「focus」の検索結果

Route AutoGPT Platform self-hosting, backend, frontend, and legacy Classic agent work to focused operating 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。

Routes BasicTS time-series training, dataset, model, and pipeline workflows through focused sub-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。

Route AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating 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。

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。

Ranked next offensive moves from engagement state. Reads state/loot/Killchain, runs the deterministic analyzer, elaborates the top move. Use when asked "what next", "where to focus", "prioritize", or at the start of an engagement session.

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。

CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.

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 content-focused websites with Astro — zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.

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 building products that wrap AI APIs (OpenAI, Anthropic, etc. ) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI.

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 AI-focused SRE incident response practices for LLM outages, degraded quality, runaway cost events, and safety regressions.

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。

Scene composition and orchestration skill for creating complex multi-part animations. **Triggers when:** - User wants to compose multi-element scenes - Request involves combining multiple visual elements - Scene requires act-based structure or transitions - User mentions "scene", "compose", "combine", "orchestrate" **Capabilities:** - Scene graph construction with acts and transitions - Multi-object coordination and timing - Camera movements and focus control - Layered animation sequences

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。

Kinetic typography, logo animations, and stylized motion design skill. **Triggers when:** - User wants text animations or kinetic typography - Request involves logo reveals or title sequences - Content focuses on visual impact over education - User mentions "title", "intro", "logo", "text animation", "kinetic" **Capabilities:** - Kinetic typography with dynamic text effects - Logo reveals and brand animations - Particle effects and visual flourishes - Modern motion design patterns - Attention-grabbing title sequences

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。

Body horror art direction for Blender focusing on flesh wrongness, mutation, fusion, and visceral material storytelling.

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 a new Claude Code agent (subagent) from scratch through a relentless, evidence-based interview that walks the agent's design tree decision-by-decision — entity fit, domain focus and vocabulary, role identity, anti-patterns, description, model tier, tools, and self-containment — then reviews the finished agent against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new agent or subagent. Does not build a skill or slash command — use skill-builder. Does not serve, vendor, or refresh the authoring guidance itself — use 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。

Builds a feature specification from scratch through a relentless, evidence-based interview that walks the design tree decision-by-decision, resolving dependencies as it goes. Use when the user wants to plan, design, scope, specify, or flesh out a new feature, capability, or system behavior before implementation. Produces a feature specification focused on system behaviors, not implementation detail. Does not plan a restructure of code that already exists — use plan-a-change. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features — use project-documentation. Does not design the contract for an interface — use design-an-api. Does not research open-ended options before there is a feature to specify — use research. Does not map the bounded contexts of existing code — use ddd-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。

Create original short videos using the 群演聚焦接力｜全场到主位再回合奏 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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 retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

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。

Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.

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。

Master prioritization frameworks, roadmap planning, timeline estimation, and resource allocation. Create executable roadmaps that drive focus and alignment.

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 high-converting landing pages with persuasive copy, clear CTAs, social proof, and optimized structure. Use when building sales pages, product pages, lead capture pages, or conversion-focused pages.

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。

Batch update Yoast SEO metadata (titles, descriptions, focus keyphrases) in WordPress via GraphQL. Use when the user wants to update SEO metadata, optimize titles, fix meta descriptions, or manage Yoast SEO fields across multiple posts. Supports preview mode, progress tracking, and resume capability.

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。

Pre-publish assistant for new blog posts. Use when the user wants to classify a new post with categories and tags, generate SEO metadata (title, description, focus keyphrase), or get intelligent suggestions with rationale. Works with draft content (file path, URL, or text) and suggests from existing taxonomy to maintain balanced distribution.

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。

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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。

Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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。