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概要と使いどころ
Use when designing or building any terminal user interface — choosing a layout paradigm, keybindings/interaction model, color system, data visualization, or motion. Framework-agnostic universal patterns that work with Ratatui, Ink, Textual, Bubbletea, or any TUI toolkit. For the repo's own pythinker-code TUI, use write-tui instead.
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 frontend Solana applications with Phantom Connect SDK and Helius infrastructure. Covers React, React Native, and browser SDK integration, transaction signing via Helius Sender, API key proxying, token gating, NFT minting, crypto payments, real-time updates, and secure frontend architecture.
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 frontend Solana applications with Phantom Connect SDK and Helius infrastructure. Covers React, React Native, and browser SDK integration, transaction signing via Helius Sender, API key proxying, token gating, NFT minting, crypto payments, real-time updates, and secure frontend architecture.
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。
You MUST use this before any creative work — creating features, building components, drafting documents, designing workflows, or modifying behavior. Explores your human partner's intent, requirements, and design before any implementation begins.
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 handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.
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 the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` skill instead.
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 event-driven workflows, decoupling services, implementing fan-out patterns (one trigger, many downstream handlers), implementing idempotent event handling with IDs (24-hour dedupe window), or handling at-least-once delivery from external sources like Stripe webhooks. Covers Inngest event schema, payload format, naming conventions, IDs for idempotency, the ts param, fan-out patterns, and system events like inngest/function.failed.
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 assigning a confidence level to an analytical judgment, the user asks "how confident are we?" / "what is the confidence on X?", or the orchestrator's tradecraft pipeline calls for a confidence level before publishing. Provides three named levels (High, Moderate, Low) with 0-100 score ranges aligned to STIX, and the rule that confidence cannot exceed what the weakest load-bearing claim supports.
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 human approval gates for high-stakes agent actions. Keywords: human-in-the-loop, HITL, approval gate, checkpoint, confirmation, risk-tiered, action review.
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, optimizing, testing, or deploying robust prompt systems for AI agents. This skill provides frameworks for structured prompt engineering, meta-prompting, and automated optimization 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。
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
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。
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
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 context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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, implementing, or debugging complex agentic workflows using state machine patterns. Keywords: state machine, durable execution, agent orchestration, graph-based agents, workflow checkpoints, C4 protocol.
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。
Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
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 reliable, safe, and trustworthy agent systems that fail gracefully and operate within bounds. Use when building guardrails, handling edge cases, preventing harmful outputs, monitoring for failures, implementing safety constraints, or designing error recovery. Covers failure modes, constraint systems, oversight mechanisms, and safety validation.
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 effective reasoning processes for AI agents and optimize decision-making strategies. Use when designing agent prompts, improving decision quality, implementing specialized reasoning modes, creating reasoning traces, or debugging agent mistakes. Covers prompting, chain-of-thought, reasoning depth, and decision evaluation.
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 prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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 applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
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 building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.
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。
Best practices for designing Claude Code agent files (.claude/agents/*.md). This skill should be used when writing or reviewing agent markdown files to ensure proper design with focused domains, correct tool access, reusable definitions, and separation of capabilities from lifecycle. Combines Anthropic's official guidance with battle-tested patterns from agent team usage.
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 effective AI agent systems with clear architecture, capability planning, and tool integration. Use when building agent systems, designing agent workflows, planning tool ecosystems, defining agent roles and boundaries, or architecting multi-agent systems. Covers agent patterns, agentic loops, capability design, and system composition.
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 custom Claude Code agents with effective descriptions, tool access patterns, and self-documenting prompts. Covers Task tool delegation, model selection, memory limits, and declarative instruction design. Use when: creating custom agents, designing agent descriptions for auto-delegation, troubleshooting agent memory issues, or building agent pipelines.
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 an autonomous agent, planning agent architecture, building a scheduled automation, or creating a Claude Code agent workflow. Triggers: 'design an agent', 'build an automation', 'agent architecture', 'automate this workflow', 'create a scheduled agent', 'shell script agent'.
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。