「evaluation」の検索結果

Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".

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。

DeFi yield evaluation including fee APR, real vs nominal yield, net APY after costs, and yield sustainability 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。

Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading

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。

Iteratively review and revise a plan until no new findings survive evaluation. Use when the user asks to "refine the plan", "iterate on the plan", "tighten the plan", or "improve the 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。

Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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。

Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.

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。

Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.

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。

Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.

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 contractor bids against CWICR benchmarks. Identify pricing anomalies, compare bid components, and support bid evaluation 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。

Drive the agentkit CLI (aliases agentkit / ak) to scaffold, build, deploy, and manage agents on Volcengine AgentKit from the terminal — runtimes, knowledge bases, memory collections, tools, harnesses, invocation, SSO auth, sandboxes, and evaluation datasets. Use when a user wants to create/deploy an agent project, call or inspect a deployed runtime, manage AgentKit resources, or resolve a CLI/auth/deploy error. Does NOT handle non-AgentKit deployment, general Docker/k8s operations, Volcengine IAM management, or writing agent application code (use veadk-agent-development for that).

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Multi-dimensional rep evaluation: activity, conversion, velocity, deal size. Peer benchmarking and coaching priority identification.

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。

Creates structured hiring scorecards for any role. Takes job title, requirements, and team context. Generates comprehensive scorecard with weighted scoring rubric, interview questions per competency, evaluation matrix, red/green flags, and reference check questions.

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Review contracts for concerning clauses, extract key terms, compare to standard terms, and flag unusual provisions. Use when user needs contract review, legal document analysis, or agreement 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。

Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality 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。

Processes external resources into stored knowledge with quality scoring and routing. Use when ingesting articles, papers, or docs into a memory palace.

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 Claude Code rules in .claude/rules/. Use for frontmatter, globs, and quality audits.

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 Claude skill quality through auditing. Use when reviewing or auditing skills.

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。

Evaluate hook security, performance, and SDK compliance. Use for audits.

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Renamed to /model-assessment in v6 (removed in v7).

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。

Tạo AI Skill mới từ ý tưởng hoặc quy trình công việc. Kết hợp phỏng vấn thông minh, pattern detection, và quantitative evaluation để tạo skill chất lượng production. Dùng khi user nói "tạo skill", "biến quy trình thành skill", "make a new skill", "tự động hóa công việc này", hoặc mô tả quy trình lặp lại mà họ muốn AI tự làm. Cũng dùng khi user muốn cải thiện, test, hoặc optimize skill hiện có.

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 fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation.

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 FastVideo video, image, and audio generation, config-first inference, serving, training, distillation, evaluation, and performance 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。