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Python scripts and Dialogue with Claude Code CLI.\n\n\u003cimg width=\"300\" height=\"auto\" alt=\"image\" src=\"https://github.com/user-attachments/assets/c1959170-8f43-4638-84ac-d51020b049e0\" /\u003e\n\n## Workflow\n\n```\n.notes/ # 1. Design Prompts with Claude Code in .notes/\nscripts/ # 2. Run the python scripts in scripts\ndata/ # 3. data will output here\n.env # recommended: use API_GITHUB_TOKEN for more tool calls\n```\n\n\n## How to use\n\nFork this repo\n\nempty .notes dir and data (they are examples of how to use)\n\nInteract with Claude Code as your research partner to find issues\n\n\n## process overview\n\n1. User chooses Organisation(s) e.g. Shopify, Stripe, Playwright\n\n2.  extract repositories, sort by PR velocity highest first with `30d_PR_merge_count`. Higher counts are a green flag.\n\n3. select % of PR Velocity and filter out any repos (e.g. I don't want to work with Ruby on Rails)\n\n4. extract open issues list, sort by recently opened\n\n5. Define engagement criteria\n\n6. Manually crawl through 50 most recent issues against engagement criteria (User deletes irrelevant files)\n\n## input\n\ninput can be a CSV with\n\n```\ngithub org / github hyperlink / revenue (USD) / 5 tags / revenue hyperlink (confirm not a hallucination)\n```\n\n## methodology\n\n\n100 Highest 2024 Revenue Orgs on github --\u003e Repo's with Highest 30d Merge Rate, sorted by highest.money and time\n\nthe scope ends at repos to avoid complexity overheads of comparing tickets generally the repos will be manually chosen and examined from there\n\n\n## output\nTable output can be CSV with\n\n```\norg; repo; hyperlink; 30d_merge_rate\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flmcrean%2Fgithub-detective","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flmcrean%2Fgithub-detective","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flmcrean%2Fgithub-detective/lists"}