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概要と使いどころ
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
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。
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
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 asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.
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。
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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。
Structured cross-review protocol between specialized agents. Ensures scope alignment, priority calibration, and domain-aware feedback. Use when one agent reviews another's work, during handoffs, or when validating cross-cutting concerns.
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Create, update, review, fix, or debug VS Code agent customization files (.instructions.md, .prompt.md, .agent.md, SKILL.md, copilot-instructions.md, AGENTS.md). Use for: saving coding preferences; troubleshooting why instructions/skills/agents are ignored or not invoked; configuring applyTo patterns; defining tool restrictions; creating custom agent modes or specialized workflows; packaging domain knowledge; fixing YAML frontmatter syntax.
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 mapping audit targets to appropriate config or domain agents. Analyzes file types, patterns, and content to determine which agents should audit each file/target. Returns agents[] needed for audit workflow.
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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。
Interactive element picker for web pages. Opens a browser with click-to-select UI overlay. Use when you need to let users visually select DOM elements, identify element selectors, or get detailed element information interactively. Triggers on "select an element", "pick element", "let me choose", "which element", or any interactive element selection task. Integrates with agent-eyes for visual context.
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 AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
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。
Deep code review after beads have landed — random exploration + cross-agent review, alternating. Use every 30–60 min during active implementation, or after any epic lands.
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Deep review — random exploration + cross-agent review, alternating. Finds integration bugs that self-review misses. Run every 30–60 min during active implementation.
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Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a
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Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.
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 and audit PFC2D/PFC3D 5.0 model foundations for asphalt cases, including lifecycle, domain, balls, walls, clumps, groups, ranges, top-level cmat, staged saves, and source-version purity.
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Create, read, edit, and open draw.io (.drawio) files. Supports architecture diagrams, ER diagrams, flowcharts, domain model diagrams, and more. Use when requests include "create a diagram", "make a flowchart", "draw an architecture diagram", "create an ER diagram", "open a .drawio file", "what's in this diagram?", etc. Do NOT use for Mermaid or PlantUML generation.
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"Apply Bhaskar's critical realism to analyze phenomena through three ontological domains (real, actual, empirical), identify generative causal mechanisms via retroduction, and examine structure-agency interplay. Use this skill when the user needs to go beyond surface correlations to underlying causes, design research that distinguishes mechanisms from events from experiences, or when they ask 'what causes this beyond the observed pattern', 'what structures enable or constrain this behavior', or 'how do I move from correlation to causal explanation'.".
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"Apply meta-analysis to synthesize effect sizes across multiple studies, assess heterogeneity, and evaluate publication bias. Use this skill when the user needs to combine findings from prior research, compare fixed-effect vs random-effects models, compute pooled effect sizes, or when they ask 'what does the overall evidence say', 'how do I combine results across studies', or 'is there publication bias'.".
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"Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.".
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"Apply Porter's Diamond Model to analyze national competitive advantage for a specific industry. Use this skill when the user needs to evaluate why certain nations dominate particular industries, assess a country's attractiveness for industry investment, or diagnose gaps in national competitiveness using the four determinants plus government and chance.".
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"Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.".
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"Apply organizational ecology (Hannan and Freeman) to analyze population-level dynamics of organizational founding, failure, and selection. Use this skill when the user needs to explain industry-level patterns of birth and death rates, analyze structural inertia and liabilities of newness or aging, evaluate why adaptation is rare relative to selection, or when they ask 'why do most startups fail', 'why is this industry dominated by old firms', or 'why do organizations resist change despite environmental pressure'.".
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。
Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing. Use when building motion-rich frontend pages with stricter GPT/Codex design enforcement.
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。