「agent workflow」の検索結果

Adversarial deliberation process for making decisions. Spawns advocate agents for each option who argue their cases before a judge (you) who renders a verdict.

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。

Reporting workflow for a working journalist, using web research and Apify's X scraper (social signals). Onboards the journalist, monitors their beat and builds digests, evaluates story pitches, finds and vets sources, and prepares interviews. Use for any newsroom task, such as "what's new on my beat", "is this pitch any good", "who should I talk to about X", "prep me for this interview", or "turn these notes into a draft".

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 yt-dlp for YouTube (and supported sites) metadata, subtitles, and search inside Decisions workflows — not for Bilibili (use bili-cli via agent-reach).

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。

Turn your AI agent into a business automation architect. Design, document, implement, and monitor automated workflows across sales, ops, finance, HR, and support — no n8n or Zapier required.

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。

Enhanced terminal AI agent orchestrator with parallel execution, health checks, and workflow presets.

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。

Friction-reduction patterns for agents helping humans with disabilities. Voice-first workflows, smart home templates, efficiency 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。

Securely inject secrets from 1Password into agent workflows. Uses service accounts with op run/.env.tpl as the primary pattern, op read as fallback. Includes hardened security rules, input validation, and troubleshooting for auth/permission failures. Use when accessing API keys, credentials, or any 1Password secret.

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。

Triage GitHub issues through a label-based state machine. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue 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。

This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices.

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 deploy copilot-style assistants on Azure with secure model access, tool integration, and observability. Use this skill when users ask to build assistants, agents, or copilot workflows in Azure. Covers orchestration, retrieval, safety guardrails, and production deployment.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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。

方案到交付 Workflow：当用户显式调用 `$solution-to-delivery`，或明确要求运行“方案到交付”完整流程时使用。 把已确认产品方案转成经过独立 Review 的 PRD、适用的 UI/HTML/截图、版本拆分和 Product Delivery Package；当前 Agent Runtime 只做 Package 发布 dry-run，真实写入保持 authorization_required。

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Evaluate an incoming workflow upgrade — a skill, MCP server, subagent, library, external service, prompt, or pattern — through one disciplined pipeline before adopting it. Security is a blocking gate. Records every verdict so the same thing is never evaluated twice. Use when considering a new tool from a post, repo, or recommendation. Triggers — "vet this", "should we adopt", "is this safe to install", "evaluate this skill/MCP", "what do you think of this tool".

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。

Capture durable project or workflow knowledge discovered during work. Use when a mistake, hidden constraint, repeated question, or changed behavior reveals a documentation gap. To root-cause one specific mistake the agent just made and let the user pick fixes, use `learn` (`/learn`) instead.

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。

Find Linear issues ready for an AI agent to pick up. Equivalent of `bd ready` for Linear workflows. Use when starting a session to find claimable work, or when asked "what's next" or "find 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。

Handle MindIE-SD documentation, governance, contributor workflow, commit or PR conventions, history cleanup, template changes, and version-policy updates with minimal context, fixed review steps, and deterministic output. Use this skill whenever the task touches docs/ 或文档站点内容与配置（Sphinx / Read the Docs / conf.py / index.md / 目录导航 / 首页）、中英文双语配套、治理文档、贡献流程说明、PR/MR 模板或标题正文规范、提交历史整理、版本策略与发布规则。即使用户只说「改个文档」「双语要同步」「commit message 怎么写」「PR 模板加一项」「把提交压一下」「rebase 冲突怎么理」而未提治理规范，也应触发。不负责通用工程实现与仓库默认工作流定义（后者以 AGENTS.md 为准）；Python 格式见 code-standards，Markdown 格式见 markdown-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。

Coordinates QA workflow across planning, writing, and execution agents. Use when user requests testing, quality assurance, or test documentation.

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。

Turn friction you just hit into a concrete change to the workflow, so the same problem doesn't recur.

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。

Bridge Codex to web-based AI model products by packaging local task context, scrub-checking it, sending it through an approved browser surface to ChatGPT Pro, Claude, Grok, Gemini, or another web model, waiting for the answer, and returning the model response to the user or Codex. Also guide DevSpace-like MCP Connector Mode when the user wants ChatGPT Pro or another MCP-capable web host to access approved local workspaces without relying on Codex browser automation. Use when the user asks for GPT Pro, ChatGPT Pro, Claude web, Grok, Gemini web, web model bridge, external model consult, ask another model, second opinion, use a web AI model, browserless agent access to GPT Pro, MCP connector, DevSpace-like workflow, or send local repo context to a browser-based model for planning, review, debugging, architecture discussion, or implementation guidance.

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。

The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically expands nodes when complexity exceeds executor capability. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.

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Model organizations as BDI systems with filtered perception, discourse-shaped desires, accommodations, and abstraction-level hierarchy. Use for socio-technical coordination and organizational modeling. NOT for mechanistic workflows, individual psychology, or non-social domains.

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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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Workflow Phase 6 — TDD coordinator. Decides the scenario recipe and the implementation contract in main context, writes them to a state file, seeds per-worker tasks (scenario, implement, verify-tick, design-ui-tick) into the TaskList, and yields with harness_state continue so the harness invokes each worker as its own tick. No subagent delegation; no nested Skill calls.

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。