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Found 207 Skills
Audits AI systems for bias, fairness, and privacy. Analyzes prompts and datasets to ensure ethical and safe AI implementation.
Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
Connect Spice to data sources and query across them with federated SQL. Use when connecting to databases (Postgres, MySQL, DynamoDB), data lakes (S3, Delta Lake, Iceberg), warehouses (Snowflake, Databricks), files, APIs, or catalogs; configuring datasets; creating views; writing data; or setting up cross-source queries.
Configure data accelerators for local materialization and caching in Spice (Arrow, DuckDB, SQLite, Cayenne, PostgreSQL, Turso). Use when asked to "accelerate data", "enable caching", "materialize dataset", "configure refresh", "set up local storage", "improve query performance", "choose an accelerator", or "configure snapshots".
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
Query-first dataset access with @domoinc/query including filters, grouping, date grains, and performance constraints.
Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.
Sub-skill for environment and asset preparation in README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
Optional sub-skill for README-first AI repo reproduction. Use only when README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
Executes long-running tasks in background isolates to keep the UI responsive. Use when performing heavy computations or parsing large datasets.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.