MCP servers and skills: search, filter by where they run, and compare.
1–24 of 239 tools for “experiments”
Clear allBuild and run evaluators for AI/LLM applications using Phoenix.
OfficialSkillApache-2.0★ 40k in collectionNo key
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use
OfficialSkillMIT★ 40k in collectionNo key
Code snippets to reproduce MCP tool poisoning attacks.
MCPLink only★ 207
AI Model Experiments MCP ('Model Lab') — run the same prompts across many
Hosted by authorMITFew starsNo key
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, tes
OfficialSkillMIT★ 40k in collectionNo key
This project provides an MCP (Model Context Protocol) server that allows LLMs to perform actions to control a Mac desktop computer using bash and AppleScript.
SkillMIT★ 1Few starsNo key
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – c
OfficialMCPLink only★ 40k
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and cont
OfficialSkillMIT★ 40k in collectionNo key
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, an
OfficialSkillApache-2.0★ 40k in collectionNo key
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating p
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). S
OfficialSkillApache-2.0★ 11k in collectionNo key
Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.
OfficialSkillApache-2.0★ 3.6k in collectionNo key
turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.
SkillApache-2.0★ 1.5kNo key
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and public
SkillMIT★ 5.8kNo key
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the op
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` t
OfficialSkillLink only★ 28k in collection
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
SkillApache-2.0★ 23kNo key
A scientific-method runtime for AI agents: hypotheses, experiments, evidence, provenance, reproducibility, and auditable conclusions.
PythonApache-2.0★ 21No key
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Long-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Convert existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or prelimi
OfficialSkillApache-2.0★ 3.6k in collectionNo key
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