5,816 件 ・ 関連度順
概要と使いどころ
Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.
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 clean git branches from feature work, excluding agent-ops files. Use for PR preparation.
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。
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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。
End-to-end resume parsing (detect format → extract fields). Uses a combination of format detection, text extraction, and LLM parsing to normalize resume data.
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。
Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous 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。
Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.
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 the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent 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。
Agent skill for release-swarm - invoke with $agent-release-swarm
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 research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Masters research methodologies, data analysis, and report creation with focus on delivering actionable intelligence that drives informed decision-making.
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。
Pre-commit and pre-merge validation checks. Use before committing changes or declaring work complete to ensure all quality gates pass.
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, refine, and manage issues. Use for creating new issues from loose ideas, refining ambiguous issues, bulk operations, or JSON export.
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。
Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.
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。
Track and report agent invocation metrics including usage counts, success/failure rates, and completion times. Use for understanding which agents are utilized, identifying underused agents, and optimizing agent delegation patterns.
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 .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.
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。
Run prompt with OpenAI or Gemini provider via agent 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。
Create well-structured prompts for AI agents using proven architecture patterns. Use when users ask to write agent prompts, system prompts, or agent instructions, or want to improve existing prompts that aren't working.
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。
Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection
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 payment integration specialist mastering payment gateway integration, PCI compliance, and financial transaction processing. Specializes in secure payment flows, multi-currency support, and fraud prevention with focus on reliability, compliance, and seamless user experience.
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。
Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.
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 new AgentOps skills via interactive interview. Supports from-scratch and clone modes with tiered complexity.
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 AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration. Use when: the task directly matches context manager responsibilities within plugin agent-orchestration. Do not use when: a more specific framework or task-focused skill is clearly a better match.
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 designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous 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。
Handle failures and errors during workflow. Use when build breaks, tests fail unexpectedly, or agent gets stuck. Semi-automatic recovery with user confirmation for destructive actions.
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。
Get external agent review and feedback. Routes Anthropic models through Claude Agent SDK (uses local subscription) and other models through OpenRouter API. Use for code review, architecture feedback, or any external consultation.
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。