Data analysis, machine learning, LLM apps, and prompt design (9.1K skills)
Build workflows on OpenServ where multiple AI agents share tasks and pass results between steps. Examples cover research, writing, and image creation, with guidance on finding agents, setting dependencies, and connecting sequential or parallel work.
Use Chainbase CLI to investigate blockchain records, including token holders, prices, wallet activity, and DeFi positions. Use Tops to discover trending crypto topics, inspect related posts, and track recent Twitter/X mentions of projects or keywords.
Analyze a token’s holders and recent traders using Nansen wallet labels. Review Smart Money holdings, meaning wallets classified as informed investors, compare flows across wallet groups, and inspect who recently bought or sold the token.
Configure coding agents, AI SDKs, and HTTP clients to send model requests through AntSeed’s local proxy. Find available providers and models, select a provider, set compatible API endpoints, and verify request usage and payment activity.
Build chat, image generation, and speech transcription features on 0G Compute Network, a distributed GPU marketplace. This skill guides SDK integration, model fine-tuning, response processing for billing, and account funding through code patterns and CLI commands.
Access the Nansen API without an API key and pay per request through Tempo. Set up a Tempo-managed wallet and request data such as Smart Money flows or token holders without managing a separate Base or Solana wallet.
Tracks crypto funds and high-performing wallets using Nansen data. It retrieves net flows, trades, and holdings to show what these wallets are buying, selling, or holding, with filters for trader categories.
Plan how to split AI models and workloads across multiple GPUs. Compare memory needs, computation, communication, and waiting time to estimate completion times and choose a layout suited to response time, throughput, or cost goals.
Analyze GPU operation timings and execution traces to find costly data access, repeated computation, launch overhead, and synchronization waits. Check whether improving an individual operation actually reduces the time needed to complete a full request.
Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.
Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.
Estimate how model architecture, precision, and context length affect weight storage, generation state, computation, and memory traffic. Compare models and attention designs with separate resource estimates for input processing and token generation.
Model ongoing requests and multi-step agent work to estimate capacity, latency, and cost per completed task. Break execution into stages, account for queues and retries, and produce workload tables with average and peak resource budgets.
Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.
Guides LoopX benchmark experiments through benchmark-toolkit, covering run management, comparisons under matched conditions, and analysis of agent action histories. It helps explain outcomes and efficiency while tracking source versions, score validity, and evidence boundaries.
Use DeepChat's bundled command-line interface to compare models, generate media, transcribe audio, and extract text from images. Also inspect runtime status, artifact metadata, public settings, installed Skills, and MCP connections to external tools.
Design Apache Hudi tables through questions about data updates, scale, and usage. Receive an architecture decision record explaining the choices, plus configuration bundles and execution examples for supported Spark or validated Flink paths.
Inspect an Apache Hudi table’s storage layout using a read-only analysis tool. Get report-based findings on table size, uneven data distribution, excessive partitions, small files, and concentrated writes, with suggested next steps.
Crawl linked pages within a chosen website scope and collect clean text, Markdown, or HTML with page metadata. Use the resulting records as source material for AI search systems or chatbot knowledge bases.
Implements mathematical models from a design report as reproducible code, runs calculations, and checks results. It records methods and validation findings in a results report and produces data-based PDF charts for papers.
Generate scientific chart examples from bundled Python scripts using repeatable simulated data. Choose from templates such as raincloud plots, correlation matrices, and ROC charts, then receive PNG, PDF, SVG, and editable script files.
Turns mathematical modeling competition problems and attachments into an implementation-ready report. It breaks down questions, examines ambiguous assumptions and data quality, and defines variables, equations, constraints, solution methods, and validation steps.
Turn rambling dictation or transcribed voice memos into concise prompts for Claude Code. Remove filler and repetition, then organize the project context, goal, constraints, and expected deliverables while preserving the original requirements.
Instrument Python or TypeScript agents to record runs for Failproof AI, verify local event files, and troubleshoot missing sessions. The skill also guides building evaluator workers that score completed runs on your own infrastructure.