1,880 件 ・ 関連度順
概要と使いどころ
Apple SwiftUI 宣言的 UI フレームワークリファレンス。 View, VStack, HStack, ZStack, List, NavigationStack, TabView, State, Binding, Bindable, Environment, EnvironmentObject, sheet, fullScreenCover, modifier, animation, withAnimation, Animation, Transition, matchedGeometryEffect, PhaseAnimator, KeyframeAnimator。
Sentry の Issue 一覧 URL（例 `https://<org>.sentry.io/issues/?project=<id>&environment=<env>`）を受け取り、未解決（unresolved）の Issue を列挙して1件ずつサブエージェントで並列調査する。コード上で既に解消済みと確認できたものは Sentry 側を resolved にし、未解消のものは `create-issue` スキルで GitHub Issue を作成する。「Sentryのissueをトリアージして」「Sentryの未解決エラーをチケット化して」といった依頼で使用する。
This skill should be used when the user asks to "最小再現環境を作って", "再現環境を構築して", "問題を切り分けたい", "パッケージの不具合か確認したい", "create a minimal reproduction", "isolate the issue", "build a repro environment", "reproduce this bug", "narrow down the cause", "is this a package bug or my code", "このPRの問題を再現して", "this commit broke something", or wants to determine whether a bug is caused by a package, the environment, or application code. Accepts PR URLs, commit hashes, or branch names as input.
Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo.
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。
Standardizes development environment setup across machines by generating tool version configs (Node, Python, Ruby), package manager configs (pnpm, Volta, asdf, mise), environment variable templates, and setup scripts with onboarding documentation. Use when users need to "setup dev environment", "standardize tooling", "configure version managers", or "create onboarding 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。
リモート環境やDockerで、導入・連携・実操作のテストを進めるスキル。コードの信頼性に応じた環境の選択から、障害調査と実行後の後片付けまで扱います。
コマンド実行やファイル編集、入力・終了時のイベントを条件で検知し、危険な操作や機密ファイルの変更に警告・ブロックを設定するルールを作るスキル。
Set up, review, debug, or validate an Orca per-workspace environment recipe: the on-demand, disposable runtime (cloud sandbox, VM, SSH host, or local container) Orca creates fresh for each workspace. Use to stand up a new recipe end to end, fix an `environmentRecipes` entry in `orca.yaml`, scaffold provider lifecycle scripts, or resolve an `orca vm recipe doctor` failure. Use `orca-cli` for ordinary worktree and workspace creation with no recipe involved.
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。
Discover available task environments and create or continue tasks when the user names an environment or computer, or refers to files or apps specifically on their computer. Excludes software engineering; use orbit:software-engineering for that.
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。
Load when the user asks to add, import, compile, or ingest a research paper/PDF into OpenViking, especially when they mention ov-add-paper, ARA, claims, evidence, figures, tables, or paper-to-OV knowledge resources.
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。
Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.
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。
Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.
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。
Operate and maintain OpenViking server - configure, install, start, stop, and cleanup the server. Use when need to setup or manage OpenViking service 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。
Work with OpenViking, the persistent context database behind this agent's memory. Use it whenever the user refers to earlier sessions or shared history ("like last time", "what did we decide"), asks to remember or forget something, shares files, URLs, or repos worth keeping, or when the task needs context this session does not have — even if nobody says the word "memory". Also use it when the user asks where memories are stored: per project, per folder, or shared between repositories. Covers choosing between context search, find, list search, and grep, reading viking:// URIs, and when (not) to write.
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。
Load when an agent needs to manage, install, update, remove, or validate OpenViking skills via the `ov skills` CLI. Trigger on explicit user requests about skill management, when the user mentions `ov skills`, `install skill`, `update skill`, `delete skill`, `validate skill`, or when an agent needs to discover what skills are available on the OpenViking server.
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。
Prepare a local LangBot development and testing environment for an AI agent. Use when setting up WSL or Linux development, shared local URL variables, proxy variables, backend/frontend startup, Playwright MCP browser access, GitHub OAuth browser login, persisted Chrome profiles, or future Codex computer-use environment paths.
