GitHub Issue Template: How to Get More Contributions and Build Community
<h2> TL;DR </h2> <ul> <li>Good issue templates increase contributor activity by 40%+</li> <li>Bug report template → better bugs, faster fixes</li> <li>Feature request template → clearer roadmap</li> <li>Pull request template → higher merge rate</li> <li> <strong>Free templates included</strong> — copy and use today</li> </ul> <h2> Why Issue Templates Matter </h2> <p>When developers file issues without guidance, you get:</p> <ul> <li>Vague bug reports: "it doesn't work"</li> <li>Duplicate requests: "I already built this in #123"</li> <li>Missing context: no steps to reproduce</li> </ul> <p>With templates, you get actionable information that moves your project forward.</p> <h2> The 4 Essential Templates </h2> <h3> 1. Bug Report Template </h3> <div class="highlight js-code-highlight"> <pre cl
TL;DR
-
Good issue templates increase contributor activity by 40%+
-
Bug report template → better bugs, faster fixes
-
Feature request template → clearer roadmap
-
Pull request template → higher merge rate
-
Free templates included — copy and use today
Why Issue Templates Matter
When developers file issues without guidance, you get:
-
Vague bug reports: "it doesn't work"
-
Duplicate requests: "I already built this in #123"
-
Missing context: no steps to reproduce
With templates, you get actionable information that moves your project forward.
The 4 Essential Templates
1. Bug Report Template
--- name: 🐛 Bug Report about: Report something that isn't working labels: bug ------ name: 🐛 Bug Report about: Report something that isn't working labels: bug ---Description A clear description of the bug.
Steps to Reproduce
- Go to '...'
- Click on '...'
- See error
Expected Behavior What you expected to happen.
Screenshots If applicable, add screenshots.
Environment
- OS: [e.g. macOS 14.0]
- Version: [e.g. 1.0.0]
Additional Context Any other context about the problem.`
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2. Feature Request Template
--- name: ✨ Feature Request about: Suggest an idea for this project labels: enhancement ------ name: ✨ Feature Request about: Suggest an idea for this project labels: enhancement ---Problem What problem does this solve?
Solution Describe your proposed solution.
Alternatives Any alternative solutions you've considered.
Additional Context Mockups, examples, references.`
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3. Pull Request Template
## TL;DR
Changes
Testing
Screenshots (if UI change)
Checklist
- Tests pass
- Documentation updated
- No console.log`
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4. Custom Issue Template (config.yml)
blank_issues_enabled: false contact_links:blank_issues_enabled: false contact_links:- name: ❓ Questions url: https://github.com/YOUR_REPO/discussions about: Ask questions here
- name: 💬 Discord url: YOUR_DISCORD_LINK about: Chat with the community`
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Pro Tips
Make Templates Discoverable
Add a ISSUE_TEMPLATE.md at repo root:
# Contributing
Before opening an issue:
- 🔍 Search existing issues first
- 📖 Check the FAQ in discussions
- 🐛 For bugs: use the Bug Report template
- ✨ For features: use the Feature Request template`
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Use Labels Strategically
Label When to Use
bug Something is broken
enhancement New feature request
good first issue Easy wins for new contributors
documentation Docs improvements
question User questions
wontfix Won't address, explain why
Respond to Issues Fast
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First response within 48 hours → 3x more likely to get contributions
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Label "good first issue" → attracts new contributors
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Close duplicates politely with a link to the original
TL;DR Summary
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Bug template: Steps to reproduce + expected vs actual
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Feature template: Problem + solution + alternatives
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PR template: TL;DR + changes + testing checklist
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Config: Disable blank issues, add links
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Labels: Make them actionable
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Speed: Respond within 48 hours
For more open source growth strategies, see github.com/Gingiris/gingiris-opensource — the complete playbook from 0 to 60k stars.
Part of the Gingiris Open Source Growth Playbook — helping developers build open source communities.
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[R], 31 MILLIONS High frequency data, Light GBM worked perfectly
We just published a paper on predicting adverse selection in high-frequency crypto markets using LightGBM , and I wanted to share it here because the findings are directly relevant to anyone dealing high frequency data and machine learning The core problem we solved: Every market maker's nightmare — getting picked off by informed traders right before a big move. We built a model that flags those toxic seconds before they wreck you. The data: - 31,081,463 second-level observations of BTC/USDT perpetual futures on Bybit - February 2025 → February 2026 (381 raw daily files) - Strict walk-forward regime, zero lookahead bias The key results (this is the part that shocked us): Our TailScore metric — which combines predicted toxicity probability with predicted price move severity — flags the top
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