research-ops-skills
v2.9.04 Research-Operations skills + 1 orchestrator: clinical-research (study design: protocol synopsis, endpoint selection, sample-size/power, phase-gating, feasibility), research-finance (R&D program budgeting, burn/runway, F&A indirect-rate modeling, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM both-methods, survey/sampling design, segmentation, CI synthesis), product-research (interview/JTBD/usability/concept-test design, saturation, insight repository synthesis). Orchestrator skill uses context: fork. Each sub-skill ships per-skill onboarding (onboard.py), a customization loader (config_loader.py) consumed by every tool, and an isolated opt-in autoresearch evaluator (ar_evaluator.py) bridging to engineering/autoresearch-agent. 24 stdlib-only Python tools (12 analysis + 12 onboarding/customization/autoresearch), 12 reference docs. Distinct from ra-qm-team (regulatory/QM submission), finance (corporate close/valuation), research/grants (NIH funding discovery), product-team (persona/journey/live experiments), marketing-skill (campaign analytics).
by Alireza RezvaniMIT24.2kupdated 10 hours ago
Source
git clone https://github.com/alirezarezvani/claude-skillsClone the source, then follow the repository's marketplace instructions for your runtime. The plugin root is research-ops/ inside the repository.
Layout
├── .claude-plugin/plugin.json├── skills/clinical-research/SKILL.md├── skills/market-research/SKILL.md├── skills/product-research/SKILL.md├── skills/research-finance/SKILL.md└── skills/research-ops-skills/SKILL.md
Skills5
Use when designing a prospective clinical study before submission — selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging), estimating sample size and power for two-arm designs (means / proportions / survival), or scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named human owner (clinician / biostatistician / regulatory owner) — never clinical fact, never a finished protocol. Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study design.
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
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.
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
Manifests1
{
"name": "research-ops-skills",
"description": "4 Research-Operations skills + 1 orchestrator: clinical-research (study design: protocol synopsis, endpoint selection, sample-size/power, phase-gating, feasibility), research-finance (R&D program budgeting, burn/runway, F&A indirect-rate modeling, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM both-methods, survey/sampling design, segmentation, CI synthesis), product-research (interview/JTBD/usability/concept-test design, saturation, insight repository synthesis). Orchestrator skill uses context: fork. Each sub-skill ships per-skill onboarding (onboard.py), a customization loader (config_loader.py) consumed by every tool, and an isolated opt-in autoresearch evaluator (ar_evaluator.py) bridging to engineering/autoresearch-agent. 24 stdlib-only Python tools (12 analysis + 12 onboarding/customization/autoresearch), 12 reference docs. Distinct from ra-qm-team (regulatory/QM submission), finance (corporate close/valuation), research/grants (NIH funding discovery), product-team (persona/journey/live experiments), marketing-skill (campaign analytics).",
"version": "2.9.0",
"author": {
"name": "Alireza Rezvani",
"url": "https://alirezarezvani.com"
},
"homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/research-ops",
"repository": "https://github.com/alirezarezvani/claude-skills",
"license": "MIT",
"skills": [
"./skills/research-ops-skills",
"./skills/clinical-research",
"./skills/research-finance",
"./skills/market-research",
"./skills/product-research"
],
"source": {
"spec": "documentation/implementation/research-ops-expansion-plan.md",
"build_pattern": "Path B (direct conversion) — orchestrator skill uses context: fork. Single domain plugin (commercial/ + business-operations/ pattern), NOT the research/ parent-of-plugins pattern. Every SKILL.md ships a Forcing-question library section per Matt Pocock grill-with-docs discipline. Each sub-skill also ships onboard.py + config_loader.py (customization consumed by every tool, project>global>defaults precedence) and an isolated opt-in ar_evaluator.py that bridges to engineering/autoresearch-agent (loop edits the skill's input file; evaluator is locked ground truth).",
"distinct_from": "ra-qm-team (ISO 13485/14971, EU MDR, FDA 510(k)/PMA/De Novo/QSR submission — clinical-research designs the prospective study, not the submission). finance/financial-analysis (DCF, ratio analysis, close + report — research-finance manages internal R&D program spend). research/grants (NIH funding DISCOVERY + positioning — research-finance manages money already won). product-team/ux-researcher-designer + product-discovery + experiment-designer (persona/journey artifacts, discovery sprints, live A/B — product-research is method + repository discipline). marketing-skill/campaign-analytics + marketing-demand-acquisition (attribution/ROAS/demand-gen — market-research is upstream methodology)."
}
}