「local」の検索結果

利用中のAIエージェントにmemUの長期記憶を導入・解除するスキル。実行環境に合うガイドを選び、会話ログの定期保存と回答前の記憶検索を設定・検証します。

• AIエージェントに長期記憶を追加
• 端末内に記憶を保存したいとき
• MemU Cloudへの接続設定

CARLAのソースと文書を照合し、APIの記載漏れや古い説明、リンク切れを探します。ビルドせずに確認し、種類別の件数と指摘をレポートにまとめます。

• リリース前に文書を点検したいとき
• APIの記載漏れや古い説明の確認
• リンク切れやナビゲーション漏れの確認

TVMのTIRxとtirx-kernelsのテスト環境を確認し、GPUを使う並列テスト、読み込みと計算結果の検証、失敗の分類とログ付きの結果報告を進めるスキル。

• TIRxとカーネルの変更を検証したいとき
• tirx-kernelsの読み込みと登録を確認
• 指定GPUでテストを並列実行

文章や画像の意味を数値で表す埋め込みを生成し、意味検索や類似度比較、文書のグループ分けに使うスキル。多言語モデルの選択やローカル実行も案内します。

• RAG用の文書埋め込みを作りたいとき
• 意味の近い文章を探したいとき
• 文書を話題ごとにまとめたいとき

NVIDIA Cosmos Policyのロボット操作を、LIBEROやRoboCasaの仮想環境で評価します。GPU環境の準備から画面なしでの実行、結果確認まで案内するスキルです。

• LIBEROで小規模評価を試したいとき
• RoboCasaのキッチン操作評価
• 画面のないGPUクラスタでの環境構築

Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML 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。

CPUやApple Silicon、AMD・IntelのGPUでLLMを動かす手順を案内します。GGUF形式のモデル選びから量子化、チャット用APIの起動まで扱います。

• Apple SiliconのMacでLLMを動かしたいとき
• CPUのみの端末でLLMを試したいとき
• メモリ容量に合うモデル選び

機械学習向けの大量データを読み込み、前処理や一括推論を複数のCPU・GPUに分散するスキル。手元の端末からクラスタまで、処理規模に応じた構成を案内します。

• メモリに収まらないデータを前処理したいとき
• 複数GPUで一括推論したいとき
• 画像や表データを学習用に整えたいとき

Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, memory_share, memory_unshare, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, network_memory_detail, network_skill_pull, network_team_info, memory_timeline, memory_viewer.

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。

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

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。

Read existing Codex Security Cloud findings, reports, recorded assignment and verification, and scan status from the connected account. Use for Cloud read requests and finding links, not local scans or requests to change Cloud 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。

Spin up a live local Omnigent server and exercise the GitHub Copilot SDK harness end-to-end — build copilot agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the copilot harness (omnigent/inner/copilot_executor.py, copilot_harness.py, omnigent/onboarding/copilot_auth.py) or its auth / model / tool-bridge behavior.

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。

Spin up a live local Omnigent server + runner and exercise the native Pi TUI harness (pi-native) end-to-end — launch the real `pi` CLI via `omnigent pi`, drive turns through the web/bridge, smoke-test, and bug-bash. Load when developing, testing, or debugging the pi-native harness (omnigent/inner/pi_native_executor.py, pi_native_harness.py, omnigent/pi_native.py, pi_native_bridge.py, pi_native_credentials.py) or its bridge / extension / auth / model behavior.

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。

Spin up a live local Omnigent server and exercise the Cursor SDK harness end-to-end — build cursor agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the cursor harness (omnigent/inner/cursor_executor.py, cursor_harness.py, cursor_auth.py) or its auth / model / tool-bridge behavior.

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。

Spin up a live local Omnigent server + runner and exercise the native Antigravity (agy) TUI harness (antigravity-native) end-to-end — launch the real `agy` CLI via `omnigent antigravity`, drive turns through the web UI, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity-native harness (omnigent/inner/antigravity_native_executor.py, omnigent/antigravity_native.py, antigravity_native_bridge.py, antigravity_native_rpc.py, antigravity_native_reader.py, antigravity_native_launch.py) or its agy launch / RPC mirror / tmux delivery / OAuth / MCP-relay behavior. NOT the in-process `antigravity` Gemini SDK harness.

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。

Run the Omnigent load test and produce a results file explaining the latencies. Load when the user wants to load-test / stress-test / benchmark Omnigent under concurrency ("load test omnigent", "stress test the server", "how many hosts/sessions/turns can it handle", "load test real agent turns / conversations", "run a load test"). The test makes each simulated user a real omnigent host that creates host-bound sessions and drives real multi-turn conversations with a mocked LLM; it boots its own local stack (dev/loadtest/run.py). Gather inputs, run it, then read the generated summary.md and explain the latency distribution (avg/median/p95/p99, throughput, failures). NOT for single-request latency micro-benchmarks (that is dev/benchmarks/).

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。

Commit the validated fix and follow local-only, workflow-owned, or direct PR publication.

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。

Spin up a live local Omnigent server and exercise the Antigravity (Gemini) SDK harness end-to-end — build antigravity agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity harness (omnigent/inner/antigravity_executor.py, antigravity_harness.py, omnigent/onboarding/antigravity_auth.py) or its auth / model / tool-bridge behavior.

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。

End-to-end test the polly multi-agent coding orchestrator's critical user journeys (CUJs). Two halves — a deterministic mock-LLM driver (polly_cuj.py) that boots a throwaway local server + mock LLM and asserts the substrate (boot, bridged sys_* tool dispatch, the blast_radius / spawn_bounds / headless_subagent_purpose_guard guardrails, fan-out delegation), and a live real-CLI recipe (real claude/codex/pi, real worktrees/PRs) for polly's actual judgment. Load when developing, testing, or debugging examples/polly — its config.yaml, the claude_code/codex/pi sub-agents, the investigate/fanout/cross-review skills, or the omnigent.inner.nessie.policies guardrails — or reproducing a polly orchestration bug.

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。

Full-text search across all of the user's GitHub repos (including private ones) using a local mirror and ripgrep. Use for "where did I put X", "which repo has X", or any search spanning the user's repos - gh search code / the REST API cannot reliably search private repos.

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 a paid-media client, account, data-source, privacy, and mutation-guardrail profile for Claude Ads. Use for onboarding, initial configuration, API tokens or credential profiles, environment-variable or keychain setup, connecting exports or read adapters, safe native or verified-local installation, curl-pipe-bash install requests, declaring KPIs, or preparing a new advertising project.

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 paid-ad landing pages for message match, mobile experience, performance, accessibility, trust, forms, consent, tracking, security, and conversion friction. Use for landing-page audit, post-click experience, LP audit, conversion-rate optimization, form optimization, ad-to-page message match, redirects, blocked navigation, or requests involving private, loopback, link-local, or metadata IP destinations.

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。

Case status summary by audience — client-facing (plain language), internal (for the professor), or court-ready (formal caption format per local rules). Same facts, different framing and depth. Use when a student needs to update the client, brief the professor, or prepare a court status report.

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 a clinic supervisor author a practice-area guide that configures how student-facing skills behave — intake questions, pedagogy posture (assist / guide / teach), review gates, cross-plugin checks, and local rules. Use when a supervising attorney wants to build or revise a per-practice-area guide, tune how the clinic skills behave for their clinic type, or set their teaching philosophy as plugin configuration.

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。