Results for “experimentation”

A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate between them. How to coordinate when multi-platform is genuinely warranted. The decisions that compound for years and the ones you can defer. Triggers on which experimentation platform, choose Statsig vs PostHog, evaluate experimentation tools, switch experimentation platform, migrate from Optimizely, consolidate experimentation tools, multi-platform experimentation, experimentation platform decision, ab test platform selection, feature flag platform vs experiment platform, warehouse-native experiments, vendor lock-in experimentation. Also triggers when a team is asking about cost, governance, or migration cost across experimentation tools, or when an evaluation is starting.

The English summary is being prepared. Showing the original description.

Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.

The English summary is being prepared. Showing the original description.

Plan A/B tests to compare page changes using clear hypotheses and meaningful measures. The skill helps define sample size, duration, traffic allocation, and result interpretation, and organize a continuing program of prioritized experiments.

• Compare headlines or signup buttons
• Estimate test sample size and duration
• Interpret results and possible adverse effects

Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

The English summary is being prepared. Showing the original description.

The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

The English summary is being prepared. Showing the original description.

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.

The English summary is being prepared. Showing the original description.

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.

The English summary is being prepared. Showing the original description.

Audit existing experimentation infrastructure and past experiments for methodology issues. Use when asked to "audit our experiments", "is our experimentation sound", or "review past test methodology".

The English summary is being prepared. Showing the original description.

The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

The English summary is being prepared. Showing the original description.

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Designs, runs, and reads A/B tests and growth experiments — hypothesis, sample size, duration, and honest interpretation. Use this to plan a test, judge whether a result is real, build an experimentation program, decide what to test next, or diagnose why tests keep producing inconclusive or non-replicating results.

The English summary is being prepared. Showing the original description.

Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.

The English summary is being prepared. Showing the original description.

Growth marketing covering experimentation, funnel optimization, acquisition channels, retention, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, or prioritizing channels by CAC and LTV.

The English summary is being prepared. Showing the original description.

Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.

The English summary is being prepared. Showing the original description.

World-class growth strategy expertise combining Andrew Chen's marketplace and network effects wisdom, Brian Balfour's growth frameworks, Casey Winters' Pinterest/Grubhub playbooks, and the best of Silicon Valley growth thinking. Growth is not marketing. Growth is the systematic application of product, engineering, and data to create compounding user acquisition, activation, and retention. It's a mindset, not a department. Use when "growth strategy, how do we grow, acquisition strategy, retention strategy, viral growth, network effects, growth loops, product-led growth, plg, ltv cac, unit economics, growth model, flywheel, compound growth, channel strategy, referral program, activation rate, magic moment, aha moment, growth experimentation, growth, strategy, acquisition, retention, viral, network-effects, plg, loops, experimentation" mentioned.

The English summary is being prepared. Showing the original description.

Expert en croissance data-driven (experimentation, metrics, dashboards, correlation analysis)

The English summary is being prepared. Showing the original description.

Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps

The English summary is being prepared. Showing the original description.

Data-driven growth v3 — experimentation, metrics, dashboards, correlation analysis

The English summary is being prepared. Showing the original description.

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".

The English summary is being prepared. Showing the original description.

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, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

The English summary is being prepared. Showing the original description.

Design recurring marketing workflows for SEO checks, ad reviews, and churn monitoring. Define when an agent checks, when it acts, what requires human approval, and when it stops, using reusable workflow templates.

• Set up a weekly SEO review
• Schedule recurring ad fatigue checks
• Monitor customer churn signals

Plan A/B tests to compare page or messaging changes. Helps define hypotheses, choose success metrics, estimate sample sizes, allocate traffic, and interpret results, while also organizing a repeatable process for prioritizing and documenting experiments.

• Compare headline or button-copy variants
• Plan sample sizes and test duration around available traffic
• Analyze results alongside metrics that must not worsen

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.

• Choose model architectures for the available data type and scale
• Configure optimizers and learning-rate schedules
• Investigate loss spikes, NaNs, and GPU memory shortages