GitHub Issue Implementation
Nightly agent that finds AI-ready GitHub issues, implements them via CloudCLI into pull requests, comments the links on issues.
What it does
Get your AI-ready GitHub issues resolved automatically every night as this agent finds them, implements the changes via CloudCLI, and opens pull requests for you. You receive immediate updates with PR links on each issue and a summary of all actions sent directly to your Telegram chat.
Step by step
- Configure the nightly cron schedule in the CI/CD pipeline to trigger the agent at a specific UTC time.
- Set up environment variables for GitHub and Telegram API tokens within the secure secrets manager.
- Define the exact label filter 'AI-ready' and the target branch name in the configuration file.
Where the LLM does the work
- Generate the initial code implementation or patch based on the natural language description found in each issue body.
- Draft a concise summary of the changes made for the Telegram notification message to ensure clarity for stakeholders.
- Write the pull request title and description that accurately reflects the scope of the automated fix.
Watch out for
Telegram notifications may fail if the chat ID is invalid, verify the target chat ID during the initialization phase.
Branch naming collisions can occur in parallel runs, append a unique timestamp or issue number to every new branch name.
Tools that fit
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal GitHub Issue Implementation: Nightly agent that finds AI-ready GitHub issues, implements them via CloudCLI into pull requests, comments the links on issues. ## Specification - What it does: Get your AI-ready GitHub issues resolved automatically every night as this agent finds them, implements the changes via CloudCLI, and opens pull requests for you. You receive immediate updates with PR links on each issue and a summary of all actions sent directly to your Telegram chat. - Trigger: On a schedule (Scheduled · daily 2:00 AM) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - GitHub: Fetch open issues and create pull requests - CloudCLI: Implement code changes and open PRs - Telegram: Send run summaries and comments ## Known pitfalls, handle each one explicitly in your implementation 1. Telegram notifications may fail if the chat ID is invalid, verify the target chat ID during the initialization phase. 2. Branch naming collisions can occur in parallel runs, append a unique timestamp or issue number to every new branch name. ## Reference implementation https://n8n.io/workflows/16035 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automate-ai-ready-github-issue-implementation/ via usecasesforagents.com
Frequently asked questions
Can I use a different Messaging than Telegram?
Yes. Telegram is only the example messaging in this recipe. The same flow works with ClickSend API, Discord, Google Chat and LINE Messaging API. Swap the messaging connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for developers who want the busywork around code automated, not the code itself.