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概要と使いどころ
Builds and backtests demand and revenue forecasts: rolling-origin backtests, prediction intervals, reconciliation, zero-shot foundation models. Use when forecasting time series.
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Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
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 operating models for AI in product teams. Use when planning platform ownership, provider strategy, data boundaries, evals, or sensitive-data controls.
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 DPO, GRPO/RLVR, reward models, and over-optimization controls. Use when adapting a pretrained or SFT model with preference or verifiable reward signals.
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 analytics engineering layers for metrics, contracts, and BI-ready models. Use when shaping dbt or SQLMesh marts, metric governance, lineage, or data quality.
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。
Runs local and self-hosted LLMs with Ollama, LM Studio, MLX, llama.cpp, and Open WebUI. Use when choosing GGUF quants, sizing VRAM, or running models privately.
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。
Multi-model agent orchestration using specialized agents for planning, coding, research, math/science, visual analysis, and adversarial review. Use when tasks are complex enough to benefit from different models' strengths, when you want adversarial review to catch blind spots, or when coordinating multi-step workflows across agent roles. Triggers on complex projects, multi-step tasks, architecture decisions, or when explicitly requested.
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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。
Validate and repair source-locked Blender models using named landmarks such as leaf tips, shell corners, eyes, smile, rim thickness, aura center/radius, and view-specific depth markers. Use when bbox/IoU is insufficient and the model must align to templates at designed feature points.
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。
Closed-loop compare-adjust-repeat workflow for fitting Blender models to supplied front/side/back/top templates and originals. Use when the user asks to compare the product to templates/originals and adjust until it fits across all dimensions, or when all views must pass measurable bbox/centroid/silhouette/edge validation before export.
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 the Blender MCP addon to be running (port 9876) 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。
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。
Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/numpy; pairs with blender-uv-texturing, wireframe-to-3d, blender-modeling, blender-materials, and blender-export.
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。
Channeling the documented thinking of named notable figures (an 'itako' medium) — their mental models, heuristics, and decision-style as advisory lenses. Not for decisions (Magi) or personas (Cast).
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。
Implementing robust business logic, API integrations, data models, and reproducible AI image-generation code with type safety. Use for production implementation, Gemini image API pipelines, or interactive pair programming.
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。
Designing red team attack scenarios, threat models, MITRE ATT&CK/OWASP application, Purple Team exercises, and AI/LLM red teaming. Use when adversarial security validation is needed.
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。
Perform visual similarity search for utility model patents using an image URL, with filtering by country, legal status, date ranges, and assignee. Supports utility model patents only (type U) with shape-only or shape+pattern+color matching models. Triggered when users mention utility patent image search, utility model patent search, utility patent infringement check, product structure patent search, or utility model visual search. Even if the user does not explicitly mention "utility patent," this skill should be triggered whenever the need involves searching for similar utility model patents through an image. For design patents, use ecommerce.Nexscope-patent-image-search 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。
AI-driven image generation and editing tool for creating high-quality product images. Triggered when users ask to generate images, create product pictures, edit photos, text-to-image, image-to-image, replace backgrounds, transform styles, composite products into scenes, swap models, or create any type of AI-generated visual content. Even if the user does not explicitly say "AI image," this skill should be triggered whenever the request involves creating, modifying, or transforming images.
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。
Cross-model benchmark for gstack skills. (gstack)
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 teacher think-aloud that models historical thinking strategies with a specific document. Use when planning explicit strategy instruction or when students follow a protocol without understanding the underlying reasoning.
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。
Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.
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。
Structured reasoning frameworks for complex problems. 19 analytical frameworks, 12 cognitive bias detectors, 10 decomposition methods, 10 mental models, Cynefin domain classification, ethical dimension check, and 6 communication patterns. McKinsey-grade problem solving for AI coding assistants.
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 one-page HTML/PDF resumes from copy-paste YAML using one of 12 official templates. HARD GATES: deliver exactly one A4 page; never invent a layout — copy assets/templates/<id> into output/<id>/ and edit that copy only. Keep a full inventory in raw.data.yaml. For each job, COPY selected entries into resume.data.yaml (polish there), then apply that file to a template. Apply pasted YAML by replacing visible text in the official resume.html, then run QA. Default template is basic-a4. Use when creating, iterating, polishing copy, migrating, exporting, or designing resume/CV HTML/PDF. For polishing resume copy, prefer writing-oriented models (Claude Fable 5 / Claude Opus 4.6-4.7 or Opus 5 / Kimi K3 / GLM-5.1-5.3-max / Qwen 3.8 Max); do not use GPT-5.6 Terra/Luna or GPT-5.5 Instant.
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。