DevOps skills

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

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ai-agents/ と dotfiles/ の編集内容を各 AI CLI（~/.claude, ~/.codex）と ~/.config へ反映するデプロイ手順。「設定を反映」「デプロイ」「~/.claude に配って」「スキル/エージェント/設定を更新したから配布」等を求められたときに使用する。

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Change conviction level or status on a single thesis. Use when user says "change conviction", "downgrade", "upgrade", "move to monitoring", "close [TICKER]", or "set [TICKER] to [level]".

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Systematically evaluate weak, stale, or low-conviction theses for upgrade, monitoring, or closure. Use when user says "prune", "kill list", "clean up theses", "what should I close", or "reduce portfolio".

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Conviction-trigger surveillance via Quartr keyword alerts — parse every active thesis's Conviction Triggers for named observables, propose keyword alerts scoped to the Vault Active watchlist (or market-wide for macro observables), create them ONLY on explicit user confirmation, and reconcile the alert set against the book as theses open/close. Use when user says "monitor", "set up alerts", "keyword alerts", "watch for X", or "reconcile alerts".

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Extract point-scale CMIP6 historical and SSP future climate data from the Pangeo Google Cloud catalog and convert them into bias-corrected additive or multiplicative anomalies for LSM or EcoSIM climate forcing. Use when a task needs site latitude/longitude, scenario-specific CMIP6 variables, vapor pressure derived from huss and ps, calendar-aware day-of-year climatologies, or projected change summaries for the 2050s and 2090s.

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Read and analyze log entries from Cloud Logging using the gcloud CLI.

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Dockerベースのアプリケーションのデプロイメント手順書（Markdown）を作成するスキル。「デプロイ手順書」「デプロイメント手順」「deployment guide」「本番環境への配置手順」「サーバーへの配置手順」などの依頼でトリガーする。Docker Composeビルド→イメージ転送→サーバー配置→設定→動作確認→cron設定の流れで手順書を生成する。

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GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

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Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.

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Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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Use when: User wants to extend Docker with custom tools, personalize the Docker environment, or set up user-specific Docker customization. Triggers: 'extend docker', 'docker-extend', 'add tools to docker', 'customize docker', 'add my tools to the container', 'personalize docker setup', 'docker user setup', 'install tools in docker'. Does: Interactively sets up Dockerfile.user and docker-compose.override.yml so users can add personal tools to their Docker environment without affecting maintainer files or committing user-specific config to git.

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Operate and maintain OpenViking server - configure, install, start, stop, and cleanup the server. Use when need to setup or manage OpenViking service deployment.

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Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

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Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges

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Deploy your own Open-Inspect instance. Use when the user wants to set up, deploy, or onboard to Open-Inspect. Guides through repository setup, credential collection, Terraform deployment, and verification with user handoffs.

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Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

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Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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