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GitHub Issue Insights

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GitHub Issue Insights

GitHub Issue Insights

GitHub Issue Insights is an Apify Actor that transforms GitHub issues into clear, structured, and multilingual insights.

Pricing

Pay per usage

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0.0

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Developer

Anubhav Kokane

Anubhav Kokane

Maintained by Community

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Monthly active users

2 days ago

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🐞 GitHub Issue Insights

🚀 Overview

GitHub Issue Insights is an Apify Actor that transforms GitHub issues into clear, structured, and multilingual insights.

🎯 Who Is This For?

  • Contributors looking for actionable issues (including Good First Issues) in their native language.
  • Maintainers who want quick clarity on decisions, gaps, and next steps
  • Global Teams bridging language barriers in open-source collaboration.
  • Project Managers needing quick summaries of complex issues.

✨ Features

  • 📥 Reliable Issue Fetching: Uses GitHub API. To retrieve issue title, description, comments, and linked Pull Requests from any public GitHub repository.

  • 🧠 AI Analysis: Leveraging Google Gemini, it extracts:

    • Issue category (Bug / Feature / Improvement)
    • Severity level
    • Technical context (language, framework) with confidence scores
    • Contributor difficulty & required skills
    • Decisions made, unresolved gaps, and open questions
    • Clear, actionable next steps
  • 🌍 Multilingual Insights: Using Lingo.dev

    • Human-readable fields are translated using Lingo.dev
    • Technical metadata, keys, URLs, and labels remain unchanged
    • Supports languages like Spanish, French, Hindi, and more
  • ✅ Structured Output:

    • Returns a well-defined JSON schema
    • Designed for automation, dashboards, and further processing -Safe defaults with explicit inference disclaimers

🛠️ Input Parameters

FieldTypeDescriptionRequired
issue_urlStringFull URL of a public GitHub issue
target_languageStringISO language code (en, es, fr, hi, etc.)
gemini_api_keyStringAPI key from Google AI Studio
lingo_api_keyStringAPI key from Lingo.dev

📦 Output Example

Below is a shortened example of the structured output (translated to Spanish):

{
"issue_data": {
"state": "open",
"assignees": [],
"linked_pull_requests": []
},
"issue_analysis": {
"type": "Bug",
"severity": "High",
"labels": ["bug", "urgent"]
},
"tech_context": {
"language": "JavaScript",
"framework": "React",
"confidence": 0.95
},
"summary": {
"issue": "El usuario reporta un fallo al hacer clic en el botón de guardar...",
"discussion": "Varios desarrolladores han confirmado el error en la versión 2.1..."
},
"contributor_info": {
"good_first_issue": true,
"difficulty": "Low",
"skills": ["React", "CSS"]
},
"decisions_and_gaps": {
"decisions": ["Se decidió revertir el commit anterior."],
"gaps": ["Faltan pruebas unitarias para este componente."],
"open_questions": ["¿Afecta esto a la versión móvil?"]
},
"next_steps": [
"Reproducir el error en el entorno local.",
"Crear un caso de prueba fallido."
],
"language": "es"
}

⚙️ How It Works

  1. Fetch: Parses the issue URL and retrieves issue data and comments using the GitHub API.
  2. Analyze: Sends the issue content to Google Gemini, prompting it to extract structured insights as a senior technical reviewer.
  3. Translate: Translates only human-readable fields using Lingo.dev, preserving technical accuracy.
  4. Deliver: Stores the final structured output in the Apify dataset.

📝 Notes

  • Only public repositories are supported
  • Private repositories require authentication (not supported in this version)
  • Analysis quality depends on the clarity of the issue and discussion
  • Repository files are not read
  • Technical context is inferred and includes confidence scores
  • Linked pull requests are provided as references only
  • Each output explicitly includes:
    • data_sources
    • inference_notice