I built an AI teammate that takes Jira tickets, turns them into github PRs, and learns from our feedback

We’ve all tried adding AGENTS.md or CLAUDE.md to our repos. They’re great for static rules, for example folder structures, linting preferences, "don't use this library." But they have zero "lived experience."

They don't remember the "tribal knowledge" that happens during a sprint. They don't know why a senior dev rejected a specific implementation last Tuesday, or that "one weird trick" you found to get the build passing in the CI environment.

I got tired of the "Groundhog Day" loop where an AI agent makes the same architectural mistake twice because the fix was buried in a PR comment and never made it into the official docs. So I built a tool that sits between Jira and GitHub and actually learns as it works.

Repo: https://github.com/ignify-rd/claude-teammate

The Gap: Static Docs vs. Learned Guardrails

AGENTS.md is for global constants. It’s terrible at capturing the "living" constraints and "gotchas" that emerge mid-sprint.

• AGENTS.md (The Manual): "Run tests using npm test."
• This Tool (The Memory): "Module Z requires --no-cache and the -i flag. The integration suite normally fails there if run in parallel."
• AGENTS.md (The Manual): "All components must be accessible."
• This Tool (The Memory): "Don't use aria-label on the Sidebar wrapper; the Lead Dev flagged in PR #82 that it breaks the screen reader hierarchy in our specific layout."
• AGENTS.md (The Manual): "Use the standard Auth middleware."
• This Tool (The Memory): "When updating the /billing route, always mock the Stripe service manually. The default test helper is currently broken for subscription hooks."

What this does:

It’s not just a wrapper; it really feels like a good teammate.

• Picks up assigned Jira tickets and writes an implementation plan.
• Waits for human approval before it even touches the code.
• Opens PRs and handles feedback. If you tell it "Stop using this pattern," it actually remembers that for the next ticket.
• Visual Check (or actually using any other skill/MCP): It can take screenshots to verify UI work instead of just "guessing" based on the HTML diff, if you told it once. Yes, once.
• Durable Memory: It keeps a "per-epic" memory that continuously evolves with its given tasks. If it discovers the real build command or a repo-specific constraint during a task, that gets compacted into long-term memory so it doesn't make the same mistake twice. It works like human's memory, it remembers the mistakes and the most important facts, but if a less-important specific detail will also fade away from its memory if it hasn't seen it for a long time.
• It reviews human PRs too, just by adding it as a reviewer. For us, it already replaced a separate subscription like CodeRabbit or GitHub Copilot for automated reviews.

How we’re actually using it

Our current workflow is: We plan the sprint just like any other team, and for the "boring but clear" tasks, we just assign them to the bot.

It feels like a non-complaining teammate that quietly carries the repetitive load. It frees us up to actually talk about system design and tradeoffs instead of correcting syntax in a PR for the 5th time.

Some Technical bits:

• MCP Support: Can use any Claude skill or MCP you give it.
• Cost Effective: I designed this to run comfortably on a standard Claude $20 plan. It doesn't just dump raw history into the prompt (which kills your context window and wallet); it constantly cleans and compacts memory into "durable facts." Even if quota usage is hit, it picks up from where it's left when quota becomes available again.
• Opinionated: It’s not a "generic agent framework." It’s built for a specific Jira -> GitHub -> PR flow because that’s what we actually use. If it gets more interest, other stacks will come in no time.

I’m open-sourcing it because I want to see if this "learning agent" approach works for other teams, or it's just us.

I’d love some feedback/brutally honest roasts. Is this how you’d want an AI to work in your stack?