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 secrets extraction methodology targeting CI/CD environments across all major platforms. Covers environment variable extraction from build contexts, exploitation of vault and secrets-manager misconfigurations (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), runner and agent token abuse for lateral movement, OIDC federation attacks exploiting trust relationships between CI/CD providers and cloud platforms, build log leakage analysis for inadvertently exposed credentials, cache poisoning techniques for credential exfiltration, platform-specific credential store exploitation (GitHub Actions secrets, GitLab CI variables, Jenkins credential providers), service connection and service account abuse in Azure DevOps and GCP, and Docker registry credential theft from build environments. Maps to MITRE ATT&CK T1552 (Unsecured Credentials) and its sub-techniques. Each section provides enumeration procedures, extraction techniques, and post-exploitation pivoting guidance for using recovered secrets to expand access.
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 first security skill to install after setting up OpenClaw — powered by Tencent Zhuque Lab. Works like an antivirus for your AI environment: audits installed skills, scans skills before installation, and performs a full OpenClaw security health check to prevent data leaks and privacy risks. Backed by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). Use when the user asks to start a security health check or security scan for the current OpenClaw environment, such as `开始安全体检`, `做一次安全体检`, `开始安全扫描`, `全面安全检查`, or `检查 OpenClaw 安全`; also use when the user asks to audit a specific skill before installation, review installed skills for supply chain risk, or investigate whether a skill is safe. Do not trigger for general OpenClaw usage, project debugging, environment setup, or normal development requests. Optional cloud mode: set AIG_CLOUD_LOOKUP=off for zero outbound HTTPS; when enabled, only skill_name, source label, and OpenClaw version are sent to A.I.G (never skill bodies, chats, or workspace files).
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。
ALWAYS USE when asked to migrate, replace, or make testable existing C# static calls with a named wrapper or built-in abstraction: DateTime.UtcNow/Now or DateTimeOffset.UtcNow to TimeProvider/IClock, File.* to IFileSystem or an existing store such as ITextFileStore, and Environment.* to an existing reader such as IEnvironmentReader. Covers scoped files/projects, constructor injection, replacing temp-file or process-environment tests with fakes, "already registered" abstractions, and static classes whose callers/signatures must stay unchanged. Preserves DateTimeKind and call count. DO NOT USE for finding statics (detect-static-dependencies), choosing/designing a new wrapper (generate-testability-wrappers), behavior tests with no chosen seam (testability-obstacle), or test-framework migration.
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。
Diagnoses and fixes .NET MAUI development environment issues. Validates .NET SDK, workloads, Java JDK, Android SDK, Xcode, and Windows SDK. All version requirements discovered dynamically from NuGet WorkloadDependencies.json — never hardcoded. Use when: setting up MAUI development, build errors mentioning SDK/workload/JDK/Android, "Android SDK not found", "Java version" errors, "Xcode not found", environment verification after updates, or any MAUI toolchain issues. Do not use for: non-MAUI .NET projects, Xamarin.Forms apps, runtime app crashes unrelated to environment setup, or app store publishing issues. Works on macOS, Windows, and Linux.
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 skill when the user is dealing with the DOCA environment around their workload — verifying an install is healthy, preparing the build env (pkg-config, headers, LD_LIBRARY_PATH, hugepages, devlink, representors), debugging env-class failures, deciding container-vs-bare-metal deployment shape, or reaching a DOCA install from a host that doesn't have one yet via the NGC DOCA container Stage-1 fallback. Trigger even when the user does not explicitly mention "DOCA setup" — typical implicit phrasings include "I just got a BlueField, what now", "my code is built, how do I run it", "pkg-config can't find doca-flow", "no free 2048 kB hugepages", "representor X not found", "I'm on a Mac and want to learn DOCA". Refuse and route elsewhere for library API specifics (Flow pipes, RDMA queues), the modify-a-sample first-app workflow or DOCA_ERROR_* program-side debugging, and "where is X documented" knowledge-map questions — those belong to other 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。
Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
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。
Release workflow for deploying OpenEnv environments to Hugging Face Spaces and keeping canonical references in sync.
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。
Deploy an OpenEnv environment to Hugging Face Spaces. Use when asked to deploy, push to Hugging Face, or update a space.
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。