「agent workflow」の検索結果

AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.

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 Arcade MCP to create custom MCP servers and tools with OAuth-aware authorization, evals, and deployment paths for agent tool-calling 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。

Deep exploration and synthesis workflow using agent teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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。

Behavioral rules and lifecycle event handlers for the dev-tools package. Defines automated behaviors that trigger at specific points in the agent workflow. (converted from hooks)

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。

Behavioral rules and lifecycle event handlers for the core-tools package. Defines automated behaviors that trigger at specific points in the agent workflow. (converted from hooks)

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 exploration and synthesis workflow using Agent Teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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。

Creates and registers templates for agents, skills, workflows, hooks, and code patterns. Handles post-creation catalog updates, consuming skill integration, and README registration. Use when creating new template types or standardizing 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。

Expert-level Slack platform development using Bolt SDK — build Slack apps with slash commands, interactive components (buttons, modals, shortcuts), home tabs, event subscriptions, workflows, Socket Mode, and Block Kit. Use for building Slack bots, workflow automation, and rich interactive Slack experiences.

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。

Guided project onboarding for new codebases. Helps agents understand project structure, build systems, test commands, and development workflows by creating persistent knowledge memories.

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。

Creates JSON Schema validation files for skills, agents, hooks, workflows, and data structures. Ensures type safety and input validation across the framework.

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。

QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear

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。

Medusa v2 rules and best practices for modular commerce architecture, Workflow SDK, data models, services, and admin customizations.

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 GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.

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。

Collaborative document creation via a structured three-stage workflow. Use for writing specs, PRDs, design docs, proposals, RFCs, and any long-form document where quality and clarity matter. Brainstorms 5-20 options per section, builds iteratively, and tests with reader sub-agents.

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。

Unified lifecycle management for all framework artifacts (skills, agents, hooks, workflows, templates, 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。

Research UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI 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。

Git-native issue tracking (beads), coding guidelines, knowledge injection, and spec-driven planning for AI agents. Drop-in replacement for bd/Beads with simpler architecture. Use for: tracking issues/beads with dependencies, creating bugs/features/tasks, planning specs, implementing features from specs, code reviews, committing code, creating PRs, loading coding guidelines (TypeScript, Python, TDD, golden testing, Convex, monorepo patterns), code cleanup, research briefs, architecture docs, agent handoffs, and checking out third-party library source code. Invoke when user mentions: tbd, beads, bd, shortcuts, issues, bugs, tasks, features, epics, todo, tracking, specs, planning, implementation, validation, guidelines, templates, commit, PR, pull request, code review, testing, TDD, test-driven, golden testing, snapshot testing, TypeScript, Python, Convex, monorepo, cleanup, dead code, refactor, handoff, research, architecture, labels, search, checkout library, source code review, or any workflow shortcut.

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 Brain /brain-should-this-exist: before planning any new product, feature, workflow, or automation.

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 Brain /brain-learn: after repeated success/failure, a tricky fix, or a shipped workflow.

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。

Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

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。

Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.

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 GitOps continuous delivery using Argo CD or Flux with app-of-apps pattern, automated sync policies, drift detection, and multi-environment promotion. Manage Kubernetes deployments declaratively from Git with automated reconciliation. Use when implementing declarative infrastructure management, migrating from imperative kubectl commands to Git-driven deployments, setting up multi-environment promotion workflows, enforcing code review gates for production, or meeting audit and compliance requirements.

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 electronic signatures compliant with 21 CFR Part 11 Subpart C and EU Annex 11. Covers signature manifestation (signer, date/time, meaning), signature-to-record binding, biometric vs non-biometric controls, policy creation, and user certification requirements. Use when a computerized system requires legally binding electronic signatures for GxP records, when replacing wet-ink signatures in regulated workflows, when implementing batch release or document approval workflows, or when a regulatory gap reveals missing signature controls.

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。

Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team 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。