DEV Community: didyouseethis212-sys

A tiny CSV cleanup demo: keep identifiers and audit every change

didyouseethis212-sys — Sat, 14 Nov 2026 07:56:37 +0000

A CSV cleanup can look correct while silently losing an identifier such as 0012. Here is a small, original fictional example you can run locally, plus a selectable-PDF table example to check against the source.

Make the rules explicit

Our sample starts with four inventory rows. We agree to trim the product column and remove only exact duplicates after that trim. The result has three rows; SKU 0012 stays a string. No missing values are guessed and no other columns are normalized.

sku,product,quantity
0012, Desk tray ,12
0013,Pen holder,8
0013,Pen holder,8
0014,Notebook,25

The free example repository includes the Python script, before/after CSV files and a JSON change report. Python 3 is the only dependency for the CSV example. It makes no network or API calls.

python3 clean_csv.py messy-inventory.csv cleaned-inventory.csv --trim product --drop-exact

The helper preserves the source, refuses uneven row widths and existing output files, and records source line numbers for changes. The example was run: input 4 rows, output 3 rows, one trim and one exact duplicate removed. This is deliberately a small helper, not an automatic decision about which business records are duplicates.

Verify the output, not just the command

Confirm the row count and review every deletion.
Compare identifier strings, including leading zeros. CSV cannot enforce Excel types: import identifier columns as Text.
Check totals against the source when totals are meaningful.
Review untrusted formula-like cell values before opening a CSV in a spreadsheet. This helper is not a general CSV security sanitizer.

PDF tables need a source check too

The repository also contains an original fictional selectable-text PDF and an Excel workbook showing its extracted table, plus a separate raw/cleaned/change-log example. Compare each row, column, SKU and quantity. Being able to select PDF text does not guarantee a clean table layout. Scans, handwriting and complex merges need different handling. Flag unclear cells instead of inventing a value.

Optional done-for-you pilots

Disclosure: I operate the following Fiverr services. The examples above are free and do not require an order.

Excel/CSV cleanup: $20, 2 days, one flat sheet up to 100 data rows and 10 columns, agreed cleanup and change log, one revision.
Selectable PDF tables to Excel/CSV: $20, 2 days, one PDF up to 5 pages, 500 table rows and 10 columns, one revision. Send a de-identified sample on Fiverr first so I can confirm extractability.

Only send authorized material with personal and confidential information removed. All communication, payment and delivery stay on Fiverr. AI assisted this article and the example code; the sample output was checked locally. These are fictional demonstrations, not client results.

A tiny Python helper for source-grounded content repurposing

didyouseethis212-sys — Sat, 14 Nov 2026 07:21:54 +0000

A content-repurposing workflow has two different jobs: assembling a consistent draft pack, and deciding whether the draft actually says what the source supports. A script can help with the first job. JSON validation cannot do the second.

I put together a small Python helper and a fictional demonstration that make that boundary explicit. It runs locally with the standard library, does not need an API key, and never connects to publishing accounts.

The workflow

Start with an article or transcript you own or are authorized to process. Remove confidential or personal information first. The helper numbers the source paragraphs and prepares a prompt for an AI chat you already use:

python3 pipeline.py prepare sample-source.txt --out demo

This writes demo/prompt.txt and demo/numbered-source.txt. The prompt requests three LinkedIn drafts, one email draft and two short-video scripts. Each item has text plus source IDs.

The AI step is manual: copy the prompt to your existing chat and save the JSON-only reply. Available usage depends on that account. There is no hidden API integration.

python3 pipeline.py pack demo/response.json --out demo

The pack command checks structure and writes a readable text file. To test packaging without an AI service, use the included fictional response:

python3 pipeline.py pack sample-response.json --out demo

What validation misses

Consider this fictional source:

A weekly handoff note can include the task, owner, next action and due date. If the work is unclear, have a conversation first.

A source-grounded draft could be:

Your handoff note needs a named owner and an explicit next action. If the task is unclear, talk before sharing it.

But a draft claiming that the note increased productivity by 30% would be unsupported—even if it contains a perfectly valid source ID. The helper does not claim to catch that semantic error.

Before publishing, compare every factual claim to the original. Remove invented metrics and testimonials, check the audience and call to action, and give each draft a distinct point. Have the content owner approve the result.

Try the example

Repository, helper and fictional source/output files.

The example is a demonstration, not a client case study or evidence of engagement, time saved or sales.

Disclosure: this article and demonstration were created with AI assistance. I operate the optional B2B repurposing service linked here. The $50 pilot provides the six written drafts above, up to 1,000 delivery words from one source of up to 2,000 words, with a three-day delivery window after complete requirements and one revision. It does not include video production or automatic publishing. The local helper remains available to try without purchasing the service.

A Shopify CSV checker with line-level reports and an n8n import gate

didyouseethis212-sys — Fri, 13 Nov 2026 13:17:57 +0000

Disclosure: I maintain Catalog Import Guard, including its paid API. This article was prepared with AI assistance, and the sample results and workflow execution were checked before publication.

A product CSV can be valid CSV and still contain an accidental duplicate variant, a price field with a currency symbol, or an empty field that changes an existing value during an overwrite. I built a small checker around that pre-import step.

Try the browser checker — no account, API key or Shopify installation is required. The CSV is checked on your device.

An example you can reproduce

Load the built-in example, or paste this CSV and choose Update products:

URL handle,Title,Option1 name,Option1 value,Price,Vendor
linen-shirt,Linen Shirt,Size,S,49.00,
linen-shirt,,,S,$39.00,

The report contains two errors and one warning:

Physical line	Finding	Why it needs review
2	Blank Vendor overwrite warning	With overwrite enabled, an empty vendor can clear an existing value.
3	Duplicate variant	Size S already appears within the same product handle.
3	Price format	The price field expects a number without a currency symbol.

The checker reports physical line numbers, including files with quoted multiline cells. It does not guess what a price should be or change a store.

Using the same check in n8n

For a recurring supplier-file handoff, the public workflow and setup guide are on GitHub.

The workflow runs two synthetic cases, sends CSV text to the API, validates the response shape, produces a downloadable CSV issue report for each item, and routes blocking findings separately from results ready for manual review.

An HTTP 200 response alone does not mean the catalog passed. Errors, failed checks or a truncated report go to the blocked branch. A passing, untruncated result goes to the review branch. Both branches are connected; neither imports products or sends messages.

Import and execution were verified in local n8n 2.46.6 using the actual Worker handler through a localhost fixture and synthetic authentication. Both branches and the binary report filenames were checked. I have not tested this in n8n Cloud or through a real Shopify import.

Costs, data and limits

The browser checker remains free and local. The n8n workflow requires your own RapidAPI key and sends CSV text to the API. Remove customer information and credentials first.

The current API plans include BASIC at $0 for 100 requests/month and PRO at $5 for 1,000 requests/month, subject to platform terms, request limits and bandwidth fees. The two-case tutorial makes two requests per run. Your n8n hosting can have separate costs.

The browser accepts UTF-8 CSV text up to 256 KiB and 5,000 rows. API requests have a 256 KiB encoded JSON limit, so JSON escaping also counts. Checks cover selected product CSV fields; a passing result does not guarantee Shopify accepts an import. Review overwrite warnings and Shopify's own import preview before importing.

If you handle supplier CSVs, which errors take the most manual checking? A synthetic row and the relevant column names are enough to discuss a case; please do not post private catalog or customer data.