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Found 2,129 Skills
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
Set up GitHub Actions workflows for CI/CD with automated testing, linting, and deployment for Python/UV projects. Use when creating CI pipelines, automating tests, or setting up deployment workflows.
Wrap an existing Python agent as an Agent Stack service using agentstack-sdk server wrapper, without changing business logic.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search. Use this skill when the user mentions "mem0", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI, Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user doesn't explicitly say "mem0" but describes needing conversation memory, user context retention, or knowledge retrieval across sessions.
Build, tune, and operate Ruff for Python linting, formatting, and editor/CI integration. Use when adding or updating Ruff configuration, migrating from Black/Flake8/isort, selecting rule families, enforcing fix safety, or debugging lint/format behavior in local development, pre-commit, and CI.
Build and deploy applications on inference.sh. Use when getting started, understanding the platform, creating apps, configuring resources, or needing an overview of inference.sh app development. Supports both Python and Node.js. Triggers: inference.sh app, belt app, inf.yml, inference.py, inference.js, deploy app, app development, build app, create app, GPU app, VRAM, app resources, app secrets, app integrations, multi-function app
Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.
Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
Guide for using Microsoft MarkItDown - a Python utility for converting files to Markdown. Use when converting PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs, Jupyter notebooks, RSS feeds, or Wikipedia pages to Markdown format. Also use for document processing pipelines, LLM preprocessing, or text extraction tasks.
Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns for Python and TypeScript. Includes 24 working code examples.
Test-driven development workflow enforcement for Python and React projects. Use when the user requests TDD, test-first development, or red-green-refactor methodology. Enforces strict cycle: write ONE failing test -> implement minimum code to pass -> refactor while green -> repeat. Applies to both backend (pytest) and frontend (Testing Library). Changes agent behavior to write tests before code. Does NOT provide testing patterns (use pytest-patterns or react-testing-patterns for how to write tests).