Skills

pinecone:query

Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY envi

microsoft-foundry

Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent

otel-genai-instrumentation

Guides instrumentation of GenAI/LLM applications with OpenTelemetry for Honeycomb, including content capture and agent failure detection. Trigger phrases: "instrument my GenAI app", "add tracing to LLM calls", "trace AI agent", "instrument OpenAI", "instrument Anthropic", "GenAI observability", "tra

ml-best-practices

CRITICAL RULE: You MUST use this skill whenever the task involves any machine learning tasks or data analysis. Use this skill if the user's prompt or requirements mention any of the following: * Clustering * Classification * Regression * Time series forecasting * Statistical testing * Model comparis

fiftyone-zoo-remote-model

Use when integrating a model into FiftyOne's remote model zoo — detection, classification, segmentation, embedding, keypoint, or vision-language (VLM) models loaded via `register_zoo_model_source` and `load_zoo_model`, then applied with `dataset.apply_model`. Also for debugging zoo registration, `ma

dataset-evaluation

Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against

seo-aeo-geo

Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI assistants. Use this skill whenever the user wants to optimize for AI search, get cited by language models, appear in AI overviews, build llm

output-dev-agent-class

Use the Agent class for multi-step tool loops, conversation history, and reusable LLM agents. Use when building agents with skills, structured output, or stateful conversations.

fiftyone-sdk-guidance

Answers FiftyOne Python SDK questions with accurate, runnable code. Use when the user asks how to do something in the FiftyOne Python SDK, asks a docs question, no skill or operator covers their goal, or the agent is unsure of the correct FiftyOne method, argument, or field path while writing code —

together-chat-completions

Real-time and streaming text generation via Together AI's OpenAI-compatible chat/completions API, including multi-turn conversations, tool and function calling, structured JSON outputs, and reasoning models. Reach for it whenever the user wants to build or debug text generation on Together AI, unles

seed

Seed the Convex database with starter/fixture data or import an existing dataset (CSV/JSON). TRIGGER when the user wants sample/seed data or to import a dataset into Convex. Idempotent, schema-matching.

azuresql-db-rag

Builds local vector search, RAG, embeddings, and semantic search on Azure SQL Developer using the native VECTOR type and VECTOR_DISTANCE. Use when you need to store embeddings, do similarity search, top-k nearest neighbor, cosine distance, retrieval-augmented generation, "find similar documents", ch

tavily-search

Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current infor

airunway-aks-setup

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to AKS\", \"GPU inference

fiftyone-find-duplicates

Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.

hf-cli

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastruc

huggingface-datasets

Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.

aws-secrets-manager

Secret safety for AWS Secrets Manager, secret management, credentials, API keys, tokens, and passwords. Prevents AI agents from directly fetching secret values and teaches runtime dynamic references with asm-exec so plaintext never enters the LLM context window.

output-eval-dataset-design

Design diverse eval datasets using dimension-based variation. Use when bootstrapping eval datasets, when real traces are sparse, or when existing datasets miss edge cases.

deepeval-tracing

Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangCha

together-embeddings

Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text gen

query-patterns

Opinionated guidance for constructing and interpreting Honeycomb queries on trace and event datasets — operation selection (percentiles not AVG, HEATMAP for distributions), relational field patterns (root., parent., any., none.), calculated fields, query math, and result interpretation (P99/P50 rati

domino-modeling-assistant

Enable AI-assisted model development within Domino by writing needed model and training code and using MCP (Model Context Protocol) servers to execute domino jobs. AI coding assistants like Cursor and GitHub Copilot can execute commands as Domino jobs, maintaining security, governance, and reproduci

together-volcano

Install and use the Volcano batch scheduler on a Together AI Kubernetes GPU cluster for gang scheduling. Covers installing Volcano, creating queues, submitting all-or-nothing gang-scheduled jobs (vcjobs), and verifying placement. Reach for it when a job on a Together cluster needs its pods scheduled