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sithu/auto_pr_use_cases.md

Created July 31, 2025 06:23
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auto_pr_use_cases.md

Leveraging Gemini CLI + LangGraph + Terraform + GitHub PR automation is a solid pattern that can scale across many developer productivity use cases. Here's how you can apply this architecture to other scenarios, broken down by common workflows, efficiency gains, and tailored examples:

🔁 Generalized Pattern (Your Current Flow):

  1. Input intent (natural language) →
  2. Agent interprets and formulates prompt →
  3. Gemini CLI modifies code/config →
  4. PR created with reasoning trace →
  5. Human review & merge

This is reusable for any infrastructure or code-related automation that can be represented in a repo.

🚀 Developer Productivity Use Case Ideas

1. Kubernetes YAML Updates

  • User Input: "Expose service X on port 443 using a LoadBalancer"
  • Action: Gemini CLI edits Helm chart / service.yaml
  • Benefit: Reduces human error in low-level K8s config

2. CI/CD Pipeline Edits

  • User Input: "Add a step to run npm audit in the GitHub Actions workflow"
  • Action: Modify .github/workflows/ci.yml
  • Benefit: Empowers devs to evolve pipelines without manual YAML editing

3. Secrets Management

  • User Input: "Add secret DB_PASSWORD to staging environment"
  • Action: Gemini CLI modifies secrets.auto.tfvars or Vault policies
  • Benefit: Controlled automation of secret lifecycle with PR traceability

4. Repo Bootstrapper

  • User Input: "Create a new microservice with Node.js + Docker + GitHub Actions"
  • Action: Use templated code generation + Gemini to tweak names/configs
  • Benefit: Drastically reduces boilerplate setup time

5. Code Quality/Policy PRs

  • User Input: "Ensure all Terraform modules have tags block"
  • Action: Gemini scans and PRs any missing tags
  • Benefit: Enforces best practices without manual sweeping

6. Incident Simulation Config

  • User Input: "Simulate 500 error on service-X in staging"
  • Action: Gemini edits config files or chaos testing rules
  • Benefit: Encourages safe experimentation and resilience testing

🧠 Key Benefits of This Pattern

  • Intent-driven workflows = minimal learning curve for engineers
  • Code-first output = everything is reviewable via PR
  • Composable = reuse LangGraph + Gemini CLI + GitHub for any domain
  • Secure = IAM, secrets, infra changes go through normal GitOps flows

🔧 How to Scale the System

  • Use a prompt library mapped to repo types or file structures (e.g. TF, YAML, Dockerfile)
  • Cache prompts & diffs to support dry-run previews
  • Allow users to provide partial natural language + partial structured input
  • Add PR auto-labeling + reviewers for routing
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