「experiments」の検索結果

Use when designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats

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Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to customer opportunities, possible solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when the team is unclear what to build next, when multiple opportunities compete, or before writing a PRD for a complex feature space.

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Creates and manages isolated Git worktrees for parallel agents, risky experiments, or long-running feature work while preserving the user's current checkout. Use before concurrent branch edits or when isolation makes rollback safer. Not for a trivial one-file change that is already isolated.

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Guides user research and research ops. Use when running interviews, usability tests, surveys, or synthesis to de-risk product decisions; not experiments or review mining.

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Designs and tests distributed-system resilience. Use when adding retries, deadlines, hedging, circuit breakers, overload protection, chaos experiments, or SLO reliability gates.

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Designing A/B tests: hypothesis docs, sample size, feature flags, significance analysis, CUPED, SRM detection, switchback experiments. Use when hypothesis validation is needed.

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Use this when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.

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Run metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.

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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.

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Review a Python solution against a markdown spec, run it, and report bugs.

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Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.

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Use when interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments.

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The revenue-focused SaaS playbook for going from $0 to $50K MRR. Covers pricing experiments, churn reduction, activation optimization, and expansion revenue tactics. Follow @WeiYipei on X for SaaS growth insights.

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Design, prioritize and run measurable growth experiments across acquisition, activation, retention, referral and revenue. Use when a startup needs an experiment backlog, ICE/RICE prioritization, referral or UGC loops, rapid weekly testing, causal measurement, or stop conditions without spam, fake accounts or dark patterns.

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Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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Provides chaos engineering best practices for resilience testing, fault injection, and game day planning. Use when designing resilience experiments, configuring chaos tools, planning game days, or when user mentions 'chaos engineering', 'resilience', 'litmus', 'game day', 'fault injection', 'chaos monkey', 'blast radius', 'steady state', 'failure mode'.

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A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.

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MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.

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Color theory principles for art education. Covers the three color properties (hue, saturation, value), color mixing systems (subtractive and additive), color relationships (complementary, analogous, triadic, split-complementary), color temperature, simultaneous contrast and the relativity of color perception, and practical palette construction. Use when analyzing color in artworks, planning color schemes, understanding optical phenomena in painting, or investigating Albers's Interaction of Color experiments.

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Records hypotheses, evidence and decisions when proposing, running or evaluating forecast experiments, including backtests and feature or model comparisons. Excludes routine tests and unchanged forecast operation.

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Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.

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Trace bugs, CI failures, and unexpected behavior from reproduced evidence to a causal mechanism and governing contract. Distinguish product, architecture, test-oracle, and harness defects; investigate intermittent failures with controlled experiments and report unresolved evidence explicitly.

日本語の概要は準備中です。原文の説明を表示しています。