Stackshare Scraper

Stackshare Scraper collects structured insights about company tech stacks, popular tools, and technology comparisons from Stackshare. It helps teams analyze trends, compare tools, and understand what technologies companies are actually using in production.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project provides a unified way to retrieve technology stack data, tool popularity metrics, and comparison insights from Stackshare. It solves the problem of fragmented tooling research by offering consistent, structured outputs. It is built for developers, founders, analysts, and product teams who need reliable tech ecosystem intelligence.

Technology Stack Intelligence at Scale

• Retrieves top company stacks with associated tools and layers
• Surfaces the most used and trending developer tools
• Enables exact and fuzzy search across tools and keywords
• Supports direct tool-to-tool comparisons
• Designed for analysis, reporting, and downstream automation

Features

Feature	Description
Get Top Stacks	Returns company technology stacks up to a defined limit.
Get Top Tools	Retrieves the most widely used tools across platforms.
Get Trending Tools	Identifies tools gaining rapid adoption.
Tool Search	Performs fuzzy search across developer tools.
Tool Details	Fetches detailed metadata for a specific tool.
Companies by Tool	Lists companies using a given technology.
Tool Integrations	Returns compatible or integrated tools.
Tool Comparison	Compares two tools using slug-based matching.
Keyword Search	Searches across all supported data types.

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What Data This Scraper Extracts

Field Name	Field Description
company_name	Name of the company using the stack.
company_slug	Unique identifier for the company.
website_url	Official company website URL.
stack_id	Unique stack identifier.
tool_id	Unique identifier for a tool.
tool_name	Name of the technology or tool.
tool_layer	Category or layer the tool belongs to.
tool_image_url	Logo or image URL of the tool.
jobs_count	Number of job listings referencing the stack or tool.
comparison_slug	Identifier used for tool comparisons.

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Example Output

[
    {
        "name": "Pinterest",
        "slug": "pinterest",
        "websiteUrl": "http://pinterest.com/",
        "jobs": {
            "count": 65
        },
        "tools": [
            {
                "id": "101231773837179153",
                "name": "Python",
                "layer": "application_and_data"
            },
            {
                "id": "101231773669807419",
                "name": "Docker",
                "layer": "devops"
            },
            {
                "id": "101231773849098825",
                "name": "React",
                "layer": "application_and_data"
            }
        ]
    }
]

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Directory Structure Tree

stackshare-scraper/
├── src/
│   ├── main.py
│   ├── endpoints/
│   │   ├── stacks.py
│   │   ├── tools.py
│   │   ├── comparisons.py
│   │   └── search.py
│   ├── models/
│   │   ├── company.py
│   │   ├── tool.py
│   │   └── stack.py
│   ├── utils/
│   │   ├── http_client.py
│   │   └── validators.py
│   └── config/
│       └── settings.example.json
├── data/
│   └── samples.json
├── requirements.txt
└── README.md

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Use Cases

• Startup founders use it to analyze competitor technology stacks, so they can make informed architectural decisions.
• Developers use it to compare tools, so they can select technologies with proven adoption.
• Product managers use it to track trending tools, so they can align roadmaps with market demand.
• Market analysts use it to map technology ecosystems, so they can generate research insights.
• Investors use it to evaluate company tech maturity, so they can assess technical risk.

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FAQs

Does this support fuzzy and exact search? Yes, keyword-based fuzzy search is available, while exact slug matching is supported for precise tool queries and comparisons.

Are there result limits? Unless specified, most endpoints return up to 100 results to ensure consistent performance.

How are tools compared? Comparisons are performed using standardized slug identifiers in a tool-vs-tool format.

Is proxy usage required? Yes, proxy usage is recommended to ensure stable and reliable data retrieval.

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Performance Benchmarks and Results

Primary Metric: Processes up to 100 stack or tool records per request with consistent response times under typical workloads.

Reliability Metric: Maintains a high success rate for data retrieval when proxy routing is enabled.

Efficiency Metric: Optimized request handling minimizes redundant calls and reduces resource usage.

Quality Metric: Delivers structured, normalized data with high completeness across tools, stacks, and comparisons.

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