research-ops-skills
by alirezarezvaniresearch-ops-skills is a lightweight Research Operations orchestrator for routing clinical research planning, R&D finance, market research, and product/user research requests. Use it to classify workstreams, gather missing inputs, and produce executive-style digests from a single SKILL.md.
This skill scores 76/100, which makes it a solid listing candidate for directory users who need a research-operations routing layer across clinical, finance, market, and product research tasks. It appears easy for an agent to trigger and understand, with enough workflow substance to reduce guesswork, but users should note that the listed directory evidence shows only a single SKILL.md and no install/readme/support assets, so adoption depends on how the surrounding sub-skills are available in the repository.
- Highly triggerable frontmatter: the description lists concrete user intents such as clinical study design, sample size, R&D budget, TAM/SAM/SOM, survey design, interviews, and usability tests.
- Clear scope positioning: it explains that the skill plans, funds, scopes, and synthesizes research across clinical R&D, R&D finance, market research, and product research, and distinguishes itself from adjacent skills.
- Substantial SKILL.md content: the body is nearly 10k characters with multiple headings, workflows, constraints, and code fences, suggesting more operational guidance than a generic prompt.
- It is an orchestrator that routes to four named sub-skills, but the provided skill path shows only SKILL.md and no bundled support files, so users may need the surrounding repository structure for full value.
- No README, install command, scripts, references, or resources are present in the skill directory, which limits independent install confidence.
Overview of research-ops-skills skill
What research-ops-skills is for
research-ops-skills is a Research Operations orchestrator skill for routing broad research requests into the right operational lane: clinical research planning, R&D finance, market research, or product/user research. It is best for teams that need an AI assistant to classify a research problem, ask for the missing planning inputs, and return an executive-style digest instead of treating every request as a generic research prompt.
Best-fit users and jobs
Use the research-ops-skills skill when you are planning a study, scoping a research program, estimating market opportunity, designing surveys or interviews, reviewing R&D budget logic, or synthesizing findings across multiple workstreams. It is especially useful for research operations leads, product researchers, clinical program teams, innovation finance partners, and founders who need structured research planning without switching between unrelated prompt templates.
What makes this skill different
The key differentiator is routing. The skill is designed to fork context and choose among four Research Operations subdomains rather than answer everything in one flat response. That matters when the same phrase, such as “sample size” or “research budget,” could mean a clinical trial design issue, a survey methodology issue, or a finance planning issue. The skill also distinguishes itself from adjacent skills: it is not a regulatory submission assistant, corporate finance close tool, grant discovery workflow, campaign analytics prompt, or live experimentation planner.
Fit and adoption cautions
The repository evidence shows a single SKILL.md file and no bundled scripts, references, or resource folders. That makes research-ops-skills lightweight to inspect and easy to adapt, but it also means users should not expect built-in calculators, validated clinical templates, statistical engines, or source libraries. Treat it as an orchestration and reasoning skill, then supply your own protocols, assumptions, transcripts, ledgers, survey exports, or market data when precision matters.
How to Use research-ops-skills skill
research-ops-skills install and repository path
For Claude Code-style skill workflows, install or copy the skill from the repository path:
research-ops/skills/research-ops-skills
Repository URL:
https://github.com/alirezarezvani/claude-skills/tree/main/research-ops/skills/research-ops-skills
If your skill manager supports GitHub installation, point it at the repo and skill path. If it does not, copy the SKILL.md into your local skills directory following your tool’s expected structure. The skill frontmatter lists compatibility with claude-code, codex-cli, cursor, antigravity, opencode, and gemini-cli, but actual loading behavior depends on your local agent framework.
Read this file first
Start with SKILL.md; it appears to be the primary and only shipped source file for this skill. Look for the trigger phrases, routing logic, signal table, workflow guidance, and distinctions from neighboring domains. Because there are no visible rules/, references/, resources/, or scripts/ folders in the evidence, your install decision should be based on whether the orchestration instructions are enough for your workflow, not on external assets.
Turn a rough goal into a usable prompt
A weak prompt is: “Help with market research.” A stronger research-ops-skills usage prompt gives the skill enough information to route correctly:
“Use research-ops-skills for Research Operations. We are evaluating a new B2B analytics product for mid-market hospitals. Route this request to the right research workstream. We need TAM/SAM/SOM assumptions, a 12-question buyer survey, interview targets, and a synthesis format for leadership. Constraints: 3-week timeline, US market only, no primary clinical claims, budget under $15k.”
For clinical work, include population, intervention, comparator, outcome, endpoints, feasibility limits, and regulatory sensitivity. For finance work, include program stage, budget categories, burn rate, capitalization policy questions, and decision deadline. For product research, include users, product surface, research question, recruiting constraints, and expected deliverable.
Suggested workflow for better outputs
Use the skill in two passes. First, ask it to classify the request and list missing inputs before producing the plan. Second, provide the missing details and request the final artifact: study outline, research budget digest, market sizing model, survey plan, interview guide, or synthesis memo. This reduces misrouting and prevents the model from inventing assumptions too early. For high-stakes clinical, financial, or legal decisions, use the output as a planning draft and validate it with qualified domain owners.
research-ops-skills skill FAQ
Is research-ops-skills only for clinical research?
No. Clinical study design is one of the routed workstreams, but the skill also covers R&D program finance, market research, and product/user research. Its value is highest when your request could plausibly touch more than one research operations area and you need the assistant to choose a lane.
How is this better than an ordinary prompt?
An ordinary prompt may answer the visible question directly. The research-ops-skills skill adds a routing layer: it checks whether the request sounds like clinical research, research finance, market sizing/survey work, or product research, then shapes the response accordingly. That usually produces cleaner intake questions and more relevant deliverables.
When should I not use this skill?
Do not use it as the primary tool for regulatory submissions, quality management systems, accounting close, valuation, grant search, campaign performance analytics, or production A/B test operations. It may help frame research questions in those areas, but the repository description explicitly separates it from those neighboring skill families.
Is the skill beginner-friendly?
Yes, if the user can describe the research goal and constraints. Beginners should ask for routing and intake questions first. Advanced users can go faster by providing their workstream, assumptions, source material, and desired deliverable format up front.
How to Improve research-ops-skills skill
Improve research-ops-skills inputs before expecting precision
The fastest improvement is better intake. Provide the decision being supported, audience, timeline, geography, budget, available data, risk level, and what “done” looks like. For example, “prepare a board-ready market sizing memo” and “draft a screener for 10 usability interviews” require different evidence, structure, and confidence language.
Watch for common failure modes
The main failure mode is ambiguous routing. “What sample size do we need?” could mean a clinical endpoint calculation, a customer survey target, or usability test coverage. “Research budget” could mean R&D accounting, recruiting incentives, tooling, or study operations. If the first answer feels generic, ask the skill to state which sub-skill it selected and why.
Iterate with artifacts, not just opinions
After the first output, feed back concrete materials: protocol notes, ledger categories, market assumptions, survey drafts, interview transcripts, usability findings, or stakeholder comments. Then ask for a revised digest with changes tracked as assumptions, risks, open questions, and recommended next steps. This makes research-ops-skills more useful for real Research Operations work than a one-shot brainstorm.
Add local standards if you maintain a fork
If your team forks the skill, add lightweight references that reflect your operating model: study intake forms, survey quality rules, approved budget categories, synthesis memo templates, or review checklists. Keep the routing logic clear and avoid overloading the skill with unrelated regulatory, finance, marketing, or product experimentation duties; its strength is focused Research Operations orchestration.