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概要と使いどころ
Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.
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。
Automate Accelo tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
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。
Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Automate Abuselpdb tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Automate Abyssale tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Automate Ably tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Index-based file discovery using ABOUTME headers. Use INSTEAD of grep or Explore agent when searching for files by purpose or feature. Faster and more accurate than scanning code. Invoke this skill when user asks "which files handle X", "where is Y implemented", or when you need to find files related to a feature or task.
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 and manipulate Abaqus geometry - parts, sketches, extrusions, CAD import. Use for any geometry creation task including box, cylinder, or STEP/IGES import.
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。
Implement Agent-to-Agent (A2A) protocol for inter-agent communication. Use for agent discovery, agent cards, task delegation, and multi-agent orchestration. Triggers on "A2A protocol", "agent-to-agent", "agent discovery", "agent card", "multi-agent", "agent delegation", "agent communication", or when implementing spec/api/017-a2a-protocol.md.
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 skill for Abaqus FEA scripting. Use for any finite element analysis, topology optimization, or Abaqus Python scripting task. Routes to appropriate specialized skills.
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。
[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.
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。
Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.
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。
[71] CLOSE. Quick, safe cleanup after completing a milestone. Fix objective issues only (syntax errors, dead code, poor naming). Must be <5% of main task time, <30 seconds per fix, and reversible. Use after key points, not after every small change.
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。
[20] UNDERSTAND. Achieve complete clarity on goals, expectations, and success criteria. Use when starting new tasks, when requirements seem unclear, when scope is ambiguous, or anytime you need full understanding of what success looks like. Triggers on "clarify goal", "understand requirements", "what exactly needed", "define success", or when facing unclear expectations.
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。
[21] UNDERSTAND. Deep research workflow for this project using 150% scope (100% core + 50% boundary), evidence-based reasoning, and structured investigation notes. Use when the task requires investigation, root-cause analysis, or mapping unknown areas. Always maintain a research log file that captures findings, hypotheses, and next branches; use web.run when external verification is needed.
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。
[02] META. Create new skills when existing ones don't cover the task. Analyze unique requirements, build framework (Frame → Research → Plan → Execute), integrate risks, and declare new skill. Use when facing novel problems that existing skills can't address.
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。
Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Code quality standards for OneKey. Use when fixing lint warnings, running pre-commit tasks, handling unused variables, writing comments, or ensuring code quality. All comments must be in English. Triggers on lint, linting, eslint, oxlint, tsc, type check, unused variable, comment, documentation, spellcheck, code quality, pre-commit, yarn lint.
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。
[01] META. Сканирует доступные skills, создает план выполнения и идет шаг за шагом с подтверждением каждого этапа. Triggers on complex tasks, multi-step work, or when structured execution is needed.
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。
Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Plan, validate, and use 1Password CLI setup for secret injection and auth. Use when tasks need 1Password CLI usage, secret references, op run/read/inject, or provisioning secrets via env vars/.env files and scripts.
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。
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
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。
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
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。