「workflows」の検索結果

Run an effective bug bounty program — scope design, triage, researcher relations, and remediation 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。

Master Alfred on macOS: search, workflows, clipboard history, snippets, and the Powerpack. Use when building a keyboard-driven command layer on the Mac with Alfred.

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 agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.

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。

Argo CD guidance — GitOps workflows, Application manifests, sync strategies, AppProjects, and multi-cluster ops.

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 teams of role-based agents — role definitions, task delegation, sequential and hierarchical workflows, and shared memory. Use when a job naturally splits into specialist roles collaborating.

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。

Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, DMARCbis, RFC 9989, DMARC pct tag, DMARC t=y test mode, DMARC rollout, move DMARC to reject, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.

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。

Extract Lanhu design HTML, CSS, and assets through Lanhu MCP for Claude Code design-to-code workflows. Called by design-to-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。

Unity data-driven design architecture. ScriptableObject config hierarchies, JSON data pipelines, designer handoff workflows, data versioning and migration, Inspector attributes for self-documenting configs. DECISION format: WHEN/DECISION/SCAFFOLD/GOTCHA. Based on Unity 6.3 LTS.

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 generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.

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 new observable frontend behavior or fixing a behavior-changing bug where a failing test can describe the missing or incorrect behavior first. Apply to components, hooks/composables, utilities, API clients, route guards, or user workflows; pure behavior-preserving refactoring uses an already-green contract 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。

Use when absorbing ideas, capabilities, workflows, architecture, quality systems, ecosystem extensions, or engineering practices from any reference system into the current project through original, project-native redesign rather than copying.

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。

Set up LLM-as-judge evaluation for ORC 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 publication-quality, editable scientific and engineering schematics with explicit physical meaning, exact labels, visual hierarchy, and provenance. Use for experimental facilities, thermal-fluid mechanisms, workflows, graphical abstracts, system architectures, or research diagrams.

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 analyze engineering experiments or simulations using baseline cases, hypothesis-driven DOE, traceable data processing, defensible plots, uncertainty, and validation. Use for CFD, laboratory data, ML workflows, parameter studies, or research 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。

Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance 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。

Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.

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。

Browser automation via Playwright CLI for navigating pages, interacting with elements, capturing screenshots, and testing web applications through shell commands. Use when user mentions "playwright", "browser automation", "take a screenshot", "browser testing", "headless browser", "web testing", "fill out a form", "e2e test", or needs to automate browser workflows from the command line. This is a pre-installed CLI tool — do NOT install anything via npx or npm. Invoke this skill first, then use playwright-cli bash commands.

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 this when the user wants to generate, normalize, verify, deduplicate, or export training datasets for Codex, Antigravity, or Claude Code from topics, URLs, reference material, web research, or existing JSONL/CSV files. Supports SFT and DPO workflows, custom export schemas, and deterministic local pipeline 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。

Help users draft and, when authorized, submit feedback to volcengine/volcengine-skills: skill problems, missing capabilities, cloud scenarios, and successful Agent workflows. Use for explicit feedback or sharing requests, or an optional invitation at a natural summary after Volcengine CLI errors or an incompletely solved task. Feedback never blocks the main 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。

Add SLSA build-provenance attestations to existing GitHub Actions workflows. Use when the user wants to add artifact attestations, build provenance, or SLSA attestations to Docker container image builds in GitHub Actions CI/CD pipelines.

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。

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends your capabilities with specialized knowledge, workflows, or tool integrations.

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。

Guidance for creating Tauri v2 projects using official create-tauri-app workflows and minimal run 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。

Best practices for writing reliable Pulumi programs. Covers Output handling, resource dependencies, component structure, secrets management, safe refactoring with aliases, and deployment 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。

Manages cloud infrastructure through natural language conversations with Pulumi Neo, an AI agent for platform engineers. Enables infrastructure analysis, resource provisioning, stack deployment, and configuration management via conversational AI. Use when creating Neo tasks, requesting infrastructure analysis, automating cloud deployments, managing infrastructure as code (IaC), provisioning AWS/Azure/GCP resources, managing infrastructure through natural language prompts, reviewing PRs with Neo, handling Neo approval workflows, or checking Neo task status and events. Also use when the user mentions "Pulumi Neo", "Neo task", "Neo agent", or wants AI-assisted 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。