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Found 2,408 Skills
Format and validate code in various languages. Python, JavaScript, JSON, YAML, Markdown, and more. Uses standard formatters when available.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
[Hyper] Update semantic versions across node/rust/python projects, keep discovered version files synchronized, and prefer the installed `git-commit` skill for the final git step with a direct fallback when it is unavailable.
Create reproducible, cross-platform development environments with Flox — a declarative environment manager built on Nix. ALWAYS use this skill when the user needs to: set up a project with system-level dependencies (compilers, databases, native libraries like openssl, libvips, BLAS, LAPACK); configure reproducible toolchains for Python, Node.js, Rust, Go, C/C++, Java, Ruby, Elixir, PHP, or any language; manage environments that must work identically across macOS and Linux; pin exact package versions for a team; run local services (PostgreSQL, Redis, Kafka) alongside development tools; onboard new developers with a single command; or solve 'works on my machine' problems. Especially valuable for AI-assisted and vibe coding — Flox lets agents install tools into a project-scoped environment without sudo, system pollution, or sandbox restrictions, and the resulting environment is committed to the repo so anyone can reproduce it instantly. Use this skill even if the user doesn't mention Flox — if they describe needing reproducible, declarative, cross-platform dev environments with system packages, this is the right tool. Also use when the user mentions .flox/, manifest.toml, flox activate, or FloxHub.
Update LLM prices in the repo: Use this skill to snapshot live LLM pricing into a checked-in file so billing or cost math can run offline with deterministic rates. Use for any language or stack (TypeScript, Python, Go, JSON registries, etc.) — not only typescript. Use when the user wants pinned prices, wants to remove a runtime dependency on the Narev API, wants to refresh a committed pricing file, or mentions "snapshot pricing", "freeze prices", "pin model rates", "regenerate pricing file", "update pricing in the repo", or "sync token pricing from Narev".
Find focused, runnable Deepgram recipes for a specific feature × language. Use whenever someone wants a minimal working code snippet for ONE feature (transcribe URL, diarize, smart-format, voice agent connect, etc.) rather than a full starter app. Recipes are under 50 lines, read DEEPGRAM_API_KEY from env, and ship with a runnable example_test. Covers Python, JavaScript, Go, .NET, Java, Rust, and the Deepgram CLI.
Audit an AI agent skill for security risks before installing or trusting it. Runs a deterministic scanner (regex patterns, Python AST analysis, source-to-sink taint tracking, and YARA signatures) and then reasons about intent — catching prompt injection, credential exfiltration, persistence, memory poisoning, malicious code, supply-chain risks, and description-vs-behavior mismatch. Make sure to use this skill whenever the user wants to scan, audit, vet, review, or check the safety of a skill, plugin, SKILL.md, or agent tool — whether it is a local folder, a zip/.skill file, or a cloned repo — and whenever someone asks "is this skill safe to install?".
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Operator API, nsight-compute, compute-sanitizer。
Use when starting, relaunching, verifying, or debugging Windows localhost web apps, API servers, or dev services, especially long-running dev servers, bounded readiness polling, interrupted command cleanup, PowerShell startup, execution policy, Corepack/package-manager, Python runtime, spawn EPERM, duplicate Path/PATH, port conflict, detached launch, sandbox persistence, or empty-log failures.
Migrate Python Google Agent Development Kit (ADK) applications to Pydantic AI. Use for `LlmAgent`, `Runner`, sessions, state, memory, tools, callbacks, plugins, graph or dynamic workflows, resumability, events, artifacts, MCP, and ADK deployment boundaries.