Plexara
Working with assets and collections
301 - Creating reports and dashboards
AI chat tools produce excellent dashboards and reports in HTML, JSX, and SVG, formats that do not move easily through normal business workflows. Plexara gives them a home in the portal under Assets: your team's catalog of AI-built work, shared like Google Docs, stored in your S3, editable in place, and discoverable by future agent sessions. This article is the working playbook, including the two prompting habits that make the agent produce a saved asset efficiently.
302 - Exporting data
When a teammate asks for the data instead of the dashboard: a spreadsheet to pivot, a JSON to feed another system, a markdown table for a wiki. Plexara has a dedicated path for this called Trino Export. It runs the query, writes the file straight to your S3 bucket, and never puts the rows in your chat. This article is the analyst's playbook.
303 - Sharing your work
Sharing in Plexara sends real mail. Name a colleague and they get an email carrying your note and a link that opens the work; name somebody with no Plexara account and they can still read it, through a single-use link sent to the address you named. This lesson is the playbook for getting a dashboard in front of the right person and knowing what happened to it after you clicked Share.
304 - Creating collections
A board briefing is rarely one dashboard. It's a dashboard plus a summary plus the underlying data, opened from a single link in the order you chose. Plexara calls that packaging unit a collection. You can ask the agent to assemble one during the same session that produced the assets, or build one by hand on the Collections page. This lesson covers both.
305 - Editing what you already have
When the dashboard you saved last week is mostly right but needs a fix, you do not re-create it from scratch. You edit the existing asset in place. The link the recipient already has keeps working, the version history accumulates on one asset instead of fragmenting across copies, and any collection that references it picks up the change. This lesson covers the three kinds of edit (metadata, content, revert), what each one does to the version history, and the portal vs agent path.
306 - How an asset was built
Every asset in Plexara carries two kinds of metadata: descriptive fields the agent fills when it saves the asset (name, description, tags) and that you can edit later, and provenance the platform records on its own. Provenance is the audit trail Plexara captures at the MCP boundary: the catalog searches and queries the agent invoked, with what parameters, in the producing session. This lesson opens that record, names what it can and cannot tell you, and shows how to use it to answer the questions stakeholders ask about a number.
307 - Turning a comment into something the agent remembers
A reviewer writes "we don't use that term" on your dashboard. In most tools that comment stays a comment, and the same correction gets made again next quarter. In Plexara an agent can fold it into the knowledge loop: memory_capture with thread_ids records the lesson as a pending insight, resolves the thread, and routes it to the review queue that produces knowledge pages and catalog changes. The person who raised it then confirms or disputes the resolution. This lesson covers the whole loop, the notification rules, the access rules, and how a reviewer with no account participates through a public link.
308 - Reproducible prompts
Plexara has a first-class prompt object: a saved instruction template with named arguments that you (or a teammate) can re-run later with different values. Manage Prompts (manage_prompt) is the tool. This article covers what a prompt record actually is, how arguments substitute at run time, which scope to pick (personal, persona, global), the four built-in workflow prompts, and the honest limits of what re-running a prompt does and does not guarantee.