This workflow automates refreshing a ranked markdown file of popular LLM applications by scraping GitHub topics and sorting by stars. It wraps get_app_list_by_github_star.py with clear guardrails: the generated list lives in section/x_llm_apps.md and gets linked from applications.md, never pasted inline. You can tune the star threshold, export to JSON or CSV for inspection, or narrow the topic set when the default pull is too broad. The main gotcha is forgetting to use a GitHub token, which will get you rate limited halfway through a full topic sweep. It deliberately outputs plain text star counts instead of live badges, so don't run badge scripts on the generated file or your next refresh will clobber them.
npx -y skills add kimtth/awesome-azure-openai-llm --skill update-app-count --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
github/awesome-copilot
microsoft/win-dev-skills
github/awesome-copilot
github/awesome-copilot