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https://github.com/huangsam/hotspot

Diagnose debt before it ships! ๐Ÿ”ฌ
https://github.com/huangsam/hotspot

cli devex devops git golang hacktoberfest insights maintenance statistics

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Diagnose debt before it ships! ๐Ÿ”ฌ

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# Hotspot

[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/huangsam/hotspot/ci.yml)](https://github.com/huangsam/hotspot/actions)
[![Go Report Card](https://goreportcard.com/badge/github.com/huangsam/hotspot)](https://goreportcard.com/report/github.com/huangsam/hotspot)
[![License](https://img.shields.io/github/license/huangsam/hotspot)](https://github.com/huangsam/hotspot/blob/main/LICENSE)
[![GitHub Release](https://img.shields.io/github/v/release/huangsam/hotspot)](https://github.com/huangsam/hotspot/releases/latest)

Hotspot is an agentic intelligence layer and CLI that analyzes Git history to diagnose technical debt and bus factor risk based on developer activity, ownership, and churn patterns.

Hotspot Demo

Unlike traditional linters or team-velocity metrics, Hotspot analyzes **development behavior**. It turns Git metadata into high-fidelity signals for technical debt, knowledge silos, and refactoring ROIโ€”empowering both humans and AI agents to make data-driven architecture decisions.

## Features

- ๐Ÿค– **Agentic Hub** - Native MCP server with shape-aware "Reasoning" labels for autonomous auditing.
- ๐Ÿ” **Tactical CLI** - Rapid file/folder ranking by activity, complexity, and ownership.
- ๐Ÿงฎ **Deep Metrics** - High-fidelity signals for churn, Ginni coefficients, and bus factor risk.
- ๐Ÿ•“ **Trend Tracking** - Time-anchored analysis and delta tracking across Git references.
- ๐Ÿ“Š **Polyglot Exports** - Professional CSV/JSON/Parquet/Markdown reporting.

## Installation

### Requirements

- **Go 1.26+** for building from source
- **Git 2.2+** for repository analysis

### Install from source

```bash
go install github.com/huangsam/hotspot@latest
```

### Download pre-built binary

Visit the [latest release](https://github.com/huangsam/hotspot/releases/latest) and download the `tar` archive for your system (supports **Windows**, **macOS**, and **Linux**), then extract the binary to your `$PATH`.

## Quick start: Choose your path

Hotspot is designed for both human-driven tactical analysis and AI-driven strategic auditing.

### ๐Ÿค– Path A: AI Agent

Hotspot includes a **Self-Documenting Agentic Hub** via the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). This allows AI agents to autonomously explore your repository.

```bash
# Start the MCP server (stdio)
hotspot mcp
```

### ๐Ÿ” Path B: Tactical CLI

For immediate terminal-based checks and CI/CD integration.

```bash
# Initialize hotspot with sensible defaults
hotspot init

# Analyze files for tactical risk
hotspot files

# Analyze folders for strategic subsystems
hotspot folders
```

### Documentation

- **[USERGUIDE.md](./USERGUIDE.md)**: Essential guide for analysis features, scoring modes, and core workflows.
- **[PLAYBOOK.md](./PLAYBOOK.md)**: Actionable guidance on using this data to foster a healthy engineering culture.

## Performance

Hotspot is optimized for speed, even on massive repositories, by caching Git analysis results and using concurrent workers.

[csv-parser]: https://github.com/vincentlaucsb/csv-parser
[fd]: https://github.com/sharkdp/fd
[git]: https://github.com/git/git
[kubernetes]: https://github.com/kubernetes/kubernetes

### Benchmark results

Comprehensive performance benchmarks using [this script](./benchmark/main.go). This shows cold vs warm timings:

| Repository | Files (Cold/Warm) | Compare Files (Cold/Warm) | Timeseries (Cold/Warm) |
|------------|-------------------|---------------------------|------------------------|
| [csv-parser] | 0.075s / 0.026s | 0.144s / 0.051s | 0.168s / 0.074s |
| [fd] | 0.045s / 0.024s | 0.083s / 0.048s | 0.132s / 0.069s |
| [git] | 0.546s / 0.041s | 1.389s / 0.175s | 2.039s / 0.243s |
| [kubernetes] | 3.018s / 0.146s | 7.330s / 1.456s | 11.508s / 0.925s |

The data shows that Hotspot caches Git analysis results to speed up repeated runs.