Skills for “llm”

Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin.

…or HITLTrigger. Requires Airflow 3.1+. Not for AI/LLM task calls (see…

…with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llm_branch, @task.embed), switching from model strings/objects to connection-based LLM configuration, updating…

…AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, then rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git…

…agent-advisor, gcp-to-aws, heroku-to-aws, and llm-to-bedrock: once their output is final, this skill checks whether a relevant Activate offer exists for what the founder is building or migrating and, if…

…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…

Generates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my…

…or migrating an existing GCP/Azure/Heroku workload or an LLM SDK rewrite (see gcp-to-aws / azure-to-aws / heroku-to-aws /…

…offline inference for a self-hosted 1B to 8B generative language model on AWS while minimizing GPU spend and recovering automatically from Spot interruption. It prescribes AWS Batch backed by Amazon ECS Managed…

Subscribe to cost events (cost:http:request, llm:generation:metering) to forward LLM and API spend to your own observability system. Use when adding cost/spend logging, building a cost observability integration, or…

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

Wire encrypted credentials to environment variables using the credential: convention. Use when setting up LLM provider keys (ANTHROPIC_API_KEY, OPENAI_API_KEY) or any env var that should come from encrypted credentials.

Calculate and display the cost of an Output SDK workflow execution run. Use when checking LLM token costs, API service costs, or total spend for a specific workflow run.

Create step functions in steps.ts for Output SDK workflows. Use when implementing I/O operations, error handling, HTTP requests, or LLM calls.

Implement LLM text streaming in Output workflow steps with generateTextWithStreaming, Agent.generateWithStreaming, streamText, or Agent.stream. Use when adding token progress, onChunk callbacks, or handling streamText…

Create .prompt files for LLM operations in Output SDK workflows. Use when designing prompts, configuring LLM providers, or using Liquid.js templating.

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…

Design effective LLM judge .prompt files for evaluators. Use when creating judgeVerdict/judgeScore/judgeLabel prompts, or when existing judges produce unreliable results.

…guide to Output.ai Framework for building durable, LLM-powered workflows orchestrated by Temporal. Covers project structure, workflow patterns, steps, LLM integration, HTTP clients, CLI commands, and the full…

Validate LLM judges against human labels using TPR/TNR metrics and train/dev/test splits. Use after writing a judge prompt to verify it agrees with human judgment.