データ・AIのスキル

Migration specialist for zero-downtime schema changes, data migrations, and backward-compatible evolutionUse when "migration, schema change, database migration, zero downtime, backward compatible, rollback, blue green, data migration, migration, schema, database, zero-downtime, backward-compatible, rollback, blue-green, feature-flag, ml-memory" mentioned.

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。

Comprehensive expertise in blockchain data analysis using Dune Analytics, custom indexers, and on-chain data querying. Covers SQL for blockchain, dashboard creation, protocol metrics, and alpha discovery. Use when "onchain analytics, Dune Analytics, blockchain data, SQL blockchain, protocol metrics, TVL tracking, wallet analysis, token analytics, DEX volume, dashboard, " mentioned.

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 designing multi-tenant SaaS architectures, implementing tenant isolation, data partitioning strategies, or building billing/metering systems - covers pooled, siloed, and hybrid modelsUse when ", " mentioned.

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。

Comprehensive guide to using LLMs throughout the game development lifecycle - from design to implementation to testingUse when "ai game development, llm game dev, claude game, gpt game, ai coding games, vibe coding game, prompt game development, llm, ai, game-development, workflow, prompting, coding, prototyping, claude, gpt, cursor" mentioned.

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。

Building AI-powered NPCs that maintain personality, remember conversations, and never break characterUse when "npc dialogue, ai npc, llm npc, character dialogue, npc personality, ai character, dialogue system llm, talking npc, conversational npc, dynamic dialogue, llm, npc, dialogue, ai-characters, personality, memory, game-ai, conversational-ai, role-playing" mentioned.

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。

Knowledge graph specialist for entity and causal relationship modelingUse when "knowledge graph, graph database, falkordb, neo4j, cypher query, entity resolution, causal relationships, graph traversal, graph-database, knowledge-graph, falkordb, neo4j, cypher, entity-resolution, causal-graph, ml-memory" mentioned.

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。

LLM application architecture expert for RAG, prompting, agents, and production AI systemsUse when "rag system, prompt engineering, llm application, ai agent, structured output, chain of thought, multi-agent, context window, hallucination, token optimization, llm, rag, prompting, agents, structured-output, anthropic, openai, langchain, ai-architecture" mentioned.

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。

Integrating local LLMs into Godot games using NobodyWho and other Godot-native solutionsUse when "godot llm, nobodywho, godot ai npc, gdscript llm, godot local llm, godot chatgpt, godot 4 ai, godot, llm, nobodywho, gdscript, game-ai, npc, local-llm" mentioned.

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。

GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully. 2025 lesson: GraphQL isn't always the answer. For simple CRUD, REST is simpler. For high-performance public APIs, REST with caching wins. Use GraphQL when you have complex data relationships and diverse client needs. Use when "graphql, graphql schema, graphql resolver, apollo server, apollo client, graphql federation, dataloader, graphql codegen, graphql query, graphql mutation, graphql, api, apollo, schema, resolvers, dataloader, federation, typescript" mentioned.

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。

Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.

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。

Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned.

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 adapting large language models to specific tasks, domains, or behaviors - covers LoRA, QLoRA, PEFT, instruction tuning, and full fine-tuning strategiesUse when ", " mentioned.

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 implementing GDPR compliance, handling data subject requests, conducting DPIAs, managing consent, or responding to data breaches - covers all key GDPR requirementsUse when ", " mentioned.

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。

Graphile Worker expert for high-performance PostgreSQL job queues with trigger-based job creation and millisecond job pickup via LISTEN/NOTIFY. Use when "graphile worker, postgres trigger job, listen notify queue, postgraphile worker, database trigger queue, transactional job, graphile-worker, postgresql, triggers, listen-notify, job-queue, postgraphile, high-performance, supabase" mentioned.

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。

Technical documentation specialist for API docs, tutorials, architecture docs, and developer experienceUse when "documentation, docs, readme, tutorial, api docs, guide, changelog, comments, openapi, documentation, api-docs, tutorials, readme, openapi, swagger, developer-experience, technical-writing, ml-memory" mentioned.

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。

Comprehensive patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.

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。

Event sourcing and CQRS expert for AI memory systemsUse when "event sourcing, event store, cqrs, nats jetstream, kafka events, event projection, replay events, event schema, event-sourcing, cqrs, nats, kafka, projections, event-driven, memory-architecture, ml-memory" mentioned.

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。

Patterns for computer-aided drug discovery including virtual screening, molecular docking, ADMET prediction, lead optimization, and integration with AI/ML methods. Covers both structure-based and ligand-based approaches. Use when ", " mentioned.

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 knowledge for Drizzle ORM - the lightweight, type-safe SQL ORM for edge and serverlessUse when "drizzle, drizzle orm, drizzle-kit, drizzle schema, drizzle migration, drizzle relations, sql orm typescript, edge database, d1 database, orm, database, typescript, sql, edge, serverless, d1, postgres, mysql, sqlite" mentioned.

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 training models across multiple GPUs or nodes, handling large models that don't fit in memory, or optimizing training throughput - covers DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointingUse when ", " mentioned.

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。

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rotUse when "context window, token limit, context management, context engineering, long context, context overflow, llm, context, tokens, memory, summarization, optimization" mentioned.

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。

Infrastructure and practices for reproducible computational research. Covers environment management, data versioning, code documentation, and sharing protocols that enable others to reproduce your results. Use when ", " mentioned.

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 implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and complianceUse when ", " mentioned.

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 community tool selection and implementation - Discord bots, community platforms, analytics tools, CRMs, moderation systems, and automation. Knows the tool landscape, integration patterns, and how to build a cohesive community tech stack. Use when "community tools, discord bots, community platform, community crm, moderation bots, community automation, orbit, common room, commsor, " mentioned.

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。