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”

37 skills

experiment-readout

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

brycewang-stanford

experiment-bridge

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.

experimentation

Orchestrator for the experimentation skill suite — turn assumptions and product questions into rigorous, well-instrumented experiments and decision-grade readouts. Routes through backlog → spec → runbook → readout based on user need and existing artefacts. Platform-agnostic with PostHog as the primary binding. Load when the user asks to design an experiment, A/B test something, set up an experiment, run a holdout, test a hypothesis, decide what to test next, read out experiment results, analyse a test, or says "should we A/B test this", "experiment on the landing page", "is this lift real", "ship or kill this test", "what should we test next", "build an experiment backlog", "test the pricing page", "validate this with an experiment".

Plan, design, and analyze A/B tests and growth experiments, from hypothesis to statistically sound results.

36.3k · bundle

brainstorm-experiments-existing

Design low-effort experiments to test product assumptions for an existing product, including prototypes, A/B tests, spikes, and other validation methods.

More results

experiment-designer

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

20.4k · bundle

brainstorm-experiments-new

Design lean startup experiments (pretotypes) for a new product by creating XYZ hypotheses and suggesting low-effort validation methods like landing pages, explainer videos, and pre-orders.

experiment-backlog

Turn assumptions, funnel opportunities, and product questions into a prioritised, feasibility-checked experiment backlog. Filters by traffic reality, metric latency, and method feasibility — not just ICE/RICE scoring. Maintains a living portfolio with status (idea → designed → running → readout → archived). Load when the user says "what should we test next", "build an experiment backlog", "prioritise our tests", "where should we experiment", "what's worth testing", or when the experimentation orchestrator routes here.

Runs throwaway experiments to validate feasibility, compare approaches, and surface unknowns before committing to a real build.

prioritize-assumptions

Prioritize assumptions using an Impact × Risk matrix and suggest targeted experiments for each.

Runs focused experiments to validate feasibility of an idea, saving artifacts to .planning/spikes/ and supporting both idea and frontier modes.

monetization-strategy

Brainstorm 3-5 monetization strategies with audience fit, risks, and validation experiments for a product or feature.

gtm-0-to-1-launch

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

experiment-plan

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

brycewang-stanford

experiment-plan

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

opportunity-solution-tree

Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to opportunities, solutions, and experiments.

Replace heavy UX deliverables with hypothesis-driven design, collaborative sketching, and rapid experiments to learn what users actually need.

Guide cross-functional teams through creating Jeff Gothelf's Lean UX Canvas v2 to frame business problems, surface assumptions, and define experiments.

discovery-process

Guide product managers through a complete discovery cycle from problem hypothesis to validated solution using problem framing, customer interviews, synthesis, and experimentation.

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.

Access the RCSB Protein Data Bank (PDB) for EXPERIMENTALLY determined 3D structures (X-ray, cryo-EM, NMR) of proteins and nucleic acids — searching by text, sequence, or structure similarity and downloading coordinates in PDB/mmCIF format with metadata. Use when retrieving a structure by PDB ID, running sequence or structure similarity searches, or obtaining experimental coordinates for structural biology and drug discovery; for AI-PREDICTED structures of proteins lacking experimental data prefer alterlab-alphafold-db, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Guides setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

project-management

Coordinates sprint planning, delivery tracking, experiment management, knowledge management, and changelog governance across project management workflows.

a-b-test-design

Design rigorous A/B tests with clear hypotheses, controlled variants, appropriate metrics, and sample size calculations.

pricing-strategy

Design pricing strategies grounded in value delivery, competitive positioning, and willingness to pay. Recommends pricing models, tiers, and experiments.

alterlab-alphafold-db

Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Design MVPs, validated learning experiments, and pivot-or-persevere decisions using the Build-Measure-Learn loop and innovation accounting.

Creates a professional paid-advertising strategy covering objectives, economics, platform selection, campaign architecture, audiences, budget, creative, measurement, experiments, governance, rollout, and reporting.

idea-discovery-analysis

Orchestrate a full theory-first idea discovery pipeline. Use when the user wants the analysis counterpart of idea-discovery: literature context, proof-oriented idea generation, novelty checking, DeepSeek critique, and an analysis plan instead of an experiment-first workflow.

epic-hypothesis

Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.

user-onboarding

Design and improve product user onboarding (first-time user experience) to drive activation and early retention. Produces an Onboarding & Activation Pack (aha moment spec, first 30 seconds + first mile plan, onboarding journey map, experiment backlog, measurement plan). Use for Growth teams.

opportunity-solution-tree

Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.

Routes product requests to one of 12 bundled skills covering prioritization, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, and SaaS scaffolding.

product-analytics

Expert product analytics advisor for Senior PMs. Use when defining success metrics for a PRD, designing an A/B experiment, setting up an analytics tracking plan, analyzing post-launch impact, or when data exists but there's no clarity on what to measure. Produces structured metrics frameworks that connect to product decisions, not dashboards.

ml-training-recipes

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.