「discovery」の検索結果

Optimize YouTube videos for search and discovery. Generates SEO-optimized titles, descriptions, tags, hashtags, and chapters. Includes keyword research and competitor analysis. Use when publishing videos, improving discoverability, or optimizing existing 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。

User research methods, customer insight gathering, and problem validation for product discovery.

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。

Generate cold email templates, LinkedIn messages, follow-up sequences, and value propositions tailored to specific industries that actually get responses and book discovery calls.

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。

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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 of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

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。

Investigate MCP tool availability, discovery, and selection, including controlled adoption evaluations.

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 an agent needs to inspect or control a real Unity Editor through Locus, especially when Unity MCP is unavailable, named-pipe discovery is needed, C# must be executed, or Unity scripts must be recompiled.

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。

Investigate a Rezolus performance recording (.parquet, .rez, .dendro) through the rezolus MCP server. Discovery before query, verdicts and marks written back, links to the viewer.

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 what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.codex/journal

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 what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.claude/journal

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。

Analyze failed or stuck proofs and propose auxiliary lemmas to help complete the proof in Isabelle/HOL or Coq. Use when encountering proof failures, stuck proof states, unprovable subgoals, or when needing to strengthen induction hypotheses. Identifies missing lemmas, suggests proof strategies, and generates helper lemmas with appropriate statements and proof sketches. Supports inductive proofs, case analysis, rewriting, and complex proof obligations.

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。

Generate templated responses for common legal inquiries and identify when situations require individualized attention. Use when responding to routine legal questions — data subject requests, vendor inquiries, NDA requests, discovery holds — or when managing response templates.

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 orchestrates autonomous discovery of brand materials across enterprise platforms (Notion, Confluence, Google Drive, Box, SharePoint, Figma, Gong, Granola, Slack). It should be used when the user asks to "discover brand materials", "find brand documents", "search for brand guidelines", "audit brand content", "what brand materials do we have", "find our style guide", "where are our brand docs", "do we have a style guide", "discover brand voice", "brand content audit", or "find brand assets".

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 generates, creates, or builds brand voice guidelines from source materials. It should be used when the user asks to "generate brand guidelines", "create a style guide", "extract brand voice", "create guidelines from calls", "consolidate brand materials", "analyze my sales calls for brand voice", "build a brand playbook from documents", "synthesize a voice and tone guide", or uploads brand documents, transcripts, or meeting recordings for brand analysis. Also triggers when the user has a discovery report and wants to convert it into actionable guidelines.

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。

Guidance for using asc cli in this repo (flags, output formats, pagination, auth, and discovery). Use when asked to run or design asc commands or interact with App Store Connect via the CLI.

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。

A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

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 a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

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。

Connects Claude Code, Codex, Cursor, and other MCP hosts to Writingmate for model discovery, text comparison, image generation, and video generation with Seedance, Sora, Veo, Kling, PixVerse, and other available models.

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 for authorized security assessment of REST, GraphQL, WebSocket, or SOAP APIs, including discovery, authentication, authorization, rate-limit, and CI/CD testing.

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。

Full-spectrum product research pipeline. Runs competitive analysis, technology trend scouting, user feedback analysis, and feature ideation. Trigger on: research, competitive research, market research, feature discovery, what should we build next, technology trends, user feedback, app store reviews, GitHub issues analysis, competitive landscape, product strategy.

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 testing a bundle-plugin locally before release — generating dev-marketplace environments, verifying component discovery, running hook smoke tests, and validating cross-platform readiness

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。

Discover and reduce task unknowns — blindspots, missing context, and unknown unknowns — before committing to a plan, implementation, review, or merge.

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。

Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, capability discovery, tool filtering, programmatic execution, or encountering context bloat, server configuration, tool execution errors.

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。