Results for “evaluation”

Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.

Guides users through creating, refining, and evaluating agent skills, including drafting, testing, and optimizing descriptions for better triggering.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Provides guidance on using the Aeon library for time series forecasting, covering model selection, implementation, and evaluation.

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Manage NVIDIA Brev GPU instances for TAO training, evaluation, and inference using the Brev CLI and Docker.

Automatically evaluates Claude Code sessions to extract reusable patterns and save them as learned skills.

Audits UX quality of a codebase or validates implementation against design mockups, then fixes issues and commits changes.

Evaluates execution transcripts and output files against a list of expectations, assigning pass/fail verdicts with cited evidence and critiquing the assertions themselves.

Guides data protection impact assessments under GDPR Article 35, covering mandatory triggers, risk evaluation, and mitigation steps.

Summarize a contributor's Git history, a provided work log, or both into a concise, review-friendly self-evaluation for quarterly, semi-annual, or promotion cycles.

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

Use this skill when selecting a TPR-registered Training Provider for behind-the-wheel ELDT training. Covers the registry, evaluation criteria, and common pitfalls.

Implement OpenFeature feature flags in software projects. Use when adding feature flags with OpenFeature SDKs, configuring providers, setting up evaluation context, or integrating the OpenFeature MCP Server.

Provides technical leadership frameworks for architecture decisions, engineering team scaling, technology strategy, and technical debt assessment.

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

Compare coding agents head-to-head on reproducible tasks with pass rate, cost, time, and consistency metrics.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Instrument LLM applications with Langfuse to trace, score, and monitor cost, quality, and latency across OpenAI and LangChain integrations.

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for Python and TypeScript.

Guides users through creating, editing, and optimizing agent skills, including drafting, testing, evaluating, and improving skill descriptions for better triggering.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Computes the InfoLM metric from torchmetrics for evaluating text generation against ground truth, with configurable information measures and sentence-level scoring.

Scores generated text for morphosyntactic well-formedness by measuring how closely it adheres to language-specific dependency rules extracted from treebanks.

Evaluates machine-generated log summaries without human-written references, using LLM judgment and dense embeddings to score relevance, informativeness, and coherence.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Design high-level system architecture, create Architecture Decision Records (ADRs), evaluate technology trade-offs, and plan for scalability.

Dataset analysis and data quality specialist. Use when the user asks to talk to Cypher, requests the data detective, or needs dataset assessment, bias analysis, and benchmark evaluation.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

Spawns multiple parallel AI agents that compete on the same task using isolated git worktrees, evaluates results, and merges the best solution.

Build AI applications using the Azure AI Projects SDK for TypeScript, managing agents, connections, deployments, datasets, indexes, and evaluations.