「agent workflow」の検索結果

Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

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 and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

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 workflow automation with n8n, Zapier/Make, Temporal, Trigger.dev, Inngest. Use when choosing platforms, durable execution, or approval gates; not job queues or 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。

Designs team Git workflows for branching, PRs, and releases. Use when choosing branching models, stacked PRs, merge queues, worktree isolation for agents, or collaboration rules.

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。

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

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。

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository 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。

Discovers and invokes agent skills. Use when starting a session, or when you need to decide which skill or workflow applies to the piece of work at hand. This is the meta-skill that governs how all other skills are discovered and invoked.

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 high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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 guide for building robust, secure, and efficient CI/CD pipelines using GitHub Actions. Use when the task involves `creating or editing GitHub Actions workflows`, `.github/workflows/*.yml`, `CI/CD pipelines`, `GitHub Actions best practices`, or `workflow 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。

Use when implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

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。

Integrates patterns from external sources (URLs or local files) into local skills, agents, and plugins. Use when comparing external agent definitions against local equivalents, extracting best practices from frameworks like GSD or BMAD-METHOD, enhancing local skills with external workflow patterns, or adding interoperability with external tool ecosystems. Runs a 3-phase workflow — parallel candidate mapping, contextual enhancement, and validation — with source attribution and backlog tracking for deferred enhancements.

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, improve, or audit Vapi voice agent and Squad system prompts for production phone and web based voice agents. Use when the user wants help designing a Vapi assistant prompt, multi-assistant Squad prompt set, refining an existing prompt, creating prompt sections, building an intake or handoff workflow, improving tool-use instructions, adding guardrails, or optimizing voice-agent behavior for brevity, turn-taking, error handling, caller data collection, escalation, handoffs, and spoken formatting.

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。

Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.

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 the "how-to" for workflow / task execution orchestration. Defines methods for planning multi-phase task/workflows, implementing them through agent delegation, and managing artifacts.

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。

Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent 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。

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。

Coordinate multiple AI agents and skills for complex 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。

Ready-to-use prompt templates for specialized agents. Use when building n8n workflows, AI integrations, or sales materials. Contains structured prompts for automation-architect, llm-engineer, and sales-automator 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。

Agent assignment matrix, blocker escalation, and TDM coordination patterns. Use when assigning work to specialists, managing blockers, 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。

Create custom Claude Code sub-agents with specialized expertise and tool access. Use when you need to build reusable agents for specific tasks like code review, debugging, data analysis, or domain-specific 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。

Build a plug-in-ready SDR operator package for sales development and outbound pipeline work: ICP definition, compliant lead-source plan, enrichment/research workflow, account scoring, outbound assets, CRM handoff, reply triage, meeting prep, approval gates, and loop memory. Use when "SDR", "sales development", "outbound SDR", "operator room", "lead gen pipeline", "prospecting pipeline", "ICP targeting", "Apify", "RapidAPI", "Clay", "Apollo", "agentic SDR workflow", "sales operating loop", "sales dashboard handoff", or any request to package outbound sales workflows that can later connect to data tools, CRMs, or operator surfaces.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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 Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production code.

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。