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Found 106 Skills
When the user wants to design, prioritize, or analyze growth experiments -- including A/B tests, hypothesis frameworks, ICE/RICE scoring, or growth sprints. Also use when the user says "A/B test," "experiment design," "growth sprint," "experiment prioritization," or "statistical significance." For analytics setup, see product-analytics. For growth modeling, see growth-modeling.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Scientific method expert for systematic bug investigation and root cause analysis. Use when users report bugs, crashes, unexpected behavior, or debugging requests. Applies hypothesis-driven investigation, controlled experiments, and rigorous validation across any programming language or platform.
Apply the Efficient Market Hypothesis (Fama, 1970) to evaluate information incorporation in asset prices across weak, semi-strong, and strong forms. Use this skill when the user needs to assess market efficiency, determine if a trading strategy can generate abnormal returns, evaluate event studies, or when they ask 'can technical analysis work', 'does the market already know this', or 'is this anomaly exploitable'.
Dynamic, reflective problem-solving through structured sequential thoughts with support for branching, revision, and adaptive depth. Use this skill when: (1) Breaking down complex problems into steps, (2) Planning and design with room for revision, (3) Analysis that might need course correction, (4) Problems where the full scope is not clear initially, (5) Multi-step solutions requiring maintained context, (6) Situations where irrelevant information must be filtered out, (7) Any task benefiting from hypothesis generation, verification, and iterative refinement. Triggers: think through, step by step, break this down, sequential thinking, reason through, analyze step by step, think carefully, or when a problem clearly benefits from structured multi-step reasoning.
Root-cause discipline for bugs, test failures, and unexpected behavior. Embedded grill on the hypothesis before writing fix code. Use when encountering any bug, failing test, or behavior that doesn't match expectation.
Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.
Use when diagnosing unexpected behavior, failed workflows, bugs, browser or Node.js runtime issues, logs, traces, or when preparing a root-cause hypothesis. 诊断异常、定位 bug、判断修复方向时使用：先建立证据表，区分运行时事实和代码推断，避免多层猜测；证据不足时添加 copy-friendly 浏览器日志或本地 Node.js JSONL 日志。
Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics. TRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create. DO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, "just one quick fix" seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.