product-research
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),
Profile →npx skills add alirezarezvani/claude-skills --skill product-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 candidat
- Purpose
- When to use
- Workflow
- Scripts
- Onboarding & customization
- Optimize with autoresearch (opt-in)
- References
- Assumptions
- Anti-patterns
- Distinct from
- Quick examples
- Forcing-question library (Matt Pocock grill discipline)
python3 scripts/onboard.py # interactive (also: --defaults, --set key=value, --reset) python3 scripts/onboard.py --show # see the questions + current effective config python3 scripts/study_designer.py --sample python3 scripts/saturation_planner.py --method thematic --segments 3 python3 scripts/insight_synthesizer.py --sample --min-sources 3
What does the product-research skill do?
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),
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill product-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.