「mcp」の検索結果

Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.

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。

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

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。

Analyze tau2-bench task export files to identify exactly where and why tasks failed. Map failures to specific conversation rounds, show root causes, check whether relevant documentation was available, and cite sources. Use this skill whenever you need to debug a tau2-bench evaluation, understand why an agent made wrong decisions, identify missing knowledge, or review task performance—especially when context length, eligibility criteria, or policy compliance might be at fault.

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。

Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Use this to automate part of an operation, design an agent or MCP workflow, reduce repetitive manual work, connect tools into a system, decide which automation to build first, or audit an automation that is not delivering.

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 TypeUI with Hermes Agent to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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 TypeUI with Grok to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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 TypeUI with OpenClaw to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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。

Xiaodu smart device control skill via MCP protocol. Control Xiaodu devices and ecosystem hardware for smart home IoT tasks, scene automation, and physical interaction. Use when user wants to control smart home devices or IoT equipment.

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。

Tencent Meeting MCP assistant for meeting lifecycle management. Create, modify, cancel meetings, track attendance, export recordings, query transcripts, and generate smart minutes. Use when user mentions online meetings, video conferencing, or Tencent Meeting operations.

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。

Automate Google Calendar events, scheduling, availability checks, and attendee management via Rube MCP (Composio). Create events, find free slots, manage attendees, and list calendars programmatically.

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。

FlyAI travel search and booking skill powered by Fliggy MCP. Search flights, hotels, attractions, trains, concerts, and travel deals with natural language. Supports diverse travel scenarios including individual, group, business, family trips. No API key required for basic features.

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。

Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, and handle deployments programmatically.

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。

Automate Gmail tasks via Rube MCP (Composio): send/reply, search, labels, drafts, attachments. Always search tools first for current schemas.

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。

使用AgentShield扫描您的Claude代码配置（.claude/目录），以发现安全漏洞、配置错误和注入风险。检查CLAUDE.md、settings.json、MCP服务器、钩子和代理定义。

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。

使用 AgentShield 扫描 Claude Code 配置（.claude/ 目录）中的安全漏洞、配置错误和注入风险。检查 CLAUDE.md、settings.json、MCP 服务端、钩子（Hooks）和智能体（Agents）定义。

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。

使用 AgentShield 扫描您的 Claude Code 配置（.claude/ 目录）是否存在安全漏洞、配置错误和注入风险。检查项包括 CLAUDE.md、settings.json、MCP 服务器、钩子（Hooks）以及智能体（Agent）定义。

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。

Single entry skill for open-websearch setup and focused live retrieval, preferring local CLI/daemon paths while remaining compatible with workspace-exposed MCP tools.

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。

Maintain and extend the open-websearch MCP server. Trigger when changing search engines, proxy or TLS behavior, tool registration, live parsing behavior, or compatibility and regression behavior inside this repository.

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。

This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration

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 Vite with vite-plugin-singlefile for mandatory single-file HTML bundling of MCP Apps. All assets (JS, CSS, images, fonts) must be inlined into a single HTML file for sandboxed iframe compatibility.

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。

Master C#/.NET backend development patterns for building robust APIs, MCP servers, and enterprise applications. Covers async/await, dependency injection, Entity Framework Core, Dapper, configuration, caching, and testing with xUnit. Use when developing .NET backends, reviewing C# code, or designing API architectures.

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。

Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration

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。

Triage academic email and calendar (Gmail / Google Calendar via the session's MCP) into a prioritized digest plus a referee-obligations tracker — classifying referee requests, R&R and editor correspondence, co-author threads, seminar and conference invites, and grant/admin deadlines, and proposing a human-gated action for each (draft reply, calendar hold, scaffold a project, snooze). Use when user says "triage my inbox", "check my academic email", "what needs my attention this week", "go through my mail", or when run as a scheduled routine. NEVER auto-sends or auto-accepts anything.

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。

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

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。