product-research

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: product-research
description: Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.
version: 2.9.0
author: claude-code-skills
license: MIT
tags: [research-ops, product-research, ux-research, jtbd, usability, saturation, insight-synthesis, research-repository]
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# 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. `study_designer.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. `saturation_planner.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. `insight_synthesizer.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.

**Do NOT use this skill to**: generate personas / journey maps (use `product-team/ux-researcher-designer`), plan a discovery sprint or validate an opportunity (use `product-team/product-discovery`), design or analyze a live product A/B experiment (use `product-team/experiment-designer`), or do market sizing / surveys (use the `market-research` sibling).

## Workflow

1. **Frame the study** — Fill `assets/research_plan_template.md` (research questions, method rationale, participant criteria, analysis plan, repository tagging scheme).
2. **Pick the method** — Run `study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}`. Honor the redirect if it routes to experiment-designer.
3. **Size it** — Run `saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N`. Record the confidence label and limits.
4. **Synthesize** — After fielding, code observations and run `insight_synthesizer.py --input observations.json --min-sources 3`. Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship.
5. **File in the repository** — Tag insights to the atomic schema at synthesis time, with their evidence and confidence.

## Sc