Loading...
Loading...
Found 8 Skills
Apply behavioral science to product design and produce a Behavioral Product Design Pack (target behavior, behavioral diagnosis, intervention map, prioritized concepts, design specs, experiment + instrumentation plan, ethics/trust review). Use for retention, onboarding, habit loops, and behavior change problems.
Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.
A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.
Template system for building quarterly social channel roadmaps with KPIs and experiments.
Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.
Turn a promising ML/AI research idea into a precise algorithm or method design before implementation. Use this skill whenever the user has an idea or project direction and wants to design the actual method, objective, architecture, inference procedure, assumptions, failure modes, ablations, implementation handoff, or method section plan before coding or experiment design.
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Structure complex questions into testable hypotheses. Use when validating product ideas, debugging problems, planning experiments, or breaking down ambiguous challenges into actionable research.