Before opening an editor, decide what the plugin is meant to help someone finish. “Make our AI better” is hard to test. “Help a staff member prepare an enquiry summary from the details they provide, flag missing information, and draft questions” gives you something concrete to build and review.
Then choose the kind of package. A skills-based plugin usually needs a manifest that identifies it, instruction files that describe when and how to apply each skill, and any reference material the instructions rely on. It may also include example prompts and documentation. That package can guide a model, but it does not create access to company software. If the job needs fresh records or an operation in another system, plan a separate tool connection, such as an MCP server, and specify its permissions.
Build from a real example
Gather a few representative requests, including an incomplete one and an awkward edge case. Write the expected approach in plain language: what to ask, what to treat as confirmed, when to stop, and what a person must approve. Use approved examples and remove personal or confidential data unless you have a proper reason and safeguards for using it.
Keep each skill focused. One file can help diagnose a business process; another can review how customers find and contact the business. Shared reference files can hold approved service facts so those facts do not drift across copies. Include instructions for uncertainty. If the material does not say whether a service covers a particular case, the assistant should ask rather than invent a confident answer.
Package the files exactly as the target client expects, then test the actual archive or installation. Try a normal request, missing context, contradictory details, and a request outside scope. Check whether the assistant follows the intended process, avoids claiming access it does not have, and leaves decisions with the right person. These tests are more revealing than a polished demo with a hand-picked prompt.
Distribution is a separate job
Some teams only need a private package shared with colleagues. A public listing may ask for metadata, icons, support and policy pages, validation, or platform review. Requirements and client interfaces can change, and they vary across products. Read the current instructions for the specific destination before promising a launch date. A package being valid on your machine does not mean it has been approved or published.
For a connected version, add the MCP server only after there is a clear reason. Document the data it can see, the actions it can request, and where confirmation happens. Start read-only if that meets the need. Our article on building MCP from an existing repository discusses keeping that scope close to the work already in place.
Brownsmith Dynamics can help scope and implement a focused AI workflow. If you have a draft process, share it on WhatsApp or the contact page. The useful starting materials are a sample task, the current instructions people follow, and the software involved.