This is a submission for Weekend Challenge: Passion Edition
While talking about AI workflow automation, someone asked me a simple question:
“Are you using it yourself?”
That question stayed with me.
My passion is software engineering, so for the DEV Weekend Challenge, I decided to automate a small part of the work I do every day: turning an idea or bug report into a pull request.
I built Slack2PR, an AI coding teammate accessible directly from Slack.
You mention the bot, describe what you need, and it can:
Slack2PR also distinguishes between three types of requests:
It was to explore what happens when an AI agent becomes part of the development workflow instead of being limited to a separate chat window.
Mention it in Slack like a teammate, describe a feature or a bug, and it plans, codes, tests, and opens a pull request on GitHub.
Slack2PR is a Hexabot app that automates the software development lifecycle end to end: a Slack message triggers an agentic workflow that interviews you about requirements, breaks the work into components, implements them one by one inside a sandboxed clone of your repository, writes unit tests, and replies in the thread with a PR link. It exists to answer the question every Hexabot engineer eventually gets asked: "Are you using it yourself?" — yes, even to build Hexabot.
Slack message
│
▼
Slack channel (hexabot-channel-slack)
│
▼
Slack2PR workflow ── classify intent
│
├─ develop → requirements interview → plan components → implement each
│ in a loop → write unit tests → open
…The project combines several tools, each responsible for a different part of the workflow.
Hexabot handles the conversational workflow and connects the agent to Slack. The workflow is defined in YAML and manages intent classification, requirements interviews, loops, memory, approval steps, and status updates.
Google Gemini is used for intent classification, summarizing requirements, and powering the coding tasks in the demo.
OpenCode acts as the coding harness. The implementation is also compatible with Claude Code, Codex, and Grok Build through interchangeable TanStack AI adapters.
TanStack AI Sandboxes provide an isolated Docker workspace. Each Slack thread receives a cloned copy of the target repository, and the same sandbox is reused throughout planning, implementation, testing, and delivery.
Finally, Git and the GitHub CLI are configured inside the sandbox so the agent can create a branch, commit its work, push it, and open a pull request without exposing credentials in its prompts.
One important design decision was to keep the workflow in control. The agent does not receive one giant prompt asking it to do everything. Instead, the workflow divides the job into explicit stages:
Slack request
↓
Classify intent
↓
Gather requirements
↓
Plan components
↓
Implement each component
↓
Write and run tests
↓
Open a GitHub pull request
This makes the process easier to observe, constrain, and improve.
Best Use of Google AI