How do I install the Oss Issue Scout skill?

Run npx skillmds@latest add peterfile/oss-issue-scout in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Oss Issue Scout safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Oss Issue Scout?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Oss Issue Scout free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Oss Issue Scout?

PeterFile (@peterfile) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Oss Issue Scout by peterfile — Agent Skill

Name: Oss Issue Scout
Rating: 4.666666666666667 (3 reviews)
Author: PeterFile

Oss Issue Scout

Analyze a user-provided Git repository and GitHub issues to confirm contribution rules (CONTRIBUTING, CLA/DCO, templates) and shortlist high-value, maintainer-friendly issues likely to be accepted. Use when a user wants help selecting open-source issues to contribute to, or when triaging a GitHub repo for good first contributions.

PeterFile 3e335bb 2 files · 4.1 KB Updated Sep 10, 2026

File contents

OSS Issue Scout

Overview

Identify contribution rules and surface a small set of high-value, maintainer-friendly issues for a given GitHub repository.

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.

---name: oss-issue-scoutdescription: Analyze a user-provided Git repository and GitHub issues to confirm contribution rules (CONTRIBUTING, CLA/DCO, templates) and shortlist high-value, maintainer-friendly issues likely to be accepted. Use when a user wants help selecting open-source issues to contribute to, or when triaging a GitHub repo for good first contributions.---# OSS Issue Scout## OverviewIdentify contribution rules and surface a small set of high-value, maintainer-friendly issues for a given GitHub repository.## 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.

Frequently asked questions