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CASE STUDY · Public repository

GitHub Analytics Dashboard

An open-source agentic pipeline and React dashboard that ingests developer activity from the GitHub API and leverages AI to generate actionable insights regarding coding patterns, language diversity, and contribution streaks.

Role
Full-Stack Engineer
Access
Public repository
Link
GitHub Repository ↗
Analytics dashboard

Current streak

18days

TypeScript 42%
Python 31%
PHP 17%

Contribution activity

[01]

Context

An open-source agentic pipeline and React dashboard that ingests developer activity from the GitHub API and leverages AI to generate actionable insights regarding coding patterns, language diversity, and contribution streaks.

[02]

My responsibility

Built across the backend workflow and its operational interface. This page documents the implementation areas that can be shown publicly.

[03]

System scheme

Engineering scheme

Repository analytics pipeline

Input → Processing → Output

GitHub activity is normalized into deterministic metrics before the language model adds a separate interpretation layer.

  1. 01

    GitHub API

    Fetch profile, repository, language, and contribution data.

  2. 02

    Rate-limit layer

    Track quotas, pagination, and recoverable API failures.

  3. 03

    Normalizer

    Convert provider responses into stable internal records.

  4. 04

    Metrics engine

    Calculate streaks, language share, and contribution patterns.

  5. 05

    AI analysis

    Generate narrative insights from structured metrics.

  6. 06

    React dashboard

    Present heatmaps, charts, summaries, and follow-up chat.

API caching
Deterministic metrics
Prompt context
Visualization
[04]

Implementation breakdown

  1. 01

    Capability 01

    Real-time GitHub API ingestion and rate-limit handling.

  2. 02

    Capability 02

    LLM-generated developer profile assessments.

  3. 03

    Capability 03

    Interactive data visualizations (Heatmaps, Radar charts).

[05]

Technical scope

Python01
Node.js02
React03
GitHub API04
OpenAI API05
[07]

Implementation details

  • ✓Real-time GitHub API ingestion and rate-limit handling.
  • ✓LLM-generated developer profile assessments.
  • ✓Interactive data visualizations (Heatmaps, Radar charts).
[08]

Engineering focus

  • →API caching remained an explicit concern across the implementation.
  • →Deterministic metrics remained an explicit concern across the implementation.
  • →Prompt context remained an explicit concern across the implementation.
  • →Visualization remained an explicit concern across the implementation.

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