Total 55,884 skills, AI & Machine Learning has 9296 skills
Showing 12 of 9296 skills
A meta-skill for generating new standard skill modules. Context-aware: analyzes user requirements to select appropriate templates (Basic/Generator/Data) and automatically populates metadata (name, tags, description) for the new skill.
MCP (Model Context Protocol) server build and evaluation guide, including local conventions for tool surfaces, config, and testing
AI agents: autonomous agents, multi-agent systems, LangChain, LlamaIndex, MCP.
Get real-time stock prices and financial info for US stocks (like AAPL, TSLA, NVDA).
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.
Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.
Principal backend engineering intelligence for Python AI/ML systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale ML services and pipelines. Focus: data quality, reproducibility, reliability, performance, security, observability, model evaluation, MLOps.
Generate fashion model imagery, virtual try-on, runway videos, and campaign visuals using EachLabs AI. Use when the user needs fashion content, model photography, or virtual try-on.
Use when designing futuristic agentic workflows, when wanting AI to proactively act on team communications, or when eliminating the bottleneck of formal specifications
Create clever, non-offensive trash talk. Rivalry-specific references, historical callbacks, memes. Fun, not mean.
Use when managing Ralph orchestration loops, analyzing diagnostic data, debugging hat selection, investigating backpressure, or performing post-mortem analysis
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.