Skills

aidp-profiling-tables

Profile an AIDP table — row count, per-column null %, distinct count, min/max/mean, and top-K values. Use when the user asks to profile a table, wants column statistics or a data-quality snapshot, or needs to understand a dataset's shape before using it. Runs bounded Spark SQL via the bundled aidp_s

sdk-getting-started

Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deploy

azure-compute

Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform

agent

Add an AI agent / chatbot / RAG backend on Convex with @convex-dev/agent (durable threads, messages, tools, vector search). TRIGGER when the user wants an AI assistant/chatbot/agent or 'chat with my docs' feature. Keeps the LLM key in Convex env.

brightdata-sdk

Web data extraction and discovery using the Bright Data Python SDK. Use when user asks to "scrape", "get data from", "extract", "search for", or "find" information from websites. Also use when user mentions specific platforms like Amazon, LinkedIn, Instagram, Facebook, TikTok, YouTube, Reddit, Pinte

train-sentence-transformers

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classificatio

huggingface-vision-trainer

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. C

datarobot-predictions

Tools and guidance for making predictions with DataRobot deployments, including real-time predictions, batch scoring, prediction dataset generation, and prediction explanations (SHAP/XEMP). Use when making predictions, running batch scoring, generating prediction datasets, or explaining individual p

aidp-ai-sql

Run LLM functions inside Spark SQL on AIDP via ai_generate(). Use when the user wants to summarize/classify/extract/enrich rows with an LLM directly in SQL, generate narratives over aggregated results, or do grounded RAG-style analysis in the lakehouse. Signature is model-first; available models mus

feature-usage-feed

Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, w

signals

How to query the document_embeddings table for raw signal data using HogQL. Use when you need to perform semantic search over signals, fetch every signal that contributed to a specific report, or list signal types. For browsing the curated report layer (the Inbox) — listing reports, filtering by sta

knowledge-update

Corrects outdated LLM knowledge about the Vercel platform and introduces new products. Injected at session start.

output-dev-model-selection

Pick the right LLM model for an Output SDK prompt file. Use when writing a new .prompt file, reviewing a model choice, or upgrading a stale model. Walks through priority (reasoning/balance/speed/cost), provider selection, and a live lookup against the Vercel AI Gateway model index.

databricks-mlflow-evaluation

MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with

datarobot-data-preparation

Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.

use-case-specification

Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly decl

domino-datasets

Work with Domino Datasets - high-performance, versioned filesystem storage. Covers dataset creation, snapshots for versioning, sharing across projects, mounting paths (/domino/datasets/), and performance optimization. Use when managing data storage, creating reproducible data versions, or sharing da

fiftyone-dataset-import

Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds), label formats (COCO, YOLO, VOC, KITTI), multimodal grouped datasets, and Hugging Face Hub datasets. Use when importing datasets from local files or Hugging Face, loading autonomou

finetuning

Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function

together-batch-inference

High-volume, asynchronous offline inference at up to 50% lower cost via Together AI's Batch API. Prepare JSONL inputs, upload files, create jobs, poll status, and download outputs. Reach for it whenever the user needs non-interactive bulk inference rather than real-time chat or evaluation jobs.

team

Spawn long-lived provider LLM teammates in tmux panes that you message via the native agent-team tools — multi-turn, collaborative, watchable. Use for sustained parallel build/work ("spawn workers", N teammates on N files), or when you need a collaborator you message across turns. NOT a fire-and-for

tavily-extract

Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles

signals-scout-ai-observability

Signals scout for PostHog AI observability. Watches LLM traces for cost, latency, error, volume, and eval-performance regressions, sliced by the dimensions it discovers over time, and files each validated regression as a report in the inbox.

aidp-agent-highcode

Build AIDP agents in high-code Python with aidputils + LangGraph (the code-first alternative to the low-code agent-flow canvas). Use when the user wants to write an agent in Python, use LangGraph / create_react_agent / StateGraph, call aidputils (OCIAIConf, AIDPToolConf, init_oci_llm, create_langgra