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Found 134 Skills
Turns ideas into working prototypes and MVPs with the smallest useful scope. Use when validating a core hypothesis, shipping a demo-ready flow, or choosing a practical stack for fast learning.
The combined phase of mathematical modeling competition problem analysis and modeling design. It is used to read the problem statement and attachments, and complete sub-problem decomposition, data understanding, hypothesis pre-check, variable definition, model formulas, objective functions, constraint conditions, solution strategies, and a modeling report that can be handed over for code implementation.
Validate system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.
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.
Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
Guide product managers through Jeff Gothelf's Lean UX Canvas v2—a one-page tool that frames work around a business problem, exposes assumptions, and ensures learning every sprint.
Statistics, probability, linear algebra, and mathematical foundations for data science
Comprehensive protocol for validating root causes of software issues. Use when you need to systematically debug a complex bug, flaky test, or unknown system behavior by forming hypotheses and validating them with specific tasks.
Use when theoretical calculations need experimental validation, protocols must be designed with clear hypotheses and success criteria, or resource requirements (equipment, materials, expertise) must be estimated for proposed experiments
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
Define a Proof of Life (PoL) probe—a lightweight validation artifact that surfaces harsh truths before expensive development. Use it to test hypotheses with minimal investment.