Packs

7 packs

Dotnet Experimental

Dotnet Experimental skills from dotnet/skills.

3 skills · pack

Prioritize Assumptions and Experiment

Install this pack to prioritize assumptions and design targeted experiments.

3 skills · pack

Experimentation Pipeline

From hypothesis to impact reporting, this pack enables rigorous experimentation and evidence-based decisions.

4 skills · pack

Product Discovery

Product discovery skills for PMs: ideation, experiments, assumption testing, feature prioritization, and customer interview synthesis.

13 skills · pack

Validate Product Idea

Validate a product idea by clarifying intent, identifying risky assumptions, and designing experiments to test them.

4 skills · pack

Validate New Product Idea

Stress-test assumptions, design experiments, and validate a new product idea using lean startup methods.

3 skills · pack

@alirezarezvani

13 product skills with 17 Python tools: product manager toolkit (RICE, PRDs), agile product owner, product strategist, UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, product analytics, experiment designer, product discovery, roadmap communicator, code-to-prd, research summarizer, apple-hig-expert.

10 skills · pack

Results for “experiment”

19 skills

arize-experiment

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

36.2k · bundle

jupyter-notebook

Create, scaffold, and edit Jupyter notebooks for experiments, exploratory analysis, or tutorials using bundled templates and a helper script.

23.3k · bundle

Run automated hyperparameter optimization for NVIDIA TAO models using AutoMLRunner, supporting multiple search algorithms and experiment tracking.

gtm-0-to-1-launch

Launch new products from idea to first customers using frameworks for positioning, outreach, and experimentation.

Build and run evaluators for AI/LLM applications using Phoenix, covering error analysis, custom evaluators, experiments, and production monitoring.

36.2k · bundle

nemo-rl-brev-etiquette

Provides storage and environment conventions for NeMo-RL agents on Brev instances, ensuring large experiment outputs go to /ephemeral and secrets are loaded from .env.

More results

tao-analyze-changenet-rca

Performs deep root cause analysis on NVIDIA TAO Visual ChangeNet classification experiments, using image-evidence-driven investigation to diagnose model failures and produce actionable reports.

huggingface-trackio

Track and visualize ML training experiments with Trackio, including logging metrics, firing alerts, and retrieving data via CLI. Supports real-time dashboards, webhook alerts, and HF Space syncing.

10.8k · bundle

nemo-rl-auto-research

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

azure-mgmt-weightsandbiases-dotnet

Manage Weights & Biases ML experiment tracking instances on Azure using the .NET SDK. Create, configure, list, update, and delete W&B instances with marketplace integration and SSO.

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

Migrate Android Jetpack Compose UI components to use the experimental Styles API for centralized theming and state-based styling.

Run file-based C# apps with the .NET CLI for quick experiments, prototypes, and small utilities without creating a project.

Manage Arize datasets and examples using the ax CLI: create, list, get, export, and append datasets for evaluation and experimentation.

36.2k · bundle

Builds reliable browser automation skills through iterative experimentation, running an inner agent to browse sites and improving navigation instructions until tasks pass consistently.

nemo-mbridge-perf-moe-vlm-training

Provides practical guidance for training Mixture-of-Experts Vision-Language Models in Megatron Bridge, comparing FSDP and 3D-parallel approaches with lessons from recent multimodal experiments.

nemo-mbridge-perf-moe-long-context

Provides guidance for training Mixture-of-Experts models with long context windows, covering context parallelism sizing, selective recomputation, dispatcher choices, and practical patterns from recent experiments.

Debug LLM applications using the Phoenix CLI: fetch traces, analyze errors, structure trace review with open and axial coding, inspect datasets, review experiments, and query the GraphQL API.

36.2k · bundle

tao-analyze-gaps-visual-changenet

Identifies the weakest samples per ground-truth label in NVIDIA TAO VCN Classify experiments by running a Docker container that performs threshold sweep, weakness scoring, and per-lighting expansion, then surfaces top-K weak samples for downstream augmentation or relabeling.