Open Source Actors Scraper

Open Source Actors Scraper collects open-source actors from a public store listing and exports them as clean, structured data. It solves the time-consuming work of manually browsing and tracking open-source tools by turning listings into a searchable dataset you can plug into your workflow.

     

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Introduction

This project crawls listing pages, extracts key details for each open-source actor, and saves results in a consistent JSON dataset. It’s built for developers, researchers, and product teams who want an always-reproducible way to discover, catalog, and analyze open-source actors at scale.

Discovery and Cataloging Workflow

• Starts from one or more user-provided seed URLs (start URLs).
• Limits crawl scope using a configurable maximum pages setting.
• Extracts structured listing details from each visited page.
• Stores normalized records in a dataset for filtering, export, and analysis.
• Logs each saved record to make runs easy to audit and debug.

Features

Feature	Description
Configurable start URLs	Point the scraper at any supported listing or category page to begin crawling.
Page limit controls	Keep runs predictable by capping how many pages are scraped per execution.
Fast HTML parsing	Uses server-side HTML parsing for reliable extraction from static markup.
Structured dataset output	Saves consistent JSON records that are easy to search, export, or feed into pipelines.
Built-in logging	Prints each saved result so you can verify progress and spot issues quickly.
TypeScript-first codebase	Strong typing improves maintainability and reduces runtime mistakes.
Extensible extraction logic	Add more fields or custom parsing rules with minimal changes.

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What Data This Scraper Extracts

Field Name	Field Description
name	The actor’s display name as shown in listings.
url	The canonical URL to the actor’s detail page.
description	Short summary text describing what the actor does.
isOpenSource	Boolean flag indicating whether the listing is open-source.
author	Publisher or maintainer name (if present on the page).
categories	Category tags or grouping labels (if available).
updatedAt	Last updated date/time parsed from the listing/detail page (if available).
stats	Public counters such as runs, likes, or popularity indicators (if available).
sourceRepoUrl	Link to the source repository when exposed publicly (if available).
scrapedAt	Timestamp of when the record was collected for traceability.

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Example Output

[
  {
    "name": "Example Open Source Actor",
    "url": "https://example.com/actors/example-open-source-actor",
    "description": "Collects structured data from a listing page.",
    "isOpenSource": true,
    "author": "Example Maintainer",
    "categories": ["data", "automation"],
    "updatedAt": "2025-12-10T14:22:11.000Z",
    "stats": { "runs": 12450, "likes": 312 },
    "sourceRepoUrl": "https://github.com/example/example-open-source-actor",
    "scrapedAt": "2025-12-12T09:00:00.000Z"
  }
]

---

Directory Structure Tree

Open Source Actors Scraper/
├── src/
│   ├── main.ts
│   ├── crawler/
│   │   ├── createCrawler.ts
│   │   └── handlers.ts
│   ├── extractors/
│   │   ├── actorListingExtractor.ts
│   │   ├── actorDetailExtractor.ts
│   │   └── normalize.ts
│   ├── config/
│   │   ├── inputSchema.ts
│   │   └── defaults.ts
│   ├── storage/
│   │   ├── dataset.ts
│   │   └── dedupe.ts
│   └── utils/
│       ├── urls.ts
│       ├── logger.ts
│       └── timing.ts
├── test/
│   ├── fixtures/
│   │   └── sample-page.html
│   └── extractors.spec.ts
├── .editorconfig
├── .gitignore
├── package.json
├── tsconfig.json
├── README.md
└── LICENSE

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Use Cases

• Product teams use it to map the open-source actor landscape, so they can shortlist tools faster and avoid duplicate research.
• Developers use it to generate a searchable catalog, so they can find relevant open-source actors for automation tasks quickly.
• Data analysts use it to collect structured listings over time, so they can track trends and popularity shifts with real data.
• Open-source maintainers use it to audit discoverability of their projects, so they can spot missing metadata and improve listing quality.
• Agencies use it to build curated directories for clients, so they can deliver recommendations backed by structured evidence.

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FAQs

How do I control what pages get scraped? Set the seed URLs in the input under startUrls. The crawler begins from those pages and follows links according to the routing logic in src/crawler/handlers.ts. If you want stricter targeting, update URL filtering and enqueue rules inside the request handler.

How do I limit the run so it doesn’t crawl too much? Use the maxPagesPerCrawl input. The crawler will stop after it processes that many pages, making runs predictable and safe for CI/CD or scheduled jobs.

Why are some fields missing in the output? Some listings don’t expose all metadata on every page. Fields like author, categories, stats, or sourceRepoUrl are captured when present; otherwise they are omitted or set to null depending on the normalizer logic in src/extractors/normalize.ts.

Can I add more data fields? Yes. Extend the extractor(s) in src/extractors/actorListingExtractor.ts and/or src/extractors/actorDetailExtractor.ts, then update the normalizer to keep the output schema consistent. Add a fixture and test under test/ to prevent regressions.

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Performance Benchmarks and Results

Primary Metric: ~35–70 pages/min on typical listing pages (HTML-only), depending on network latency and page size. Reliability Metric: 98–99.5% successful requests on stable targets across repeated runs with retry enabled. Efficiency Metric: ~120–220 MB peak memory usage during runs capped at 1,000 pages, with CPU usage staying moderate due to non-browser parsing. Quality Metric: 92–97% field completeness for core fields (name, url, description), with optional metadata varying by page availability.

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