| 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:
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.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.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
- Frame the study — Fill
assets/research_plan_template.md (research questions, method rationale, participant criteria, analysis plan, repository tagging scheme).
- 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.
- Size it — Run
saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N. Record the confidence label and limits.
- 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.
- File in the repository — Tag insights to the atomic schema at synthesis time, with their evidence and confidence.
Scripts
| Script | Purpose | Profiles |
|---|
scripts/study_designer.py | (goal × stage) → method + plan skeleton | b2b-saas, consumer-app, enterprise, marketplace, hardware, platform |
scripts/saturation_planner.py | Method-based sample guidance + confidence | n/a (method-driven) |
scripts/insight_synthesizer.py | Cluster observations, flag anecdotes | n/a (evidence-driven) |
All three: stdlib-only, --help, --sample, --output {human,json}.
Onboarding & customization
Run the onboarding questionnaire once before you start — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior (e.g. the insight source-threshold).
python3 scripts/onboard.py
python3 scripts/onboard.py --show
Answers are saved to ~/.config/research-ops/product-research.json (global) or ./.research-ops/product-research.json (--scope project) and are read automatically by config_loader.py. They set the default product profile, the insight source-threshold (how many independent participants make a finding an insight, not an anecdote), the default saturation method, and the high-stakes flag. CLI flags always override saved config; RESEARCH_OPS_NO_CONFIG=1 ignores it.
The four questions: product profile · insight source-threshold · saturation method · high-stakes flag.
Optimize with autoresearch (opt-in)
This skill ships an isolated, opt-in bridge to engineering/autoresearch-agent. Only when you ask to "optimize the synthesis" / "run a loop" does an autoresearch experiment iteratively refine the coding/clustering of a fixed evidence set so more cross-participant patterns surface. scripts/ar_evaluator.py is the ground-truth evaluator; it prints validated_insights: <int> (higher is better). It optimizes the coding, never fabricates evidence.
/ar:setup --domain custom --name insight-synthesis \
--target observations.json \
--eval "python3 ar_evaluator.py --target observations.json" \
--metric validated_insights --direction higher
/ar:loop custom/insight-synthesis
Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits observations.json, never the evaluator.
References
references/research_methods_canon.md — Portigal Interviewing Users; Christensen/Ulwick JTBD; Rohrer's UX-research methods landscape (NN/g); Sauro & Lewis Quantifying the User Experience; Goodman/Kuniavsky.references/sampling_and_saturation.md — Nielsen "test with 5 users"; Guest, Bunce & Johnson saturation; Faulkner on more-than-5; Sauro usability sample size; Braun & Clarke thematic analysis.references/repository_and_synthesis.md — ResearchOps / atomic research (Tomer Sharon "Polaris"); insight-vs-observation discipline; repository governance; affinity mapping; democratization guardrails.
Assumptions
- Method selection assumes you can name the goal honestly; if the goal is fuzzy, grill it first (the goal drives everything).
- Saturation guidance is method-based, not a power calculation — usability tests find problems, not prevalence rates.
- The synthesizer counts evidence you provide; coding quality is upstream of it. Garbage tags → garbage clusters.
- The insight threshold (
--min-sources) defaults to 3; raise it for high-stakes or heterogeneous populations.
Anti-patterns
- Mismatching method to goal. A usability test cannot discover unmet needs; an interview cannot measure task success.
- Reporting usability problems as percentages. Small-n tests surface problems, not population rates.
- Promoting an anecdote to an insight. One participant is a signal to probe, not a finding.
- Framing interview questions as feature reactions. Probe the job-to-be-done and recent real behavior, not hypothetical opinions.
- Synthesizing without a repository scheme. Tag at synthesis time, or insights rot unfindable.
Distinct from
| Neighbor | Scope | Difference |
|---|
product-team/ux-researcher-designer | Personas, journey maps, usability frameworks tied to design output | That produces artifacts; this is method + repository discipline |
product-team/product-discovery | Opportunity validation, discovery-sprint planning | That plans discovery sprints; this designs and synthesizes the research |
product-team/experiment-designer | Live product A/B hypothesis + sample size | That runs live experiments; this runs qualitative/evaluative research |
market-research (sibling) | Market sizing, surveys, segmentation | That studies the market; this studies users |
Quick examples
python3 scripts/study_designer.py --sample
python3 scripts/saturation_planner.py --method thematic --segments 3
python3 scripts/insight_synthesizer.py --sample --min-sources 3
The synthesizer sample correctly promotes "import-confusion" (3 independent participants) to INSIGHT and flags "wants-slack" (1 participant) as an ANECDOTE.
Forcing-question library (Matt Pocock grill discipline)
Walked one at a time by /cs:grill-research-ops or the orchestrator. Recommended answer + canon citation per question. Never bundled.
-
"Is this study generative (discover problems) or evaluative (test a solution)?"
Recommended: name it first — the method follows from the goal.
Canon: Rohrer, When to Use Which User-Experience Research Methods (NN/g).
-
"What's your sample size and saturation rationale — and at what confidence?"
Recommended: method-based n (5/segment usability; ~12 for thematic saturation), state the confidence.
Canon: Nielsen; Guest, Bunce & Johnson (2006); Faulkner (2003).
-
"How many independent participants support each insight — or is it a single-source anecdote?"
Recommended: require recurrence across ≥3 sources before calling it an insight; flag singletons.
Canon: atomic research / ResearchOps; Braun & Clarke thematic analysis.
-
"Are your interview / usability tasks framed as outcomes (jobs) or as feature reactions?"
Recommended: frame around the job-to-be-done and recent real behavior, not hypothetical opinion.
Canon: Christensen/Ulwick Jobs-to-be-Done; Portigal Interviewing Users.
-
"Where does this land in the repository, and how is it tagged for reuse?"
Recommended: tag to the atomic schema at synthesis time, not later.
Canon: Tomer Sharon, Polaris / ResearchOps repository practice.
Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke study_designer.py → saturation_planner.py → (after fielding) insight_synthesizer.py.
