Research

Run the GPT Researcher autonomous agent to generate comprehensive deep research reports. Requires LLM and Search API keys (e.g., OPENAI_API_KEY, TAVILY_API_KEY).

The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.

This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.

Generate comprehensive scientific research-style documentation for completed coding projects. Use when the user requests project documentation, a technical breakdown, a study paper, a lecture document, or wants to understand everything about a project they just built. Triggers include phrases like "document this project," "create a study paper," "explain everything we did," "write up the full breakdown," "scientific documentation," or "I want to learn from this project." Produces formal Word documents (.docx) with academic structure, beginner-friendly explanations, and exhaustive code analysis.

Use when creating or improving educational concept files - includes curriculum research for new concepts, always updates translations before validation

This skill empowers Claude to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. It analyzes user preferences, item features, and interaction data to generate personalized recommendations. Use this skill when the user requests to build a recommendation engine, needs help with collaborative filtering, wants to implement content-based filtering, or seeks to rank items based on relevance for a specific user or group of users. It is triggered by requests involving "recommendations", "collaborative filtering", "content-based filtering", "ranking items", or "building a recommender".

Conducts user experience research and analysis to inform design decisions. Reviews first-party and third-party user data, analyzes industry trends from UX and visual design perspectives, and plans user research studies. Creates personas, customer segments, design principles, design roadmaps, and research discussion guides.

機能フラグ（Feature Flag/Feature Toggle）による段階的リリースとA/Bテスト実装スキル。リスク管理とカナリアリリースを可能にし、本番環境での安全な機能展開を実現します。Anchors:• Feature Toggles (Pete Hodgson - Martin Fowler blog) / 適用: フラグ設計パターン / 目的: Release/Experiment/Ops/Permission Togglesの適切な使い分け• Continuous Delivery (Jez Humble) / 適用: デプロイ戦略 / 目的: デプロイと機能リリースの分離による安全性向上Trigger:Use when implementing feature flags, feature toggles, A/B testing, gradual rollouts, canary releases, or managing feature lifecycles.feature flag, feature toggle, A/B testing, canary release, gradual rollout, dark launch, percentage rollout, kill switch

Guides scientific hypothesis development and testing methodology. Use when formulating research questions, developing testable hypotheses, designing experiments, or evaluating research approaches. Triggers on phrases like "hypothesis", "test if", "experiment design", "research question", "how would I test", "is it true that".

Creates detailed implementation plan from validated research. Produces task breakdown with dependencies.

Game building mechanics case studies and decision frameworks. Use when designing building systems, evaluating trade-offs, or learning from existing games. Reference-only skill with detailed analysis of Fortnite, Rust, Valheim, Minecraft, No Man's Sky, and Satisfactory building systems.

Use xAI Grok API with Agent Tools for real-time web and X (Twitter) search and synthesis. Requires XAI_API_KEY.

This skill performs protein-mediated regulatory community analysis from ChIA-PET datasets and provide a way for visualizing the communities. Use this skill when you have a annotated peak file (in BED format) from ChIA-PET experiment and you want to identify the protein-mediated regulatory community according to the BED and BEDPE file from ChIA-PET.

Use when implementing features from Jira tickets, PRDs, or user requirements. Orchestrates Research-Plan-Implement workflow with quality gates for hallucination, overengineering, and underengineering detection.

Use when encountering any bug, test failure, or unexpected behavior - IMMEDIATELY enter on first error. Four-phase framework (basic checks, root cause, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Comprehensive guide for Unity development. Provides architecture patterns, MCP tool selection, and composite task workflows. Parent skill always referenced for Unity-related tasks. See child skills for detailed implementation. Use when: Unity development in general, C# editing, scene operations, UI implementation, asset management, PlayMode testing

Use Hugging Face Smolagents framework for code-based agentic research with tool support. Supports multiple LLM providers and web search.

Generate resident schedules respecting ACGME rules, institutional policies, and individual preferences. Orchestrates constraint propagation, optimization, and conflict resolution for medical residency program scheduling.

This skill should be used when the user asks to "创建大纲", "outline", "文章结构规划", "plan article structure", or needs help structuring their content before writing. Provides collaborative outline creation with interactive questioning and research gap identification.

Run LangChain Open Deep Research agent for iterative web research and comprehensive reports. Requires LLM API keys and search API (e.g., OPENAI_API_KEY, TAVILY_API_KEY).