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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

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INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.

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Expert Proxmox VE automation using the community.proxmox Ansible collection for VM provisioning, template management, and cluster operations with minimal CLI usage.

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INVOKE THIS SKILL when creating, running, or analyzing Arize experiments. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI.

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Core principles and golden rules for writing production-quality Ansible automation, covering FQCN requirements, module selection guidance, and execution patterns using uv run.

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Implements robust error handling in Ansible using block/rescue/always patterns, retry logic with until/retries, and clear assertion patterns for graceful failure management.

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Techniques for writing truly idempotent Ansible tasks using changed_when, failed_when, and check-before-create patterns to eliminate spurious "changed" reports.

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INVOKE THIS SKILL when creating, managing, or using annotation configs on Arize (categorical, continuous, freeform), or applying human annotations to project spans via the Python SDK. Configs are the label schema for human feedback on spans and other surfaces in the Arize UI. Triggers: annotation config, label schema, human feedback schema, bulk annotate spans, update_annotations.

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INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.

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INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.

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Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.

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Automatically issue/renew HTTPS certificates using Alibaba Cloud ESA DNS + acme.sh (including wildcard *.example.com + example.com), with optional installation to Nginx. Trigger this skill when the user mentions ESA, ATrustDNS, _acme-challenge, acme.sh, Let's Encrypt, No TXT record found, InvalidRecordNameSuffix, wildcard certificate, or Nginx certificate configuration.

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Count words, characters, sentences, paragraphs, and reading time for any text using AceToolz.

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Convert images and text prompts to 3D models (.glb) using the 3D AI Studio API. Supports TRELLIS.2, Hunyuan Rapid, and Hunyuan Pro models.

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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Create HTML-based video compositions, animated title cards, social overlays, captioned talking-head videos, audio-reactive visuals, and shader transitions using HyperFrames. HTML is the source of truth for video. Use when the user wants a rendered MP4/WebM from an HTML composition, wants to animate text/logos/charts over media, needs captions synced to audio, wants TTS narration, or wants to convert a website into a video.

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Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.

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Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.

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Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).

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Passive domain reconnaissance using Python stdlib. Subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required.

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