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Jev-driven browser automation: Jev picks operations, programs execute, a text model writes field values only when Jev cannot pick one from the goal.

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。

Kubernetes operations: debugging, security, RBAC, and infrastructure tooling.

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。

Apple's open-source `container` CLI to build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS — no Docker daemon required. Use when the user mentions the `container` CLI, "apple container", running or building containers on macOS without Docker/Podman, `container run`, `container build`, `container images`, `container system start`, pushing/pulling images to a registry, or container networking on macOS. This is Apple's `container` tool specifically (repo `apple/container`), NOT Docker, Podman, containerd, nerdctl, or Kubernetes — the CLI is Docker-like but is a different 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。

Convert documents and text to audio using Google Cloud Text-to-Speech. Use this skill when the user wants to: narrate a document, read aloud text, generate audio from a file, convert text to speech, create a recording of documentation or analysis, create a podcast from a document, or use Google TTS/text-to-speech. Trigger phrases: "read this aloud", "narrate this", "create a recording", "text to speech", "TTS", "convert to audio", "audio from document", "listen to this", "generate audio", "google tts", "create a podcast".

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。

Send and receive WhatsApp messages via the unofficial linked-device client pywhats (pip install pywhats) — pair with QR, send text/images, group chat, read receipts, presence/typing, and a long-running JSON event stream. Use when the user wants to script WhatsApp as a linked companion device (like WhatsApp Web), pair a device, send a WhatsApp from Python, or listen for inbound messages. Triggers: 'whatsapp', 'send whatsapp', 'pair whatsapp', 'pywhats', 'linked device', 'whatsapp web client'. NOT the official WhatsApp Business/Cloud API.

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。

Search, read, and manage Outline wiki documents. Use when: (1) searching wiki for documentation, (2) reading wiki pages or articles, (3) listing wiki collections or documents, (4) creating or updating wiki content, (5) exporting documents as markdown. Works with any Outline wiki instance (self-hosted or cloud).

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 Azure DevOps projects, work items, repos, PRs, pipelines, wikis, test plans, security alerts, variable groups, environments/approvals, branch policies, and attachments. Use when user asks to: manage sprints, create/update work items, list repos, create PRs, run pipelines, search code, manage wiki pages, check security alerts, manage variable groups, approve deployments, or configure branch policies. Covers 13 domains with 99 tools via REST API.

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 Jira issues and Confluence wiki pages in Atlassian Cloud. Use when: (1) searching/creating/updating Jira issues with JQL, (2) searching/reading/creating Confluence pages with CQL, (3) managing Jira workflows, transitions, and comments, (4) browsing Confluence spaces and page hierarchies. Supports OAuth 2.1 via MCP server (recommended) or API token authentication (fallback).

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 Cloud integration for issue management and search. This skill should be used when working with Jira tickets, searching issues with JQL, creating or updating issues, adding comments, or transitioning issue status. Covers REST API v3 and Jira Query Language.

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 infrastructure design and deployment patterns for AWS, Azure, and GCP. Use when designing cloud architectures, implementing IaC with Terraform, optimizing costs, or setting up multi-region deployments.

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 RxJS patterns for reactive programming in Angular. Use this skill when working with Observables, operators, subscriptions, async data flows, and error handling. Covers common patterns like combineLatest, switchMap, debounceTime, catchError, retry logic, and integration with Angular Signals using toSignal() and toObservable(). Ensures proper subscription cleanup with takeUntilDestroyed().

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。

When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role.

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。

Integration skill for Lovable.dev projects. Activates when working with: - Lovable.dev projects with GitHub sync - Supabase Edge Functions that need deployment - Database migrations for Lovable Cloud - Projects with supabase/ directory structure - Any mention of "Lovable", "deploy edge function", "apply migration" Provides exact Lovable prompts for backend operations that can't be done via GitHub alone.

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 checking deployment health, investigating errors, reading logs, or working with Tiltfiles. Queries Tilt resource status, logs, and manages dev environments.

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。

Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.

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。

Docker, Kubernetes, and AWS ECS/Fargate patterns. Triggers on: Dockerfile, docker-compose, kubernetes, k8s, helm, pod, deployment, service, ingress, container, image, ecs, fargate, task definition, ecs service, awsvpc, FARGATE_SPOT, ALB, ecs vs kubernetes.

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, manage, or connect to a headless Windows 11 VM running in Docker with SSH access. Use when the user wants to spin up, stop, restart, or SSH into a Windows VM.

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。

SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.

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。

Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).

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。

Project-kickoff rubric for the self-host vs managed-cloud decision — when is running your own inference engine / LLM platform worth the ops cost vs paying per-token for a managed API? Decide on two axes — VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). Use at kickoff when choosing where to run inference, or when cost / data-residency pressure forces a re-evaluation.

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。

Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.

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。

Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.

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。

Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).

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 creating new skills, editing existing skills, or verifying skills work before 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。