Plugins

3 plugins

ML Model Lifecycle

Train, evaluate, and deploy a production ML system with monitoring.

10 skills · plugin

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

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”

33 skills

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

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

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Eval Driven Dev

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

36.2k · bundle

Arize Experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.

36.2k · bundle

More results

Build evaluation frameworks for agent systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, and outcome measurement.

16.9k · bundle

Continuous Agent Loop

Provides patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

lucaspmarie-a11y

Instruments LLM applications with Langfuse for tracing, observability, and evaluation, covering setup, OpenAI and LangChain integrations, and best practices.

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs.

36.2k · bundle

Dynamic Workflow Mode

Design task-local harnesses, eval gates, and reusable skill extraction for adaptive agent workflows.

Instrument LLM applications with Langfuse for tracing, prompt management, evaluation, and cost tracking, including integrations with OpenAI, LangChain, and LlamaIndex.

Azure AI Projects Java

Manage Azure AI Foundry projects, connections, datasets, indexes, and evaluations using the Java SDK.

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

Harness Engineering

Designs autonomous agent harnesses with locked evaluators, editable surfaces, durable logging, novelty gates, pruning, rollback, and human approval boundaries.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

30.2k · bundle

Design multi-agent system architectures, generate tool schemas for Anthropic and OpenAI formats, and evaluate execution logs for cost, latency, and failure bottlenecks.

20.4k · bundle

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Gan Style Harness

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

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.

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

Benchmarks language model agents on the C2C multi-agent negotiation task, reporting win rate across starting positions.

sakamoto-family-smile

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles.

Compares coding agents head-to-head on reproducible tasks, measuring pass rate, cost, time, and consistency.

nimoqup046-collab

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

Evaluates the agent's own work, catches mistakes, and improves permanently through self-reflection, self-criticism, and learning from corrections.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Discovers and evaluates community skills from skills.sh and awesome-openclaw-skills when local skills don't cover a need, scoring credibility and installing top recommendations with permission.

Build, deploy, evaluate, debug, and measure latency for a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection and VLM classification.

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation across multiple programming languages.

Google Agents CLI Onboarding

Sets up Google's agents-cli toolkit and guides through the full agent development lifecycle: scaffold, build, evaluate, deploy, publish, and monitor on Gemini Enterprise Agent Platform.

Tao Run Deft Aoi

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.