「pack」の検索結果

Deploy the QM package from an organization-owned deployment repository to Fly.io or AWS, onboard an administrator, configure connectors, and optionally activate Slack.

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。

Update a QM source fork by merging upstream, or upgrade a package deployment dependency, and open a PR. Use when asked to "update qm", "sync from upstream", "pull in the latest qm".

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 a database migration with Drizzle on a fresh Neon branch, and check it is safe to run against production under load. Use when changing the packages/db schema, generating migrations, or reviewing one.

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。

Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.

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。

Starts a CARLA UE 5.8 server from a ue58-dev tree — the editor binary in -game mode (the working headless server), the packaged shipping build, or the full editor — with offscreen/nullrhi rendering, custom RPC and streaming ports, and native ROS 2 via the single-dash -ros2 flag. Probes a running server, and stops one cleanly despite the 15-character process-name truncation. Use when the user asks to "run/start CARLA UE5", "launch a ue58 server", "run headless", "publish ROS 2 topics from UE5", or a client cannot reach the simulator.

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。

Downloads CARLA so nothing has to be fetched by hand — an official release package for any published version (resolved live from the GitHub releases, with AdditionalMaps), the rolling nightly Dev build, a shallow source checkout of a branch or tag, or the carlasim/carla Docker image. Reports exactly what was obtained and where, plus the environment variables the other skills read. Use when the user asks to "download CARLA", "get CARLA 0.9.16", "install CARLA", "clone the carla repo", "fetch the latest CARLA", or has no CARLA at all yet.

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。

Installs CARLA's ScenarioRunner (the official scenario engine) and picks the branch that matches the CARLA in use — master for 0.9.14-0.9.16 (UE4), ue5-master for 0.10.0 (UE5), leaderboard-1.0/2.0/2.1 when driving the Leaderboard. Clones or checks out the branch, installs requirements, wires PYTHONPATH (including the `agents` package the carla wheel does not ship), and verifies the import. Use when the user asks to "install scenario runner", "set up SR", "run CARLA scenarios", "use OpenSCENARIO", or has a scenario_runner that fails to import.

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。

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

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。

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

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。

Scan package manifests and lockfiles for outdated and vulnerable dependencies. Classify by severity and update type.

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, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

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 demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in 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。

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 for a standard, single-pass security audit of an entire repository or a scoped path, package, folder, or submodule with no diff to review. This is the default repository scan. Do not use for PR, commit, branch, or working-tree diffs, or for deep, multi-pass scans.

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。

Author a new Agent Skill for a Hive agent that conforms to the Agent Skills specification (SKILL.md with YAML frontmatter, optional scripts/references/assets directories). Use when the user asks to create, scaffold, add, or package a new skill for a Hive agent.

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。

Auto-generate a professional, client-ready GEO service proposal from audit data. Creates a full proposal in markdown and PDF including executive summary, findings, recommended service packages (Basic/Standard/Premium), pricing, timeline, and terms. Use when user says "proposal", "proposta", "offerta", "preventivo", "generate proposal", or after completing a GEO audit for a prospect.

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。

Draft or refresh Omnigent package release notes for the next version using the existing GitHub Actions preview, merged PR material, and release-note composer. Use when preparing release highlights before a tag exists or reviewing a release-note draft; does not cut or publish a release.

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。

Conventions for editing Bazel files in the github/codeql repository: the shared `misc/bazel` helpers, `codeql_platform_select` and the `{CODEQL_PLATFORM}` packaging placeholder, adding `MODULE.bazel` dependencies, and the `semmle_code` stub that keeps the standalone build working. Use when editing any `BUILD.bazel`, `*.bzl`, `MODULE.bazel` or `.bazelrc` here, and before validating such an edit.

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。

Practice-profile interview that recommends and installs a starter pack of community legal skills. This IS the cold start for the whole ecosystem — it asks what kind of lawyer you are and recommends what to install first. Use on fresh install, when the user says "get me started" or "what should I install", or to re-run the integration-availability check after adding or removing an MCP connector.

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。

Guided customization of your Legal Builder Hub profile — change one thing without re-running the whole cold-start interview. Adjust practice profile, installed starter pack, watched registries, update preferences, or QA strictness. Use when the user says "change my [thing]", "add a registry", "update my profile", "edit my config", or "customize".

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。

Publish the Octop Python package: cut a release branch from develop, bump version, update CHANGELOG, open a PR to main; after merge, Actions tag on main (PyPI / Docker Hub + GHCR) and sync main into develop. Use when the user asks to publish, release, bump version, cut a release, or run /publish.

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 for ClawHub staff moderation actions with the repo-local ClawHub admin tool: skills, users, org publishers, plugin packages, trusted publishers, official publishers, and guarded staff email.

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 an idea or objective into a goal package for /goal. Interviews the user, builds a reviewed fact sheet via Plannotator, then explores the codebase to produce an execution plan.

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 and execute a Plannotator release — draft release notes with full contributor credit, bump versions across all package files, build in dependency order, and kick off the tag-driven release pipeline. Use this skill whenever the user mentions preparing a release, bumping versions, writing release notes, tagging a release, or publishing. Also trigger when the user says things like "let's ship", "prep a release", "what's changed since last release", or "time to cut a new version".

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。