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大規模言語モデルの知識・推論・コード生成を共通の課題で評価するスキル。複数モデルの比較表や学習途中の成績推移を作り、評価結果と個別の回答を保存します。

• MMLU・GSM8Kでのモデル評価
• 複数モデルの比較表作成
• 学習途中のモデルの成績追跡

Query available AI models, list model aliases, and browse the full model catalog from the CLI. Filter by provider, search by capability, and resolve model name variants.

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。

Query available AI models across all configured providers. List models, resolve model aliases, and browse the full model catalog including provider-specific variants.

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。

Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.

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 Model Context Protocol (MCP) servers in C#/.NET against the current ModelContextProtocol 2.x NuGet packages. Helps with cases the model gets wrong without guidance — stale versions (0.x preview or 1.x-era defaults), the v2 stateless-by-default HTTP flip, the 2026-07-28 spec deprecations (roots/sampling/logging), MCP Apps and Tasks extension packages, elicitation URL mode, per-session HTTP wiring, OAuth and reverse-proxy deploy specifics, and debugging MapMcp / STDIO / Streamable-HTTP errors. Also covers STDIO and Streamable HTTP transports (SSE is deprecated), tools, prompts, resources, completions, and a basic .NET MCP client. Trigger when the user says or implies any .NET MCP server work: ModelContextProtocol, McpServerTool, MapMcp, WithStdioServerTransport, "MCP server in C#", "MCP tool in dotnet", "expose this as MCP", or names a primitive (prompt/resource/elicitation/MCP App) in a .NET context. Skip for MCP work in other languages.

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 a new model to the SGLang Cookbook (docs/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an MDX page, the docs.json nav entry, NEW-tag hygiene, and the homepage vendor card. Interactive, multi-phase. Run with /cookbook-add-model.

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。

Detect MITRE ATLAS AML.T0024 attacks (model stealing, inversion, membership inference) performed via inference-API abuse, by monitoring per-principal query volume/distribution, rate-limiting and perturbing outputs, and red-teaming your model's extractability. Use for a public or partner inference API needing cloning/inversion/membership-inference detection, or a pre-deployment red-team exercise to measure extraction risk.

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。

Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

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 Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design 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。

Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

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。

Defensive threat modeling and risk management for your own estate — map where sensitive data lives across your asset graph, run compromise scenarios (what a hacked asset exposes, its blast radius, how you'd respond), and maintain a persistent risk register with likelihood×impact scoring, owners, mitigations, and review cadence via a deterministic CLI. All real data stays in your private USER tree; this skill is code only. USE WHEN threat model, threat modeling, risk register, risk assessment, what if X got hacked, compromise scenario, blast radius, sensitive data map, where is our sensitive data, data classification, risk review, add a risk, accept a risk, security risk posture. NOT FOR active pentesting or exploitation (use an offensive-security skill), world-scale futures stress-testing of ideas (use WorldThreatModel), or executing incident response (use your incident-response runbooks — this skill plans them).

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design 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。

Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.

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。

Runs Scenic probabilistic scenarios against a running CARLA server with the `scenic FILE --simulate --2d` CLI — the carlaChallenge1-10 traffic scenarios shipped by ScenarioRunner (`model srunner.scenic.models.model`) and by Scenic itself (`model scenic.simulators.carla.model`) — bounding the run, sampling scenes headlessly first to tell a map problem from a syntax problem, and restoring the world afterwards. Use when the user asks to "run a Scenic scenario", "run carlaChallenge", "test a .scenic file", "scenic --simulate", or reports a Scenic scenario that rejects every sample or cannot find blueprints.

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 Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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 fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user asks to threat model a codebase or path, enumerate threats or abuse paths, or perform AppSec threat modeling. Do NOT use for general architecture summaries, code review, security best practices (use security-best-practices), or non-security design 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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design 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。

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

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。

How to swap the DeepStream CV detection model in the VSS Alerts Blueprint verification (2d_cv) mode - covers ONNX export, custom bbox parsers, compose mount gotchas, nvinfer config, runtime TRT engine build, deployment, and a segmentation-capable model addendum handoff.

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 you need to add, remove, edit, list, or change the boot default of an nvpmodel power mode on a Jetson/Tegra (Orin, Thor) target. Triggers: edit power mode, tune frequency caps.

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。

Onboard a new model generation or sibling into oh-my-hermes: probe router recognition, research the official contract, write trait-to-counter calibration, place routing in both lanes, price from documented list only, gate machine config on a served route, prove with the gates, close with a benchmark pair. Use when a model family ships a new generation (for example "Fable 5.1 is out", "GLM 5.4 shipped", "onboard new model", "add model to chains").

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。