AI app for science and research · no coding needed
Methods transfer question generator
Show what a paper does not specify before attempted replication.
Made for: Research teams adopting published methods

What it does for you
The problem
Teams overlook practical details missing from a methods section.
What it gives you
Researcher-reviewed methods clarification brief
What you give it
Licensed papersinvestigator-defined transfer goals
How it works, step by step
- Extract reported parameters
- Link exact passages
- Flag unspecified conditions
- Compare local constraints
- Draft clarification questions
- Export transfer briefing
What you see on screen
- Method evidence
- Missing details
- Author questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Methods transfer question generator with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Methods transfer question generator with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links1 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria15 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
For research teams adopting published methods, turn licensed papers and investigator-defined transfer goals into researcher-reviewed methods clarification brief. Address this specific problem: teams overlook practical details missing from a methods section. The aim: show what a paper does not specify before attempted replication. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies licensed papers and investigator-defined transfer goals, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final researcher-reviewed methods clarification brief before use. Retain source links and a version history for the next cycle.
How the AI works
Identify reporting gaps without inventing experimental parameters. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. General evidence review; hazardous procedural optimization excluded. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
What to build first
Costed pilot: General evidence review; hazardous procedural optimization excluded. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract reported parameters; link exact passages. Support the third task through an assisted review queue: flag unspecified conditions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of researcher-reviewed methods clarification brief. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
What it can connect to
Authorized datasets, papers, protocols, code and research records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Begin with uploads and exports of licensed papers and investigator-defined transfer goals. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
The screens in detail
Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. Open with method evidence; move into missing details for the detailed task; finish in author questions for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.





