4,129 件 ・ 関連度順
概要と使いどころ
Use when running throughput/latency (serving speed) benchmarks against an already-running model server. Uses sgl_jax.bench_serving to sweep batch-size/concurrency and measure throughput and latency, producing summary.csv and per-point bs_<N>/result.jsonl. Speed only — accuracy/eval belongs to model-accuracy-benchmark. Triggers on throughput, latency, bench_serving, batch-size sweep, tok/s, TTFT, TPOT — not accuracy/eval.
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。
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
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 ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
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 and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.
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 to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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。
Surface expert frameworks. Use when creating or evaluating skills, hooks, or 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。
Use when designing or auditing how an LLM or multimodal LLM is evaluated on a clinical task (report generation, VQA, text extraction). Covers reference standard, clinical-efficacy metrics beyond BLEU/ROUGE, hallucination, contamination and prompt sensitivity. Imaging models are /model-assessment.
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 evaluating, designing, or pressure-testing the business model of an AI agent product. Triggers on "agent business model", "agent economics", "agent canvas", "evaluate agent product", "agent pricing", "agent unit economics", "agentic business", "AI agent company", "agent cost structure".
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 FlagEmbedding for embedding, reranking, retrieval evaluation, and fine-tuning 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。
Use CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation 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。
Guides ChatGLM2-6B Transformers inference, local demos, FastAPI/OpenAI-compatible serving, C-Eval evaluation, and P-Tuning v2 fine-tuning.
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。
Operate Baichuan-7B model loading, architecture inspection, C-Eval/MMLU evaluation preflights, and DeepSpeed pretraining setup from self-contained repo guidance.
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。
Route BEIR repository tasks for benchmark data loading, retrieval evaluation, reranking, generation, and training 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。
Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.
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。
Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
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。
Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.
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 automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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。
Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).
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。
Investigate MCP tool availability, discovery, and selection, including controlled adoption evaluations.
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。
Evaluate Jacobian reliability using recently resolved conjectures as held-out probes.
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。
Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code quality.
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。
Evaluate vendors with comparison matrices, TCO analysis, risk assessment, reference check templates, and negotiation strategies
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。
Evaluate, compare, and manage vendor relationships. Trigger with "evaluate this vendor", "compare vendors", "vendor review", "should we renew", "RFP", or when the user is making procurement or vendor decisions.
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。