2,556 件 ・ 関連度順
概要と使いどころ
Use when asked to organize finances, create a budget, plan debt payoff, build savings goals, audit subscriptions, or do any personal money management task. Triggers on "budget", "finances", "savings", "debt", "subscriptions", "emergency fund", "investing", "cash flow", "money system", "financial 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。
Use when a completed Shell scenario planning exercise needs to be converted into a TikTok script. Triggers when user says "make a tiktok", "tiktok script", "generate tiktok", or "content from scenario". Requires a completed scenario with narratives, findings, and worldview integration.
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。
Operate the Fengshenbang-LM Fengshen package for pipelines, model families, data/training utilities, examples, and conversion planning.
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 this repo skill for DiscoArt image generation, configuration/prompt scheduling, CLI, Jina serving, Docker runtime planning, and troubleshooting.
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 Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.
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。
Guides CUDA-accelerated cuRobo v2 robotics workflows for robot models, differentiable kinematics, collision-aware IK and motion planning, trajectory optimization, MPC, perception mapping, and motion retargeting.
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 DeepVariant and DeepTrio for genomics variant-calling command planning, input validation, pipeline-stage troubleshooting, custom training, and report 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。
Use Chronos Forecasting for pretrained time-series forecasting, Chronos-2 covariates, data validation, fine-tuning, evaluation, and deployment planning.
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 AiZynthFinder for retrosynthetic planning, configuration, route analysis, custom extensions, and focused development workflows.
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 this skill for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection. Covers the local AlphaFold 3 inference package, its JSON dialect, run_alphafold workflow, outputs, troubleshooting, and safe helper scripts.
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 AReaL for large-scale asynchronous LLM reinforcement learning, agentic RL, distributed post-training, inference/agent services, backend planning, and customization workflows.
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。
Collaborative brainstorming mode for thinking through problems, ideas, and features before planning. Use when the user wants to discuss, explore ideas, or think through a problem without writing 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 when creating, writing, reviewing, annotating, repairing, refactoring, or validating readable Verilog RTL, including synthesizable Verilog-2001 .v files, existing-RTL analysis, semantic comment annotation, testbench scaffold planning, ASIC-quality review, local or remote Vivado/xsim validation, evidence-backed repair, and workflow trace diagnosis.
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 and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。
This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project. NOT for planning a scene's structure, subtext, or outcome (use scene-craft), drafting without an outline (use discovery-drafting), or revising existing chapters (use revision-continuity).
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。
This skill should be used when the user asks to "revise a chapter", "continuity check", "find inconsistencies", "audit character state", "check timeline consistency", "developmental edit", "structural revision", "reverse outline", "cut a subplot", "revision passes", "what pass next", "pacing check" as a revision pass, "clue check", "cut to a word count", or "length pass", or to prepare existing story material for the next revision pass. NOT for planning book structure (use plot-structure), scene-level craft (use scene-craft), voice consistency (use voice-style), or reconciling a chapter drafted by discovery (use discovery-drafting).
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 starting any Blender asset task, planning a production pipeline, coordinating multiple disciplines, or matching a reference photo/concept art. Invoke for game-ready assets, hard surface, environment, character, creature, export, or any multi-skill Blender workflow.
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 preparing a Spring Boot 3 application for Boot 4, upgrading to Boot 3.5 first, removing deprecated APIs, auditing dependencies, or planning a staged Framework 7 migration.
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。
Reduce AWS spend with rightsizing, autoscaling, commitment planning, and storage lifecycle policies. Use when running FinOps reviews, lowering cloud bills, or improving cost-per-request metrics.
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。
Authorized multi-stage attack-path planning and orchestration spanning reconnaissance, initial access, privilege escalation, lateral movement, and reporting. Entry point for full engagements and cross-phase operations.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
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。
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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 planning a modeling solution, selecting the next useful step or briefing a specialist.
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。