Plugins
3 pluginscurated
ML Model Lifecycle
Train, evaluate, and deploy a production ML system with monitoring.
10 skills · plugin
@owl-listener
Visual Critique
Visual critique skills: hierarchy analysis, brand consistency checks against mood/voice/tokens, composition evaluation, and typography audits — with a /critique-screen command that compiles a prioritised fix list.
7 skills · plugin
@alirezarezvani
Agenthub
Multi-agent collaboration — spawn N parallel subagents that compete on code optimization, content drafts, research approaches, or any task that benefits from diverse solutions. 7 slash commands (/hub:init, /hub:spawn, /hub:status, /hub:eval, /hub:merge, /hub:board, /hub:run), agent templates, DAG-based orchestration, LLM judge mode, message board coordination.
8 skills · plugin
Results for “l-eval”
240 skills
github Phoenix Evals
Build and run evaluators for AI/LLM applications using Phoenix, covering error analysis, custom evaluators, experiments, and production monitoring.
36.2k · bundle
antigravity Evaluation
Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.
42.4k
alirezarezvani Eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.
20.4k
dvy1987 Eval Rubric Design
Design structured evaluation rubrics for scoring LLM and agent outputs — defining quality dimensions, scoring scales, hard gates, score descriptions, and edge cases. Load when the user asks to create an eval rubric, define evaluation criteria, design scoring dimensions, write an eval spec, or says "what should I evaluate", "design a rubric", "create eval criteria", "define quality dimensions", "evaluation rubric for", "how do I measure quality of". Sub-skill of eval-output orchestrator.
3 · bundle
nvidia Nemo Evaluator Plugin
Run evaluation tasks against a NeMo Platform server using the Evaluator plugin CLI and Python SDK.
2.2k · bundle
dvy1987 Eval Output
Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation workflows.
3 · bundle
More results
samyakjhaveri Eval Run
Launches a model evaluation batch with parameter collection, pre-flight checks, execution, and post-run analysis for interactive or foreground runs.
0
orchestra-research Evaluating Cosmos Policy
Evaluate NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments with headless GPU evaluation and inference profiling.
10.4k · bundle
orchestra-research Langsmith Observability
Debug, evaluate, and monitor LLM applications with tracing, datasets, and built-in evaluators.
10.4k · bundle
github Agentic Eval
Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.
36.2k
orchestra-research Nemo Evaluator Sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution on local Docker, Slurm HPC, or cloud platforms.
10.4k · bundle
qcmuu Evaluating Cosmos Policy
Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.
0 · bundle
lucassantana-dev Eval
Evaluate LLM outputs systematically — benchmarks, automated metrics, human preference, and regression tracking
1 · bundle
github Eval Driven Dev
Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.
36.2k · bundle
kursku Advanced Evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
55 · bundle
samyakjhaveri Overnight Eval
Launches long-running evaluation batches in isolated tmux sessions with pre-flight verification, monitoring, and post-flight analysis for unattended runs.
0
github Arize Experiment
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.
36.2k · bundle
seaworld008 Oracle
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.
65 · bundle
kintsugi-programmer LLM Evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
0
qhjqhj00 L Eval
Benchmarks long-context language models across 20 sub-tasks spanning 3k–200k tokens, covering retrieval, reasoning, summarization, and instruction understanding, with exact-match accuracy as the primary metric.
3
jarbitechture Eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.
0
ssrjkk LLM Eval
Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.
2 · bundle
thedixitjain Eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.
2
dylanckawalec Eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session.
3
sinhoneyy Eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.
11
github Arize Evaluator
Creates and runs LLM-as-judge evaluators on Arize, including managing tasks, column mappings, and continuous monitoring.
36.2k · bundle
qhjqhj00 Cab Eval
Benchmarks LLM bias by scoring responses to automatically generated open-ended questions across sensitive attributes, producing a composite fitness score from 0 to 5.
3
netanel-abergel Eval
Evaluate everything the PA agent manages — tasks, skills, PA network health, billing, calendar connections, and memory quality. Use when: owner asks for an evaluation, wants to know what's working and what isn't, or requests a performance report. Combines supervisor status with quality scoring.
6
lambenthan Exp Eval
实验判决门：Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges
77
qhjqhj00 Caa Eval
Benchmarks large audio-language models against adversarial audio attacks using the CAA dataset, computing WER, ROUGE-L, cosine similarity, and coherence scores to assess robustness in conversational settings.
3
affaan-m Eval Harness
Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.
226k
projectious-work Eval Gate Authoring
Turn observed run outputs into eval-spec Artifacts, paired Gates, and policy bindings. Use when creating or calibrating automated, human, or LLM-as-judge eval gates for processkit workflows.
0 · bundle
rajanthar Eval Harness
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
0
jarbitechture Agent Eval
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
0
qhjqhj00 Aya Eval
Evaluates open-ended generation quality of multilingual LLMs across brainstorming, planning, and long-form tasks, using AYA and DOLLY datasets with qualitative fluency and quality scoring.
3
affaan-m Agent Eval
Compare coding agents head-to-head on reproducible tasks with pass rate, cost, time, and consistency metrics.
226k