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概要と使いどころ
Automates Metabase cards, dashboards, Remote Sync, embedding, tenants, and the Agent API/MCP server for AI workflows. Use when scripting, promoting, or embedding Metabase content.
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。
Chooses subagent, team, workflow, or debate and launches it on Claude Code or Codex. Use when delegating, running agent review boards, or installing shared agents.
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。
Designs coding-agent plugin systems. Use when adding plugin manifests, extension points, or reloadable integrations, or keeping plugin cache compatible across CLI upgrades.
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。
Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, embeddings, MoE or SSM. Use when picking or scaling an architecture, or self-host vs API.
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。
Builds repeatable deep-research workflows for verified synthesis. Use when producing evidence-backed briefs, comparisons, dossiers, or research pipelines.
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。
Designs approval and sandbox policy for local coding agents: permission modes, isolation, network and destructive-command guards. Use when setting approvals. Not cloud sandboxes.
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。
Designs and runs agent hives and multi-agent waves. Use when setting hive roles, code gates, dependency-graph fan-out, or verifier, vote and loop-until-dry passes.
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。
LLM serving for latency, batching, caching, quantization, and routing. Use when tuning vLLM or SGLang, sizing KV cache, adding speculative decoding, or cutting serving cost.
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 per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
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。
Designs RAG retrieval for LLM grounding: chunking, BM25+vector hybrid, reranking, GraphRAG. Use when adding embeddings to SQLite FTS5 search or running held-out retrieval eval.
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。
Simplifies recently changed or explicitly scoped code while preserving observable behavior, error semantics, side effects, ordering, and public contracts. Use for readability cleanup, dead-code removal, reducing nesting, eliminating redundant wrappers, or making an implementation easier to maintain. Not for architecture redesign, new behavior, speculative cleanup, or a refactor without an adequate safety net.
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。
Audit a .glb or .gltf and make it small, correct, and fast to load in a browser, with a measured before/after report. Use when a model is "too big" or "slow to load", when the user asks to "optimize this GLB", "compress this glTF", or "export for web", or when they mention Draco, meshopt, gltfpack, KTX2, Basis, texture VRAM, draw calls, or a Blender / CAD / photogrammetry / Pascal export headed for Three.js, React Three Fiber (drei useGLTF), Babylon.js, or model-viewer. Covers inspection, spec validation, geometry and texture compression, scene-graph cleanup, and the correctness checks (metres, Y-up, node names, animations, PBR fidelity, alpha modes, vertex colors) that must not regress. Not for editing or modelling geometry, authoring or laying out a scene, generating 3D from text or images, converting from FBX/OBJ/CAD, or debugging a runtime frame rate that has nothing to do with asset size.
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。
Help order groceries for delivery. Concierge-style flow — store selection, occasion-based list building, budget tracking, and cart assembly.
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。
Help plan an event — from a birthday dinner to a wedding. Scales to the size of the occasion. Handles venue research, guest lists, timelines, vendors, and budgets.
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。
Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires a configured Blender MCP backend (official or ahujasid) and Python with opencv-python, numpy, scipy installed.
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。
Create and edit 3D meshes in Blender — primitives, hard-surface modeling, mesh operators, modifier stacks (Bevel, Subdivision, Boolean, Mirror, Array, Solidify), bmesh-level edits, retopology basics. Use whenever the user asks to "make/model/create/build a 3D object", "shape/sculpt this", "add a cube/sphere/cylinder/etc.", "extrude/inset/bevel this face", "add a modifier", or any geometry-creation request that isn't a wireframe trace. Make sure to use this skill even if the user does not say "model" — also covers "make a sword", "build a chair", "add a door", "carve out a hole". Pairs with blender-materials for look-dev and blender-pro-workflow for full pipelines.
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。
Drive Blender from natural language: create or edit 3D models, materials, lighting, cameras, renders, animation, and exports through Blender MCP. Route complete scenes to domain skills and wireframe/reference requests to reconstruction skills. Also use for 3D requests that do not name Blender. Requires a configured official or ahujasid MCP backend.
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。
Animate objects, cameras, lights, and properties in Blender — keyframes, F-curves, easing (Bezier, Linear, Sine, Bounce, Elastic), shape keys (morph targets / blendshapes / visemes), drivers (Python expressions on properties), NLA actions for reuse and layering. Use whenever the user asks to "animate this", "make it move / rotate / scale over time", "add keyframes", "loop / oscillate", "shape key / morph / blendshape", "visemes for lip sync", or any time-based property change. Make sure to use this skill even if the user does not say "animate" — also covers "spin it slowly", "make it wave", "fade in / out", "pulse", "facial expression".
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 reviewing PlainTab architecture, looking for refactoring opportunities, reducing coupling, improving testability, or making modules easier for agents to understand without redesigning stable behavior.
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。
YouYing Shopee product selection tool supporting product query and filtering across all Shopee marketplaces, covering Malaysia, Taiwan (China), Indonesia, Thailand, Philippines, Singapore, Vietnam, Brazil, Mexico, Chile, and Colombia. Triggered when users mention Shopee product selection, Shopee product search, Shopee bestsellers, Shopee market analysis, Shopee category selection, Shopee keyword selection, Shopee sales filtering, Shopee price filtering, Southeast Asia e-commerce product selection, Shopee product search, Shopee product selection, Shopee bestsellers, or Shopee market analysis. Even if the user does not explicitly mention "YouYing" or "Shopee," this skill should be triggered whenever their need involves searching for products or filtering Shopee product data on the Shopee platform.
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 Amazon product historical time-series data, including price trends, BSR (Best Sellers Rank) trends, rating changes, seller counts, and monthly sales, supporting any ASIN across multiple Amazon marketplaces. Triggered when users mention price history, price tracking, BSR history, BSR trends, historical pricing, price fluctuations, Nexscope data, rank history, price drop alerts, lightning deal historical prices, Buy Box price trends, coupon prices, FBA/FBM price comparison, seller count changes, rating trends, sales history. Even if users do not explicitly mention "Nexscope" or "time-series data", this skill should be triggered whenever the task involves analyzing Amazon product-level historical data (such as price, rank, or sales trends over time).
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。
Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP analysis.
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。
Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.
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。
Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.
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。