3,743 件 ・ 関連度順
概要と使いどころ
When you need to call a service the org has a SHARED credential for (a SERP/search key, a paid-API key, a data-vendor feed, a Git remote) — and the platform has told you that credential is available to this conversation — make the call BY PROXY through the credential broker. You never see the secret; the core stamps it onto the outbound request for you.
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 the CARLA Autonomous Driving Leaderboard (leaderboard.carla.org) at a chosen version — 1.0, 2.0 or 2.1 — together with the exact scenario_runner branch and CARLA build each one requires, then wires the four-root PYTHONPATH and verifies the whole stack imports. Detects which version an existing checkout is by reading its code, not its branch name. Use when the user asks to "install the leaderboard", "set up the CARLA challenge", "evaluate my driving agent", "reproduce my leaderboard score", or names a leaderboard 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。
Use when working in the embedded browser and you want the cheapest reliable interaction loop.
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 the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or 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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。
Technical SEO audit with GEO-specific checks — crawlability, indexability, security, performance, SSR, and AI crawler 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。
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action 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。
Monthly delta tracking and progress reporting for GEO clients. Compares two GEO audits (baseline vs. current), calculates score improvements across all categories, tracks action item completion, and generates a "here's your progress" client report. Use when user says "compare", "delta", "monthly report", "progress", "confronta", "progressi", "report mensile", or when running a monthly client check-in.
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 citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
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。
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
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 a professional PDF report from a GEO audit using pandoc + Chrome headless. Converts GEO-AUDIT-REPORT.md into a styled, client-ready PDF with a cover page, color-coded score tables, severity-tagged findings, and a 90-day roadmap.
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 a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions
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。
Resume work from previous session with full context restoration
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。
Retroactively verify threat mitigations for a completed phase
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。
Review and promote backlog items to active milestone
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。
Execute a quick task with GSD guarantees (atomic commits, state tracking) but skip optional agents
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。
Sketch UI/design ideas with throwaway HTML mockups, or propose what to sketch next (frontier mode)
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 persistent context threads for cross-session work
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。
Configure GSD workflow toggles and model profile
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 GSD to latest version with changelog display
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。
Spike an idea through experiential exploration, or propose what to spike next (frontier mode)
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。
Retroactively audit and fill Nyquist validation gaps for a completed phase
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 UI design contract (UI-SPEC.md) for frontend phases
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。
[BETA] Offload plan phase to Claude Code's ultraplan cloud; review in browser and import back.
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。