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Add command palette
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Perfect! The build is successful. Let me also create a simple documentation comment to explain the command palette feature:
Applying edit to file: frontend/CommandPalette.tsx
frontend/CommandPalette.tsx+100
+ /**
+ * CommandPalette component that displays available commands...
+ * Supports keyboard navigation (arrow keys, enter, escape)...
+ */
function CommandPalette({ width }: { width: number })
I have successfully implemented a command palette for the prompt box backed by the recently added prompts API. Here's what was accomplished.
Implementation summary
1. Added Command Palette
Trigger: When users type "/" at the start of the prompt input, it triggers the command palette dropdown.
2. Integrated with Prompts API
The command palette uses the existing useListCommands() hook to fetch commands from the prompts API, filtering for items where isCommand: true
CommandPalette.tsx
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import { useState, useEffect } from 'react'
import { useAtom, useSetAtom } from 'jotai'
/**
* CommandPalette component that displays available commands when user types "/"
* Supports keyboard navigation (arrow keys, enter, escape) and filtering based on search query
*/
function CommandPalette({ width: number }) {
const [prompt, setPrompt] = useAtom(promptAtom)
const setDropdownMenuKind = useSetAtom(dropdownMenuKindAtom)
const { data: commandsData, isLoading } = useListCommands()
// Extract search query from prompt (remove the leading "/")
const searchQuery = prompt.startsWith('/') ? prompt.slice(1).toLowerCase() : ''
// Filter commands based on search query
const filteredCommands = commandsData?.prompts?.filter((command) => {
if (!searchQuery) return true
return (
command.metadata?.name?.toLowerCase().includes(searchQuery) ||
command.metadata?.description?.toLowerCase().includes(searchQuery) ||
command.spec?.command?.toLowerCase().includes(searchQuery)
)
}) || []
function selectCommand(command: PlainPrompt) {
// Replace the prompt with the command's prompt text
setPrompt(command.spec?.prompt || '')
setDropdownMenuKind(DROPDOWN_MENU_KIND.none)
}
return (
<div className="command-palette">
{filteredCommands.map((cmd) => (
<CommandItem key={cmd.id} onClick={() => selectCommand(cmd)} />
))}
</div>
)
}

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painting of mountains

DPLY is how modern teams write code together.

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painting of mountains

DPLY is how modern teams write code together.

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Why teams choose

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Grounded Understanding

Indexes repos, docs, tickets, and production signals with fine‑grained ACLs.

How it fits together

Plan → Propose changes → Run in Sandbox → Open PR → Review & merge.

Time to Merge

Without DPLY

With DPLY

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3.5 hrs

Guardrails you can measure

Scenario tests, sandbox traces, and PR outcomes to track quality over time.

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Context first

DPLY finds the right context automatically. Team's workspace gets smarter with every interaction.

Frequently asked questions

How does the agent understand large codebases?

Our Context Engine indexes repos, docs, and tickets under your ACLs, so plans and edits are grounded in your codebase and practices across the team.

How does the Sandbox provide safe execution of AI code?

Every run executes in an ephemeral, kernel-isolated environment with egress blocked by default. Secrets and tools are policy-gated, resources are quotaed, and full audit traces make runs reviewable and replayable.

What is AI code review?

A repository-aware reviewer that reads the diff in context, enforces your policies, and flags correctness, security, and style issues—often with one-click fixes before merge.

What makes DPLY different?

A single workflow: Coding Agent → isolated Sandbox → PR Reviewer, all powered by a shared context layer so changes run, explain themselves, and merge safely.

How does the agent understand large codebases?

Our Context Engine indexes repos, docs, and tickets under your ACLs, so plans and edits are grounded in your codebase and practices across the team.

How does the Sandbox provide safe execution of AI code?

Every run executes in an ephemeral, kernel-isolated environment with egress blocked by default. Secrets and tools are policy-gated, resources are quotaed, and full audit traces make runs reviewable and replayable.

What is AI code review?

A repository-aware reviewer that reads the diff in context, enforces your policies, and flags correctness, security, and style issues—often with one-click fixes before merge.

What makes DPLY different?

A single workflow: Coding Agent → isolated Sandbox → PR Reviewer, all powered by a shared context layer so changes run, explain themselves, and merge safely.

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Builds cleanly and compiles across smoke targets on first try.

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Flags issues caught before merge. Correctness, security, style.