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From research-ops-skills
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.
npx claudepluginhub kruxshnx/claude-skills-devin --plugin research-ops-skillsHow this command is triggered — by the user, by Claude, or both
Slash command
/research-ops-skills:cs-product-research <research context: goal, product stage, segments, coded observations>The summary Claude sees in its command listing — used to decide when to auto-load this command
# /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 cover...
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Run the product-research skill on this input:
$ARGUMENTS
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.
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.
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.
Method must match the goal, and an insight requires recurrence across independent participants. A single quote is an anecdote, not a finding.
python3 scripts/onboard.py (product profile, insight source-threshold, saturation method, high-stakes flag) — saved config pre-configures every tool. --show lists the questions.scripts/ar_evaluator.py (validated_insights, higher is better).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.