Plugins
7 plugins@dotnet
Dotnet Experimental
Dotnet Experimental skills from dotnet/skills.
3 skills · plugin
curated
Prioritize Assumptions and Experiment
Install this pack to prioritize assumptions and design targeted experiments.
3 skills · plugin
curated
Experimentation Pipeline
From hypothesis to impact reporting, this pack enables rigorous experimentation and evidence-based decisions.
4 skills · plugin
@phuryn
Product Discovery
Product discovery skills for PMs: ideation, experiments, assumption testing, feature prioritization, and customer interview synthesis.
13 skills · plugin
curated
Validate Product Idea
Validate a product idea by clarifying intent, identifying risky assumptions, and designing experiments to test them.
4 skills · plugin
curated
Validate New Product Idea
Stress-test assumptions, design experiments, and validate a new product idea using lean startup methods.
3 skills · plugin
@alirezarezvani
Product Team
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 · plugin
Results for “experiment”
4 skills
cloudthinker-ai managing-eppo
Manage feature flags, experiments, and metrics in Eppo via its REST API, including flag configuration, experiment design, statistical analysis, and metric pipelines.
7
paramchordiya mlops-and-infra
Enforces ML infrastructure, experiment tracking, reproducibility, model packaging, CI/CD, monitoring, and infrastructure-as-code standards at principal-engineer level.
0
More results
jeffallan ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
k-dense-ai arbor
Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.
30.2k · bundle