Agent skill · research science · alirezarezvani
product-research
Use when planning or synthesizing user research: choosing the method, deciding how many participants, or turning coded notes into insights. Runs study_designer.py, saturation_planner.py and insight_synthesizer.py, which refuses single-source insights.
Why this skill is useful
Adds three executable scripts for study design, saturation planning, and insight synthesis that are not commonly found in public resources.
What it needs
About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
product-research Product / user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: method must match the goal, and an insight requires recurrence across independent participants — a single quote is an anecdote. Purpose Product researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions: Three deterministic tools: 1. studydesigner.py — Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A/B to product-team/experiment-designer. 2. saturationplanner.py — Method-based sample guidance with an explicit confidence label: Nielsen problem-discovery (5/segment), Guest et al. thematic saturation (~12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test. 3. insightsynthesizer.py — Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an ANECDOTE, never promoting it to an insight. When to use Invoke this skill when: You are planning a study and need the method to match the goal (generative vs evaluative vs validation). You need a defensible sample size / saturation rationale with a stated confidence. You have raw coded observations and need to synthesize insights without over-claiming. You are setting up or auditing a research repository and need the insight-vs-observation discipline. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/product-research--9699b3