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概要と使いどころ
Configures autonomous heartbeat and cron-based proactive routines (e.g., morning briefing setup, unanswered-message scan cadence). Invoke when user is SETTING UP or MODIFYING a proactive routine, not when the routine itself fires. Cron crons execute their own logic — they do NOT load this skill. Triggers: "set up proactive check", "configure heartbeat", "add morning briefing", "tune cron cadence".
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。
Diagnose and fix WhatsApp connectivity issues for OpenClaw agents. Use when: a PA is not responding, WhatsApp shows connected but messages don't arrive, the agent is online but not replying, or troubleshooting a new agent setup.
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。
Step-by-step onboarding guide for setting up a new AI Personal Assistant on OpenClaw. Use when: a new PA is being created, someone asks how to set up an agent, or guiding a user through the full setup process from account creation to first response.
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 working with Auth0 — auth0 identity platform management for tenant configuration, application setup, connection analysis, rule and action review, user management, and log inspection. Covers universal login, social connections, enterprise federation, and Auth0 Actions pipelines. Read this skill before any Auth0 operations — it enforces discovery-first patterns and strict read-only safety 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。
Use when working with AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) APIs.
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 working with Aws Cognito — aWS Cognito user pool analysis, identity pool management, authentication flow analysis, and MFA status tracking. Covers user statistics, app client configuration, password policy review, Lambda trigger inspection, and federation setup.
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。
Import an OpenClaw setup (memories, skills) into OpenAmer.
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。
GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
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。
API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
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。
Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.
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。
Integrate Stitch-derived UI direction into shadcn/ui React projects with registry-aware setup, ownership rules, theming, and validation.
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 OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.
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。
Translate Figma designs into production-ready application code with 1:1 visual fidelity. Use for Figma URLs, node IDs, implementation requests, or components that must match Figma specs; use the local `figma` skill for general Figma MCP context and setup.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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 the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.
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。
Digital product builder — Ebooks, templates, presets, Notion templates, Gumroad setup
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。
Course creator — Curriculum design, video production, LMS setup, pricing, launch strategy
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。
Activate this skill when BenchmarkDotNet (BDN) is involved in the task — creating, running, configuring, or reviewing BDN benchmarks. Also activate when microbenchmarking .NET code would be useful and BenchmarkDotNet is the likely tool. Consider activating when answering a .NET performance question requires measurement and BenchmarkDotNet may be needed. Covers microbenchmark design, BDN configuration and project setup, how to run BDN microbenchmarks efficiently and effectively, and using BDN for side-by-side performance comparisons. Do NOT use for profiling/tracing .NET code (dotnet-trace, PerfView), production telemetry, or load/stress testing (Crank, k6).
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 devcontainers with Claude Code, language-specific tooling (Python/Node/Rust/Go), and persistent volumes. Use when adding devcontainer support to a project, setting up isolated development environments, or configuring sandboxed Claude Code workspaces.
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。
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval 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。
Generate Python SDK setup + CRUD + bulk + paging snippets using official 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。
Audit and update an arbitrary codebase to follow the Code.mil federal open-source licensing model (MIT with 17 U.S.C. Section 105 disclaimers and INTENT.md), including CRAN/R package DESCRIPTION setup.
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。
GitHub Actions CI/CD setup for Python, R, and multi-platform testing. Use when creating, debugging, or optimizing 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。
Bring a new machine to a verified AOS installation: read-only doctor baseline, install after GO, MCP servers wired per harness with the configs backed up first, memB and OpenWiki checks, and a second doctor run for the delta. Use for "set up this machine", "onboard a new computer", "install AOS on this laptop".
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。