Total 50,962 skills, AI & Machine Learning has 8536 skills
Showing 12 of 8536 skills
LinkedIn agent that helps you enrich LinkedIn profiles. You prodive a LinkedIn URL and it will return its data from LinkedIn, in a structured JSON format. It works with both People and Companies URL.
Auto-assembles review panel using deterministic rules, dispatches agents against plan file, collects verdicts.
Opik observability for LLM agents — Agent Configuration, Local Runner (opik connect), Evaluation Suites, threads, integrations. Use for "configure my agent", "connect my agent", "evaluate my agent" or "integrate with Opik".
Complete guide for integrating a new LLM backend into MassGen. Use when adding a new provider (e.g., Codex, Mistral, DeepSeek) or when auditing an existing backend for missing integration points. Covers all ~15 files that need touching.
Transform empty rooms into beautifully furnished spaces using each::sense AI. Create photorealistic virtual staging for real estate listings, commercial properties, and interior design visualization.
A prompt repetition technique for improving LLM accuracy. Achieves significant performance gains in 67% (47/70) of 70 benchmarks. Automatically applied on lightweight models (haiku, flash, mini).
Audit existing skills with Tessl scoring, metadata and trigger-coverage checks, repo conventions, and skill-authoring best practices. Use when creating or revising a skill, triaging weak self-activation, or comparing a skill against source-repo guidance such as `AGENTS.md`, `CLAUDE.md`, or repo rules, plus external skill guidance. Do not use to verify general application code or to rewrite unrelated docs.
Authoritative reference for Anthropic products. Use when users ask about product capabilities, access, installation, pricing, limits, or features. Provides source-backed answers to prevent hallucinations about Claude.ai, Claude Code, and Claude API.
Framework for automated search over task-specific model harnesses — the code around a fixed base model that decides what to store, retrieve, and show while the model works.
Cost-conscious Claude Code mode. Reduces output tokens 40-70% and overall costs 30-60% by enforcing concise responses, smart model routing, and efficient workflow patterns. Keeps full technical accuracy. Activate with /cost-mode or "enable cost mode". Auto-triggers on mentions of budget, cost, tokens, or spending.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.