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AI Workbench3 illustrations

Reusable Prompts and Managed Contexts

Turn the instructions and reference material your agents need into named, tagged, shareable objects any session can pull in.

These images are illustrations of the concept, not screenshots of the actual product.

Overview

Teams that work with coding agents re-type the same instructions constantly, and paste the same background material into every new session. The wording drifts, the reference files go stale, and the one version that actually worked lives in somebody's scratch file. Reusable Prompts and Managed Contexts is the VibeControls concept for treating both halves as first-class objects: a prompt is a saved, tagged, parameterized thing, and a context is a registered source with a type, an owner, a visibility setting and a preview of what it contains.

The first illustration shows the prompt side. A compose panel builds a request from a saved template — in the sample data, a security review of a payments service — and surfaces the category and tags that classify it. Variables the template declares are pulled out as editable inputs, here a service name and a release branch, so the same prompt can be aimed at different work without being rewritten. A context attachment list sits beside it with checkboxes and type labels, letting the author decide exactly what reference material travels with the request. A target-session selector and a send control dispatch the finished prompt into a running agent session, and a dispatch history below records each send with a status badge, a session reference and a relative timestamp.

The second illustration is the context library itself: cards for each registered source, each showing a name, a type badge for markdown documents, repositories, plain text or links, a short preview of the content, tag chips and a signed marker on one entry. Edit, open, share and delete actions sit on every card, and a selection bar across the top reports how many items are selected and offers a clearly marked delete for the whole selection.

The third illustration shows the same library as a table — name, type, associated project, visibility, tags and last update — with the full AI tab bar above it running from overview and templates through contexts, prompts, instructions, sessions, queue, tasks, stats, playground and config. A right rail titled for inserting into chat lists prompt cards with a category badge, a usage count and an insert action, plus a variable field to fill before the prompt goes in.

The effect is a shared, searchable layer of working knowledge that sits under every agent session, rather than being retyped into each one.

What this concept shows

  • A compose panel that builds a prompt from a saved template and shows its category and tags
  • Declared variables extracted into editable inputs, such as a service name and a branch
  • A context attachment list with checkboxes and type labels for documents, plain text, links and repositories
  • A target-session selector that dispatches a finished prompt straight into a running agent session
  • A dispatch history with status badges such as completed and queued, session references and relative times
  • Context cards with content previews, tag chips, a signed marker and edit, open, share and delete actions
  • Bulk selection with a live selected count and a clearly marked destructive delete
  • A table view of contexts by type, project, visibility, tags and update time, beside an insert-into-chat rail of prompt cards with usage counts

How it works

  1. Register the reference material your agents keep needing as contexts, each with a type, tags and a visibility setting.
  2. Compose a prompt from a saved template and fill in the variables it declares for this particular run.
  3. Attach the contexts that belong with the request, so the agent starts with the right background instead of a paste.
  4. Choose the target session and send the prompt into it.
  5. Watch the dispatch history for the status of each send and the session it landed in.
  6. From a live chat, insert a saved prompt straight from the side rail, filling any variable it asks for first.
  7. Keep the library tidy over time by editing, sharing or bulk-removing sources that no longer reflect reality.

Who it's for

  • Developers who run coding agents every day
  • Platform teams standardizing how their organization prompts agents
  • Tech leads who want review and debugging instructions to be consistent
  • On-call engineers reaching for a known-good runbook request
  • Technical writers curating the reference material agents read

Illustrations

3 illustrations of this concept. Select one to view it full size.

Compose a Prompt and Send It to a Session

A prompt built from a template, with extracted variables, attached contexts and a send-to-session control.

This illustration shows the prompts tab of the AI area, one of eleven tabs running from overview through config. The main panel, labeled for composing a prompt, offers a generate-from-template action and a template selector holding a sample security review for a payments service; a small floating card shows the category and the tags applied to it. Beneath the selector, the variables the template declares appear as editable inputs — a service name and a release branch in the sample data — followed by a monospace body field holding the composed text. A target-session selector and a send button sit at the bottom. A right-hand card lists contexts available to attach, each with a name, a type label and a checkbox, two of them checked. A dispatch history below lists previous sends with status badges, session references and relative timestamps.

Context Library as Cards

Registered context sources as cards, with type badges, previews, tags and edit, open, share and delete actions.

This illustration presents the context library as a grid of cards. A filter for source type and a control to create a new context sit above a selection bar that offers select-all, reports how many items are currently selected and exposes a clearly marked delete action for them. Each card carries the source name, a type badge distinguishing markdown documents, repositories, plain text and links, a boxed preview of the content itself, and tag chips below; one card shows a lock-marked signed indicator. Four icon actions on every card cover editing, opening, sharing and deleting. One card is checked and outlined to show the selected state. Names, snippets and tags are sample data illustrating the shape of the library rather than real sources.

Context Table and Insert-into-Chat Rail

Contexts listed by type, project, visibility and update time, next to prompt cards ready to insert into chat.

This illustration shows the contexts tab as a table rather than cards, under the full AI tab bar — overview, templates, contexts, prompts, instructions, sessions, queue, tasks, stats, playground and config — and a toggle between the plan and AI views. A search field, a type filter and a new-context action sit above columns for name, type, associated project, visibility, tags and last update; the sample rows mix a markdown document, a saved command, an API call and a plain-text standards entry, some shared with a project and one kept private. A right rail for inserting into chat stacks prompt cards, each with a title, a category badge such as debugging, review or runbook, a usage count and an insert action, with a variable field beneath for the value the prompt expects.

Topics

  • reusable AI prompts
  • prompt library for developers
  • managed AI context sources
  • prompt variables and templates
  • attach context to an agent session
  • prompt dispatch history
  • shared team prompt catalog
  • context visibility and tags
  • insert saved prompt into chat
  • AI workbench context management

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