OSS Issue Scout

Overview

Workflow

1) Confirm inputs

• Ask for the repo URL or a local repo path and whether GitHub access is available.
• Ask for constraints: language/stack familiarity, time budget, desired impact, preferred work type (bug/doc/test/feature), and skill level.
• Ask if the user can run tests and open PRs.

2) Read contribution rules and signals

• Check repo docs locally or via GitHub: CONTRIBUTING.md, README, CODE_OF_CONDUCT, SECURITY.md, LICENSE, GOVERNANCE, MAINTAINERS, issue/PR templates, .github/ folder, CI configs.
• Note CLA/DCO requirements, branch naming, commit rules, test expectations, and required checks.
• If any open-source or GitHub practice is unclear, use Context7 to refresh before advising.

3) Pull issues via GitHub MCP

• Discover available GitHub MCP resources/templates and use them to list open issues with labels, assignees, milestones, and last activity.
• Prefer issues labeled good first issue/help wanted/bug/docs/tests and those with clear acceptance criteria.
• Filter out: stale/needs info, blocked, huge refactors, security-sensitive items, no reproduction steps, or already assigned issues.
• Cross-check for linked PRs, duplicates, or active discussions that indicate ongoing work.

4) Score and shortlist

• Use

  references/issue-scoring.md

  to score value, acceptance likelihood, clarity, and effort fit.
• Select 3-7 issues with the best balance of impact and low acceptance risk; keep scope aligned to user constraints.

5) Report back

• Provide a concise list of recommended issues with links, labels, last activity, estimated effort, and why they are good picks.
• Summarize contribution rules and any blockers.
• Ask targeted questions if key info is missing or if all candidate issues are low quality.

Output format

• Recommended issues
• Contribution rules and constraints
• Risks/unknowns
• Questions for the user
• Suggested next actions

References

• Read

  references/issue-scoring.md

  when scoring or filtering issues.