
High-velocity solo development workflow. Idea to production same-day. 9 commands: plan, spike, ship, review, spec-review, focus, done, drop, workflow. Auto-activates on: "plan", "spec", "ship", "spike", "spec-review", "review spec", "analyze spec", "challenge spec", "focus", "what should i do", "prioritize", "overwhelmed", "what should i work on", "done", "finish", "complete", "drop", "abandon", "workflow", "what's next", "whats next", "next step", "what now".
Quickly test and compare LLM models via OpenRouter. Find the fastest/cheapest model, compare response quality. Trigger words: openrouter, test model, compare models, find fastest model, find cheapest model