# Research Ops

> Build research operations that improve quality, speed, and ethics through recruiting systems, repositories, templates, and governance. Use when scaling research practice across teams or reducing operational friction in studies.

- Skill: `itsual/research-ops` (Agent Skill)
- Install (CLI): `npx skillmds@latest add itsual/research-ops`
- Raw SKILL.md: https://api.skillmd.com/api/skills/itsual/research-ops/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: itsual (https://skillmd.com/u/itsual)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/itsual/research-ops

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# Research Ops

## Overview

Research Ops (ResearchOps) enables researchers to do more rigorous work with less overhead — participant management, knowledge management, tools, and standards.

## When to Use

- Scaling a research practice beyond a few people
- Fixing recruiting bottlenecks
- Building insight repositories
- Standardizing ethics, consent, and incentives

## Core Practices

- Create participant panels and fair recruiting processes
- Maintain templates for plans, guides, and consent
- Build searchable repositories for past studies
- Define tooling stack and access
- Set quality and ethics guidelines lightly but clearly
- Measure ops health (time-to-recruit, reuse rates)

## Principles

- Ops should reduce friction, not add bureaucracy
- Knowledge reuse multiplies research ROI
- Participant experience is part of brand and ethics
- Central standards + team autonomy is the balance

## Verification

- [ ] Researchers can find and reuse prior work
- [ ] Recruiting time is measured and improving
- [ ] Consent and incentive processes are consistent