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remote-jobs-export is a Python command-line tool its author describes as fetching remote-job listings from an API, applying filters, and saving the results as CSV, JSON, or SQLite. The API provides the aggregated, normalized listings; the CLI handles local retrieval and export. The examples below reflect the author’s published commands and feature descriptions, not independent tests of the package or its live API behavior.
What the CLI does—and what it does not do
The project author presents remote-jobs-export as a local fetch, filter, and export layer for remote-job data. It relies on an upstream remote-job API for listings rather than gathering job posts from employer sites itself. The API’s package description says it supplies normalized listings, but a live response and current board coverage were not verified here.
That separation matters: the API determines which jobs are available and how their data is normalized; the CLI determines which retrieved records to keep and which local format to write. Its options therefore do not establish that the upstream service covers every job board or that every listing is current.
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The project author’s example uses pip to install the package, then invokes the CLI to write an export. Confirm the package name and current flags against the project’s published instructions before relying on them, since the commands have not been independently tested.
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Install the package:
pip install remote-jobs-export -
Export listings to CSV:
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The package index lists version 1.1.0, released September 29, 2026. That is release metadata, not a guarantee that these example flags match every release.
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Choose CSV, JSON, or SQLite for the next step
| Format | Best fit | What to expect |
|---|---|---|
| CSV | Opening listings in a spreadsheet or sharing tabular data | A flat interchange format suited to rows and columns. It is convenient for spreadsheet workflows, but does not provide database queries or a rich nested data model. |
| JSON | Passing listings into scripts, services, or other programmatic workflows | A common serialized data format that code can read and write. The project author says the CLI can export it; confirm the exact output shape for your integration. |
| SQLite | Repeated local analysis and SQL queries | A disk-based database file that can be queried with SQL and does not need a separate server process. Unlike CSV and JSON, it is a database, so spreadsheet users may need a SQL-capable viewer or tool. |
These format differences describe the general capabilities of the formats, not a measured comparison of this CLI’s output.
Filter listings by source, skills, or salary
The author documents filters for job source, skills, and minimum salary. The project also describes parsing salary strings into numeric ranges, currency, and pay periods so the minimum-salary filter can be applied. Treat these as project claims: the available example does not establish how accurately every employer’s salary wording is handled.
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Use the exact option names and accepted values shown by the installed version’s help output or current project instructions. No independently verified flag syntax or salary-parsing accuracy is available, so a specific filter command would risk giving you a command that does not work with your release.
- Source filter: narrows results by the listing source, where supported by the installed version.
- Skills filter: keeps listings that match the skills criteria described by the project.
- Minimum salary: uses the project’s described salary parsing and threshold filter; inspect results before treating parsed ranges, currency, or periods as authoritative.
Is it really zero-dependency?
The author describes the package as zero-dependency and says it uses Python standard-library modules including urllib, csv, json, and sqlite3. Python provides standard-library interfaces for making URL requests, reading and writing CSV, serializing and parsing JSON, and working with SQLite. This makes the design plausible without third-party packages, but does not independently audit the package’s dependency metadata or establish that every release has no undeclared dependency.
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For a small export utility, using the standard library can avoid installing extra libraries. It does not remove the need for a working Python installation, access to the upstream API, or a check that the package’s behavior and data meet your needs.
What to verify before depending on an export
- Check the package’s current release instructions for installation syntax, supported formats, and exact filter flags.
- Confirm the API’s current endpoint, access requirements, and board coverage; those details can change and were not verified against a live service.
- Review sample output from your own run before building an automated workflow around column names, JSON structure, or SQLite schema.
- For salary-based decisions, inspect parsed values and retain the original listing context where possible; the project’s illustrative parsing example is not an accuracy guarantee.
Sources and current scope
The project author’s description and examples are available in the project article. The package listing reports version 1.1.0 released September 29, 2026: remote-jobs-export on PyPI. General technical background is documented in the official Python references for URL requests, CSV, JSON, and SQLite. The author’s feature claims and the package listing are not substitutes for testing the current release against the current API.
Quick Recap
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