「tasks」の検索結果

Routes Confluence Cloud tasks to the right mk:confluence-* leaf skill. Use for 'confluence', 'wiki page', 'spec page', or ambiguous intent. NOT an executor -- forks to a leaf skill.

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 UI components, reviewing visual design, building design systems, or checking accessibility. Auto-activates on frontend design tasks and UI reviews.

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。

Task claiming and ownership tracking for parallel agent execution. Agents claim tasks from a shared queue with file ownership enforcement. Use during parallel execution phases when multiple agents work simultaneously.

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。

Auto-invoked 7-phase workflow for complex-feature intent. Includes fasttrack mode for pre-approved specs. NOT for explicit user-invoked single tasks (see mk:cook); NOT for green-field autonomous builds (see mk:autobuild); NOT for simple bug fixes (see mk:fix).

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。

Runs autonomous multi-hour green-field builds: planner, contract, generator/evaluator loop, scaffolding density per model tier. NOT for scoped tasks (mk:cook); NOT for scaffolding only (mk:bootstrap).

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。

Generate publish-grade SVG+PNG technical diagrams — architecture, data flow, flowchart, sequence, agent-memory, class, ER, state-machine, comparison, timeline, and more — across 8 visual styles. Writes output to tasks/visuals/ or an active plan's visuals/ directory. Requires rsvg-convert (librsvg) for PNG export; degrades gracefully to SVG-only when absent.

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。

JIRA agile via the jira-as wrapper: epics, sprints, backlog, ranking, story points, subtasks, velocity. Triggers: 'create sprint', 'add KEY to sprint', 'set story points on KEY', 'rank KEY before KEY', 'velocity for board', 'epic add'. Board ID ≠ project key. NOT for issue CRUD (mk:jira-issue); NOT for time tracking (mk:jira-time).

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 UI components, reviewing visual design, building design systems, or checking accessibility. Auto-activates on frontend design tasks and UI reviews.

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 safely evolve data models, database schemas, migrations, queries, indexes, and ORM data-access boundaries. Use for data-model, migration, query, index, ORM schema, or datastore-selection tasks. Discover the existing engine and migration source before proposing 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。

Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

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。

Routes agent management tasks to the correct manager agent. Use when user requests agent creation, updates, audits, git operations, or verification.

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。

Routes development tasks to the correct language or framework expert agent. Use when user requests code review, implementation, refactoring, or debugging.

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。

Routes data engineering tasks to the correct DE expert agent. Use when user requests data pipeline design, DAG authoring, SQL modeling, stream processing, or warehouse optimization.

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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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。

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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。

Openapi utility services - real-time currency exchange rates, HTML-to-PDF conversion (renders JavaScript), .it domain registration/management, and managed RAG (retrieval-augmented generation over your documents). Use for these one-off utility tasks.

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。

Italian electronic invoicing and payments through Openapi - send/receive electronic invoices via SDI (Sistema di Interscambio), digital receipts (scontrino elettronico), legal storage, and bill payments (MAV, RAV, PagoPA, car tax). Use for fatturazione elettronica and bill payment tasks.

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 Mastra framework guide for building agents, workflows, tools, memory, workspaces, and storage with current APIs. Use for documentation lookup, API verification, TypeScript setup, common errors, migrations, and `mastra api` CLI tasks: inspect or call resources on local, Mastra platform, Trace Intelligence, or remote servers.

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 tasks from an OpenSpec change (Experimental)

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。

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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。

Call this skill when faced with "wicked problems", complex challenges, or when a user asks for strategic innovation, root-cause analysis, or long-term problem solving. Do not use for simple, linear tasks. This skill forces the agent to bypass superficial symptoms, map the entire ecosystem, and anticipate second-order consequences before proposing an intervention.

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 complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.

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 for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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 Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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。