Dip your tickets in codebase context
Shared components, patterns, gotchas, connected code paths - all understood by your agents before they write a line.
Works with
import { Guard } from '@sys/auth'
import { Button } from '@sys/ui'
function Archive() {
// Context: Critical acts
// require a Guard wrapper
return (
<Guard role="admin">
<Button intent="danger">
Archive Project
</Button>
</Guard>
)
}AI agents mess up for one reason: they don't know your codebase.
They search randomly, blow up context windows, and make junior-level mistakes that cost time and trust.
Superfocus fixes this by pre-loading your agents with only the relevant codebase details - filtered, ordered, and aligned with how your team actually builds software.
When will this be done?
Because Superfocus understands patterns, complexity, and connected code paths, it can predict effort with surprising accuracy, export those estimates to your issue tracker, and show you a timeline of upcoming work.
Automates refinement
Sorts tickets by priority
Makes "When will this be done?" easy
Reduces context-switching
Supercharges - or eliminates - planning
This alone makes engineers feel dramatically less overwhelmed.
See the difference context makes
Watch an AI agent attempt the same complex ticket with and without codebase awareness.
Dipping, filtering, parallel work, no cloning.
Runs inside VS Code or Cursor
Reads your codebase locally — nothing is cloned or uploaded
Dips your tickets into the exact sub-contexts required
Spawns sub-agents for deep dives without derailing the main agent
Works entirely in the background
Can process multiple tickets in parallel
Outputs refined context directly to Jira or Linear
You keep coding. Superfocus does the research.
Superfocus is built for real companies.
Runs locally inside VS Code / Cursor
Uses your existing enterprise OpenAI account
Stores keys in secure local storage
Sends only enriched context to Jira / Linear
Tracks usage anonymously to improve the product
Flat monthly pricing — you control your API costs
Because context quality improves, your token usage for implementation actually drops.