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description: Product / user research methodology. Select the right method for the goal (generative vs evaluative vs validation), compute method-based saturation / sample size with an explicit confidence level, and synthesize coded observations into insights while flagging single-source anecdotes. Never fabricates insight. Direct invocation of the product-research skill. argument-hint: "<research context: goal, product stage, segments, coded observations>"

/cs:product-research — Study design + saturation + insight synthesis

Run the product-research skill on this input:

$ARGUMENTS

Three-tool workflow

1. study_designer.py — Map (research goal × product stage) to an appropriate method and emit a 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), evaluative coverage. Never claims a prevalence rate from a small-n usability test.

3. insight_synthesizer.py — Cluster coded observations by tag, count distinct participants, rank by cross-participant recurrence, and flag any candidate below the source threshold as an ANECDOTE — never promoting it to an insight.

Output

• Recommended method + plan skeleton (matched to the goal)
• Sample / saturation plan with confidence + limits
• Synthesized candidates: INSIGHT vs ANECDOTE with evidence
• Top 3 next actions

Hard rule

Method must match the goal, and an insight requires recurrence across independent participants. A single quote is an anecdote, not a finding.

First run + optimization

• Onboard first: python3 skills/product-research/scripts/onboard.py (product profile, insight source-threshold, saturation method, high-stakes flag) — saved config pre-configures every tool. --show lists the questions.
• Optimize (opt-in): only if the user asks to optimize the synthesis/run a loop, hand off to autoresearch via skills/product-research/scripts/ar_evaluator.py (validated_insights, higher is better).

Distinct from

• product-team/ux-researcher-designer — that produces personas/journey artifacts. This is method + repository discipline.
• product-team/product-discovery — that plans discovery sprints. This designs and synthesizes the research.
• product-team/experiment-designer — that runs live A/B. This runs qualitative/evaluative research.
• market-research (sibling) — that studies the market. This studies users.