Who it is forA buyer who is about to purchase from an unfamiliar merchant and wants to know whether the seller is who they claim to be and how trustworthy the seller's history looks.
The problemPolished seller pages and copied listings can hide who actually sells an item, and unknown seller history is easy to mistake for a safe record.
The outcomeA research checklist followed for the exact seller, with conflicts and unknowns listed, and a result that stays provisional until you check it yourself.
What you need first
- Node.js 20 or later
- An MCP-capable AI app
- Store connections you choose to configure, if you want searches beyond the built-in setup
Steps
- Run
npx northcinder initif you have not already set up NorthCinder in your AI app. - Ask your AI app to read the resource
northcinder://research/seller. - Ask the app to call
create_research_planwith your actual request and the exact seller name. - Have the app follow the returned checklist using the research tools it already controls.
- Review the sources, conflicts, and unknowns yourself before deciding whether to buy.
Prompt for your agent
Using the NorthCinder MCP server, read northcinder://research/seller, then call create_research_plan for the seller <seller name> and the request <request>. Return the checklist results, source conflicts, and unknown seller history, and mark the result as provisional. Verify that the seller identity matches the exact merchant named before treating any finding as usable.
Caveat. The README says no host and model combination is currently qualified for routine research use, so every seller research result must be checked by the buyer before relying on it.
Get 5 new use cases every morning. Free, one short email, unsubscribe anytime.
Get them freeShare: X LinkedIn Hacker News Email Copy link
Disclaimer
Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page