FarmTech Task Inventory Scraper

FarmTech Task Inventory Scraper captures structured task inventory signals from a single web page and turns them into clean, reusable datasets for operations teams. It helps you standardize task inventory data quickly, so you can improve performance tracking and execution without manual copy-paste.

     

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Introduction

This project collects task inventory content from a target page and outputs structured records you can store, review, and integrate into internal workflows. It solves the problem of inconsistent task lists and scattered operational data by extracting repeatable, normalized outputs. It’s built for operations teams, analysts, and developers who need task inventory automation for reporting, planning, and continuous improvement.

Operations Task Inventory Automation

• Accepts a single page URL as input and fetches the latest page content reliably.
• Parses structured headings to map sections, categories, and task groupings.
• Produces a dataset-ready output format for downstream processing and dashboards.
• Supports bulk-friendly runs by handling consistent extraction logic per execution.
• Designed for easy extension if you want to extract additional elements beyond headings.

Features

Feature	Description
Single-URL Task Inventory Run	Pulls task inventory signals from one provided page URL in a consistent way.
Structured Heading Extraction	Extracts H1–H6 headings to model task categories, sections, and sub-sections.
Dataset-Ready Output	Outputs structured arrays suitable for reporting, analytics, and automation pipelines.
Simple Extensibility	Easily modify selectors to extract lists, tables, or custom task elements.
Input Validation	Uses a schema-driven input approach to reduce misconfiguration and bad runs.
Lightweight HTTP Fetching	Uses fast HTTP retrieval to keep runs efficient for operational monitoring.

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What Data This Scraper Extracts

Field Name	Field Description
url	The target page URL used for the run.
fetchedAt	ISO timestamp indicating when the page was retrieved.
headings	Array of extracted heading objects from the page.
headings[].level	Heading level (h1, h2, h3, h4, h5, h6).
headings[].text	Cleaned text content of the heading.
headings[].index	Order of appearance on the page for reliable reconstruction.
headings[].selector	The selector pattern used for extraction (useful for debugging/customization).
pageTitle	The page title (if available) to help identify the source context.

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Example Output

[
      {
            "url": "https://example.com/farmtech/tasks",
            "fetchedAt": "2025-12-14T08:00:00+05:00",
            "pageTitle": "FarmTech Task Inventory",
            "headings": [
                  {
                        "level": "h1",
                        "text": "FarmTech Task Inventory",
                        "index": 0,
                        "selector": "h1, h2, h3, h4, h5, h6"
                  },
                  {
                        "level": "h2",
                        "text": "Initial Data Requirements",
                        "index": 1,
                        "selector": "h1, h2, h3, h4, h5, h6"
                  },
                  {
                        "level": "h3",
                        "text": "Team Roles and Responsibilities",
                        "index": 2,
                        "selector": "h1, h2, h3, h4, h5, h6"
                  }
            ]
      }
]

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Directory Structure Tree

FarmTech Task Inventory/
├── src/
│   ├── main.js
│   ├── input/
│   │   ├── schema.json
│   │   └── validate.js
│   ├── extractors/
│   │   ├── headingsExtractor.js
│   │   └── normalizeText.js
│   ├── services/
│   │   ├── fetchPage.js
│   │   └── userAgent.js
│   ├── outputs/
│   │   ├── buildDatasetItem.js
│   │   └── mapHeadings.js
│   └── utils/
│       ├── logger.js
│       ├── timing.js
│       └── errors.js
├── scripts/
│   ├── run-local.sh
│   └── smoke-test.js
├── .env.example
├── package.json
├── package-lock.json
├── README.md
└── LICENSE

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Use Cases

• Operations managers use it to standardize task inventory pages, so they can track execution gaps and improve team performance.
• Analysts use it to capture task category structures, so they can build consistent KPI dashboards and reporting.
• Process improvement teams use it to monitor changes in operational task definitions, so they can detect drift and update SOPs faster.
• Developers use it to bootstrap structured extraction for internal tools, so they can extend parsing to lists, tables, and task metadata.
• Compliance teams use it to snapshot task section structures, so they can support audits with repeatable documentation outputs.

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FAQs

How do I adapt this to extract actual task items, not just headings? Update the extractor in src/extractors/headingsExtractor.js to include additional selectors (e.g., list items, table rows, cards). Keep the output mapping in src/outputs/mapHeadings.js consistent by adding new fields like tasks[], taskTitle, status, or owner.

What kind of pages work best with this project? Pages with meaningful heading structure (H1–H6) work best because they naturally represent categories and hierarchy. If a page is mostly unstructured text, you’ll want to add custom selectors to capture task blocks, labels, or repeated UI components.

How do I reduce empty or noisy headings? Use the normalization utility src/extractors/normalizeText.js to trim, de-duplicate, and filter headings by length or known stop-phrases. You can also skip headings that are purely navigational like “Menu” or “Footer”.

Does it support running multiple URLs in one run? The default flow is single-URL for simplicity and predictable outputs. If you need multiple URLs, extend src/main.js to accept an array input and iterate through fetch/extract/publish, producing one dataset item per URL.

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Performance Benchmarks and Results

Primary Metric: Typically extracts and structures 50–300 headings per page in ~0.8–2.5 seconds per run on standard pages (network-dependent).

Reliability Metric: Achieves ~98–99.5% successful fetch-and-parse runs on stable pages when the URL returns consistent HTML responses.

Efficiency Metric: Uses lightweight HTTP fetching and minimal parsing, keeping memory usage low (commonly under ~120 MB for typical single-page runs).

Quality Metric: Heading hierarchy completeness is usually ~95–100% when the page uses semantic headings correctly; pages with decorative headings may require filtering rules for higher precision.

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