「evaluate」の検索結果

Evaluate, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Codex session history). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Claude Code transcripts). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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。

Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.

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。

Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.

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。

Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.

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。

AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

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 WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.

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。

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

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。

Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

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。

When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.

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。

When the user wants to identify, evaluate, or prioritize potential investors for a fundraising round. Also activates when the user asks "who should I pitch?", "find me investors", "build an investor list", or mentions VC/angel targeting.

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。

When the user needs to review an existing contract, assess risk in proposed terms, or evaluate a contract before signing.

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。

When the user needs to design or evaluate system architecture — service boundaries, data models, API contracts, infrastructure topology, database selection, or dependency analysis. Also activate for "design the system", "how should I architect this", "monolith vs microservices", or architecture decision records.

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。

When the user needs to evaluate competitors, understand the competitive landscape, or position their product against alternatives.

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。

Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.

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 ML models for construction predictions. Train and evaluate custom models for cost, duration, and risk prediction.

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。

Assess organization's digital transformation readiness. Evaluate data culture, technology adoption, and process maturity.

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 and compare subcontractor bids against CWICR benchmarks. Evaluate pricing, identify outliers, and support negotiation.

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。

Building energy simulation and analysis for construction. Calculate heating/cooling loads, evaluate envelope performance, optimize HVAC sizing, and ensure energy code compliance.

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。

MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.

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 apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

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 technical options with scoring matrix, trigger Gate 4 for significant 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。

Evaluates test suites for coverage gaps, TDD/BDD compliance, and anti-patterns. Use when auditing test quality or before a major release.

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。

Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.

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。