make_metadata.py
An open-source script that relies only on the Python standard library. It generates metadata CSV files in the respective formats of three platforms—Adobe Stock, Vecteezy, and Dreamstime—from a single asset folder.
Timeline (last 90 days)
make_metadata.py 可通过 --platform 参数为 adobe、vecteezy、dreamstime 生成对应表头的 CSV 文件。
When there is no title file, the metadata script converts the filename into title case, so the filename becomes the title.
Using a script that relies only on the Python standard library to output the exact headers required by each platform (run three times via the --platform parameter), three correct CSV files can be generated from the same folder.
Of the four scripts the author ultimately used—make_metadata.py, upload_tracker.py, md2pdf.py and pack_product.py—two depend only on the Python standard library, and the other two need only Pillow and reportlab.
make_metadata.py by default converts the filename to title-case format as the title, for example asset-001.jpg becomes 'Asset 001'.
The author used make_metadata.py and upload_tracker.py to package 39 products, turning the metadata step from half a day of spreadsheet work per batch into three commands that finish the job.
Do not manually edit the metadata CSV; instead, write the header once in code; automate the structure rather than the judgment; track every submission.
The workflow the author recommends is: first generate the CSV with default values, open it for inspection, then replace the lazy titles before submitting.
When running the make_metadata.py pipeline on a server with 1 vCPU and 2 GB of memory, using only the standard library (without pulling in pandas) is crucial, because pandas brings tens of MB of memory overhead and potential version conflicts.
The toolkit includes `make_metadata.py` (handles three CSV formats), `upload_tracker.py` (upload ledger), `md2pdf.py` (document to PDF), and `pack_product.py` (packages the files together with a 1280×720 cover and a 600×600 thumbnail).
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All Facts (19)
make_metadata.py 可通过 --platform 参数为 adobe、vecteezy、dreamstime 生成对应表头的 CSV 文件。
50%UnverifiedWhen there is no title file, the metadata script converts the filename into title case, so the filename becomes the title.
50%UnverifiedUsing a script that relies only on the Python standard library to output the exact headers required by each platform (run three times via the --platform parameter), three correct CSV files can be generated from the same folder.
50%UnverifiedOf the four scripts the author ultimately used—make_metadata.py, upload_tracker.py, md2pdf.py and pack_product.py—two depend only on the Python standard library, and the other two need only Pillow and reportlab.
50%UnverifiedWhen running the make_metadata.py pipeline on a server with 1 vCPU and 2 GB of memory, using only the standard library (without pulling in pandas) is crucial, because pandas brings tens of MB of memory overhead and potential version conflicts.
50%UnverifiedDo not manually edit the metadata CSV; instead, write the header once in code; automate the structure rather than the judgment; track every submission.
50%UnverifiedThe workflow the author recommends is: first generate the CSV with default values, open it for inspection, then replace the lazy titles before submitting.
50%Unverifiedmake_metadata.py by default converts the filename to title-case format as the title, for example asset-001.jpg becomes 'Asset 001'.
50%UnverifiedThe toolkit includes `make_metadata.py` (handles three CSV formats), `upload_tracker.py` (upload ledger), `md2pdf.py` (document to PDF), and `pack_product.py` (packages the files together with a 1280×720 cover and a 600×600 thumbnail).
50%UnverifiedThe author uses the make_metadata.py script to generate CSV headers according to platform specifications, and this script depends only on the Python standard library
50%UnverifiedGenerating a generic CSV with superset columns will fail on every platform; the effective approach is for the script to generate, for each platform, only a CSV with that platform's exact header.
50%UnverifiedUsing a generic filename (from the automatic conversion when the title file is missing) as the title easily leads to quality-related soft rejections, and such problems are harder to troubleshoot than hard header failures.
50%Unverifiedmake_metadata.py and upload_tracker.py use only the Python standard library and have no third-party dependencies.
50%UnverifiedThe make_metadata.py script relies only on the Python standard library and requires no pip install or any third-party dependencies
50%Unverifiedmake_metadata.py generates precise CSV headers for the target platform, using only the standard library and introducing no frameworks or third-party libraries, to reduce version compatibility risk
50%UnverifiedBy default, make_metadata.py converts file names to title format and uses them as titles, which is good enough for initial screening but absolutely unsuitable for final submission; the script supports the --titles titles.json parameter to provide real titles and keywords.
50%Unverifiedmake_metadata.py is a script that relies only on the Python standard library, traverses folders and generates headers precisely by platform, and supports the --platform adobe, --platform vecteezy, and --platform dreamstime parameters.
50%UnverifiedThe author used make_metadata.py and upload_tracker.py to package 39 products, turning the metadata step from half a day of spreadsheet work per batch into three commands that finish the job.
50%UnverifiedThe author used four scripts—make_metadata.py, upload_tracker.py, md2pdf.py, and pack_product.py—to package the 39 products and 856 MB of output delivered by this project on a 1 vCPU / 2 GB server.
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