Total 56,869 skills, AI & Machine Learning has 9457 skills
Showing 12 of 9457 skills
Iteratively inspect traces, interview the user, and create LangSmith online evaluators one at a time. Use specifically for creating online evaluators for use within LangSmith -- use "eval-engineering" for Harbor-style online evaluations.
Generate, monitor, and download MiniMax-H3 videos through the mmx CLI. Use for H3 text-to-video, first/last-frame video, multimodal reference image/video/audio generation, H3 prompt improvement, media preflight, Pay-as-you-go API key selection, task waiting, downloads, and H3 failure handling.
Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.
Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).
Route any dropped-in input — idea, spec path, file path, PR or issue, stack trace, bug report, or bare `/cheese` — to the right workflow skill. Use as the unified entry point — phrases include "/cheese", "what should I do with this", "help me get started", "route this", or any opening message that does not already name a downstream skill.
Audits instrumentation health of existing Arize traces. Runs deterministic checks over a bounded span sample (orphaned/uncategorized/duplicate spans, flat structure, blank root I/O, unset status, missing token counts or children) and returns a ranked report. Use when the user asks why traces look empty/flat/broken, wants to verify instrumentation is healthy, find instrumentation issues, or why evals or token/cost dashboards show n/a or zero. To debug app behavior or errors, use arize-trace.
Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
INVOKE THIS SKILL when creating, running, or operating a Managed Deep Agent against the LangSmith /v1/deepagents private-preview REST API. Covers the agent → MCP server → thread → streamed run flow, tool/interrupt configuration, and the agent file tree (AGENTS.md, skills/, subagents/, tools.json).
Create and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Give your AI agents capabilities through tools (function calling). Helps you identify what your AI needs to do, create tool definitions, and attach them to AI Config variations.
cuOpt REST server — what it does and how requests flow. Domain concepts; no deploy or client code.