“LLM evaluation”

A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard.

Use when maintaining repository Markdown, Copilot instructions, Codex AGENTS.md, repo skills, prompts, or LLM evaluation tasks.

INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks…

Evaluate a call recording with a multimodal LLM.

Evaluation — an observability concept covering evaluation, score, drift, shadow, benchmark.

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

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production.

Use when designing, configuring, running, or troubleshooting NeMo Agent Toolkit evaluations, datasets, evaluator selection, ATIF surfaces, quality gates…

Evaluate one design or a user-approved maturity-mapped batch through a transparent evidence-based rubric.

Use when the user wants proof that finished work meets its acceptance criteria, not just that tests pass: 'verify it is actually done', 'check it against the…

Test and benchmark LLM agents with behavioral contract testing, capability assessment, statistical/adversarial evaluation, regression pipelines, and…

This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or…

Open-source AI observability platform for LLM tracing, evaluation, and monitoring.

Runs unit, golden-dataset, and LLM-as-judge tests for a skill.

Composite scoring and efficiency frontier analysis for LLM evaluation — combines multiple quality dimensions (accuracy, latency, cost, consistency) into a…

Public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, decision-support agent, or adjudicative system based on…

LLM observability platform for tracing, evaluation, and monitoring.

Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4…

Attach judges to config variations for automatic LLM-as-a-judge evaluation.

Build evaluation pipelines for AI outputs — create datasets, write evaluators, and measure quality systematically.

MCP as a Judge: a behavioral MCP that strengthens AI coding assistants via explicit LLM evaluations

Use when building, running, or debugging evaluations for LLM or agent components: golden sets, regression evals, LLM-as-judge, rubric scoring…

Inspects the plan, milestones, tasks, and generated session schedules to certify they meet formatting guidelines, safety constraints, and scheduling…

Evaluate agent behavior with versioned cases and explicit verifiers.