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Dina Berry
Dina Berry

Posted on • Originally published at dfberry.github.io on

GitHub Account Cleanup: Audit, Archive & Remove Stale Repos

#github #repocleanup #audit #developertools

My end of year project is a GitHub account repository-cleanup tool to provide safe, repeatable auditing and cleanup for my GitHub accounts. I also wanted to create a catalog of my active repos. This repo focuses on repository-level cleanup (archive/delete/catalog), but the same audit run can help you discover candidates for cloud-resource reclamation and CI/workflow maintenance.

When to use this project

  • Periodic account maintenance (end-of-year or scheduled audits).
  • Before publishing a portfolio or transferring repositories.
  • When you want a reproducible audit with a dry-run-first approach.

Functionality included in GitHub account cleanup​

My TypeScript project cleans up my account in the following way:

  • Archive stale repositories,
  • Detect and delete empty repositories,
  • Remove forks,
  • Generate repo descriptions and topics with LLM and update repos with that info,
  • Generate a catalog of active repos for publishing.

High level architecture​

1- primary entry is raw `scripts/run-all.sh` endraw 2- workflows are optional 3- gh-cleanup calls github-rest and llm-completion 4- outputs go to generated/

The high-level architecture of the npm workspace monorepo:

  • packages/gh-cleanup — CLI commands and orchestration: categorization rules, scoring, reporting, and the runner that coordinates dry-run and apply flows.
  • packages/github-rest — GitHub REST helpers, typed endpoint wrappers, and shared network utilities.
  • packages/llm-completion — LLM/AI utilities: prompt helpers, request wrappers, retries, and response sanitization used by the describe step.
  • generated/ — Example outputs created by dry-run executions: catalog.md, active.json, descriptions.json, summaries, etc.
  • .github - Workflows and prompt files.
  • scripts - Top level script to clean up GitHub account, also used by

Prerequisites​

This repo can be opened with Codespaces or locally with .devcontainer/devcontainer.json. The development container has all the developer setup for this project including Node.js. Once you have the repo open with the environment, create a GitHub token and an OpenAI key and an LLM model. Set these values in the root level .env.

  • A GitHub token in GH_TOKEN (classic PAT with repo scopes; delete_repo only required for destructive operations such as deleting a repo.)
  • OpenAI key for LLM generation of repo descriptions and topics and an LLM model. I used 4.1-mini from Azure OpenAI. 
GH_TOKEN=
GH_USER=YOUR_GITHUB_USER_ACCOUNT
OPENAI_API_KEY=
OPENAI_ENDPOINT=https://RESOURCE-NAME.openai.azure.com/openai/deployments/MODEL_NAME/chat/completions?api-version=API_VERSION
OPENAI_MODEL=gpt-4.1-mini
OPENAI_TEMPERATURE=0.2
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Install and build dependencies​

The root package.json uses npm workspaces to control and access all the packages in ./packages. Use the root package.json scripts to install and build the tool.

npm install
npm run build
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Try out the tool​

One-line run examples

  • Remove forks (dry-run):

  • Archive stale repos older than a year (dry-run):

  • Delete empty repos, no PRs, no commits, size is 0 KB (dry-run):

  • Categorize repos (fetch languages + README, output Markdown):

  • Summary (write generated/summary.md):

Full run​

Once you understand what the tool does, use the ./scripts/run-all.sh to clean up your GitHub account. I used my personal dfberry account to test while building. Now that it is complete, I'll use it on my work diberry account for Microsoft.

npm run run-all:apply
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The apply means empty repos are deleted and active repos are updated for description and topics.

AI-assisted development​

I use AI tools (Copilot, Ask/Plan/Agent) to speed development while keeping human oversight. I commit frequently and review AI suggestions line-by-line. Choose models and session types (local, background, cloud) deliberately to control cost and behavior. Never let AI run unattended on critical changes—use dry runs and manual tests. I scaffold the repo, add features incrementally, and document decisions so I can return later. Copilot helps with comments and diagrams, but I always review and adjust its output.

Next steps​

Cleanup goes beyond removing unused repositories. When tidying a personal or org GitHub account you may also:

  • Reclaim unused cloud resources referenced by projects (e.g., old deployments, test clusters, or storage buckets).
  • Remove or archive unused repositories that are forks, abandoned, or no longer relevant.
  • Find and fix failing or stale GitHub Actions workflows (update action versions or workflow syntax) or remove workflows that are no longer useful.
  • Update CI matrices and runtimes (programming language versions, OS matrix entries) to reduce CI cost and avoid testing very-old combinations.
  • Bump pinned GitHub Action versions and dependencies to address deprecations and security fixes.

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