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Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

日本語の概要は準備中です。原文の説明を表示しています。

Use OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.

日本語の概要は準備中です。原文の説明を表示しています。

Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.

日本語の概要は準備中です。原文の説明を表示しています。

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

日本語の概要は準備中です。原文の説明を表示しています。

Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.

日本語の概要は準備中です。原文の説明を表示しています。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

日本語の概要は準備中です。原文の説明を表示しています。

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

日本語の概要は準備中です。原文の説明を表示しています。

Data Engineer IA — Expert en pipelines de données (ETL, data lakes, dbt, Airflow, streaming). Construit des pipelines fiables, scalables et auditable.

日本語の概要は準備中です。原文の説明を表示しています。

Plan end to end: draft, render in plan-canvas or Plan Builder, annotate, await approval, then hand off. Generated from plugin-commands.json; invoke as $bdb-aos:plan.

日本語の概要は準備中です。原文の説明を表示しています。

Start a new composition: project init, brainstorm, then ask whether graph or startcycle follows. Generated from plugin-commands.json; invoke as $bdb-aos:startproject.

日本語の概要は準備中です。原文の説明を表示しています。

Adds new visual examples to the tdmcp prompt cookbook documentation — both the English and Portuguese versions. Use when asked to: add more cookbook examples, create visual examples for the docs, show more surprising things you can do, add entries to the prompt cookbook, expand the cookbook, or any request to extend the cookbook with new prompts and results. Runs a 3-phase pipeline: curate candidates → write EN → write PT.

日本語の概要は準備中です。原文の説明を表示しています。

Quality-assure integrated tdmcp features: run the four PR gates + recipe/bridge tests, cross-boundary coherence checks (tool schema↔CLI↔docs↔registry, operator↔createable, bridge↔client), and live TouchDesigner validation (preview + post-cook error check) when the bridge is reachable. Use when validating/QA-ing/testing tdmcp tools, checking a feature actually cooks in TD, verifying CLI matches a tool, or gating a release.

日本語の概要は準備中です。原文の説明を表示しています。

Run the startcycle-graph pipeline: durable state, repair loop, quality gate, escalation. Generated from plugin-commands.json; invoke as $bdb-aos:graph.

日本語の概要は準備中です。原文の説明を表示しています。

Spin up N Rhino MCP sessions in parallel and fan out one agent per Rhino. Use when the user wants multiple Rhinos running simultaneously with each agent driving its own — e.g. "spawn 3 agents and have each launch Rhino", "run 5 Rhino tasks in parallel". For a single Rhino, use `/launch-rhino` instead.

日本語の概要は準備中です。原文の説明を表示しています。

Start a single Rhino MCP session. Accepts a version argument (e.g. `8`, `WIP`) and an optional explicit port. Walks forward from `10500` to pick a free port if none was given. On macOS this opens a new document in the running Rhino; on Windows it launches a new Rhino process. Use when the user asks to start a Rhino MCP session — for N parallel Rhinos use `/launch-rhinos` instead.

日本語の概要は準備中です。原文の説明を表示しています。

Browse and preview AOS skills, agents and playbooks in the local store UI. Generated from plugin-commands.json; invoke as $bdb-aos:store.

日本語の概要は準備中です。原文の説明を表示しています。

Set up the current project folder for AOS: slug, wiki, memory, Synapse map, AGENTS.md. Generated from plugin-commands.json; invoke as $bdb-aos:init.

日本語の概要は準備中です。原文の説明を表示しています。

Supervise other Claude Code sessions on this machine: roster, status requests, GO board, idle notices. Generated from plugin-commands.json; invoke as $bdb-aos:mastersession.

日本語の概要は準備中です。原文の説明を表示しています。

Check the machine and the current project, and print suggested permissions.allow entries without writing any settings. Generated from plugin-commands.json; invoke as $bdb-aos:doctor.

日本語の概要は準備中です。原文の説明を表示しています。

Build a tdmcp TouchDesigner-bridge vertical slice the house way — promote an op from /api/exec to a first-class REST endpoint (e.g. POST /api/connect, GET /api/logs, node_detail flags), add its typed method in src/td-client/touchDesignerClient.ts, its Zod envelope in src/td-client/validators.ts, rewire the tool(s) to prefer the endpoint with an exec fallback, and write py_compile + td/tests unittest + offline msw client tests. Use when adding/changing a bridge REST endpoint, editing td/, extending node_detail/get_bridge_logs, promoting connect/disconnect/param-modes/DAT-text off exec, or doing any exec→REST work — especially the td-depth bridge-robustness backlog. Honors probe-live discipline when TouchDesigner is offline.

日本語の概要は準備中です。原文の説明を表示しています。

Run a prompt repeatedly on an interval, using the harness built-in plus loop templates. Generated from plugin-commands.json; invoke as $bdb-aos:loop.

日本語の概要は準備中です。原文の説明を表示しています。

Wire builders' isolated tdmcp tool files into the shared registries, CLI, and docs as a single writer, conflict-safe, and confirm a green typecheck + build. Use when integrating/wiring/registering new tdmcp tools, adding their CLI commands, resolving build breaks after parallel feature work, or merging several builders' files into the registry without conflicts.

日本語の概要は準備中です。原文の説明を表示しています。

Query or store long-term memory with memB. Generated from plugin-commands.json; invoke as $bdb-aos:memb.

日本語の概要は準備中です。原文の説明を表示しています。

Design a tdmcp feature into an implementable spec before any code is written — choose the layer, define the Zod input schema, lay out the TD network topology, plan the bridge/Python approach, sketch a UI wireframe when relevant, and list probe-first risks + the test plan. Use whenever a tdmcp feature idea needs a design/wireframe/spec, or when someone asks to plan/scope/architect a new tool, generator, effect, control, or AI prompt for the TouchDesigner MCP server.

日本語の概要は準備中です。原文の説明を表示しています。