DEV Community: Venture Studios

DockerLint: Automated Dockerfile Security Linting for Python Projects

Venture Studios — Mon, 10 Aug 2026 22:15:54 +0000

DockerLint: Automated Dockerfile Security Linting

DockerLint is a lightweight Python CLI tool that performs automated security linting on Dockerfiles. It identifies common misconfigurations, security risks, and best-practice violations in your container definitions.

Features

Fast, dependency-free Python CLI
Detects privilege escalation risks
Identifies missing security contexts
Checks for outdated base images
Validates layer caching strategies
JSON and human-readable output

Quick Start

pip install dockerlint
dockerlint scan Dockerfile

Why Use DockerLint?

Docker security is often overlooked in the CI/CD pipeline. DockerLint catches issues before they reach production:

Run as non-root: Enforces principle of least privilege
Multi-stage builds: Reduces final image size and attack surface
Signed base images: Verifies container image provenance
Health checks: Ensures containers can signal readiness

Get DockerLint + LicenseAudit Bundle

Ship Safer Python Toolkit — two essential security tools for $39:

DockerLint: Container security scanning
LicenseAudit: Dependency and supply-chain risk detection

Both tools are instant-delivery, dependency-free Python CLIs.

Get the Bundle Now — Stripe checkout, instant access.

Secure your Python applications. Start with DockerLint.

Clean messy CSV files reliably: a practical workflow

Venture Studios — Mon, 10 Aug 2026 16:50:51 +0000

Clean messy CSV files reliably: a practical workflow

CSV exports often break downstream analysis because headers vary, values are blank, dates use mixed formats, and the same record appears more than once. This short workflow helps make a file predictable before importing it into a database, spreadsheet, or JSON-based application.

1. Preserve the original

Keep the source file unchanged and work on a copy. Decode it explicitly (UTF-8 is a good first choice) and inspect the first rows for delimiter, quoting, and header problems.

2. Normalize headers

Trim whitespace, use consistent casing, and map aliases to one canonical name. For example, E-mail, email_address, and Email can become email. Reject or flag duplicate column names rather than silently overwriting them.

3. Normalize values deliberately

Treat blank strings as missing values. Parse dates using a documented target format, normalize emails by trimming whitespace and lowercasing, and keep identifiers as strings when leading zeroes matter. Record any values that cannot be parsed instead of guessing.

4. Deduplicate with a business key

A full-row duplicate is easy to remove, but near-duplicates require a defined key such as normalized email plus order ID. Decide whether the newest row wins, then log the discarded rows for review.

5. Validate before exporting

Check required columns, malformed emails, invalid dates, and unexpected types. Export a clean CSV for spreadsheet users and JSON for integrations, along with a small exception report.

For a small dataset where you want this done by a human, I offer a transparent flat-rate CSV cleanup, deduplication, header/date normalization, validation, and CSV-to-JSON conversion service with a 24-hour turnaround: CSV Cleanup Service ($29). After checkout, send the file or a download link and the desired output fields to vntrstds@gmail.com. Please do not send sensitive data unless you are authorized to share it.

What validation rules do you use most often when preparing CSV exports?

A practical workflow for cleaning CSV data before JSON export

Venture Studios — Mon, 10 Aug 2026 16:37:44 +0000

CSV files often arrive with duplicate records, inconsistent column names, blank required fields, mixed date formats, email whitespace, and encoding issues. A dependable cleanup workflow is: preserve the original; standardize headers; trim and normalize values; parse dates to ISO-8601; validate required fields; deduplicate using stable business keys; then export both a clean CSV and JSON for downstream systems.

For repeatable work, record each transformation and keep rejected rows in a separate file so nothing silently disappears. For a small dataset that needs a human cleanup rather than a script, I offer a flat $29 service with a 24-hour turnaround: cleaned/deduplicated CSV plus normalized JSON. Buy here: https://buy.stripe.com/3cI28t7YxgmGaRj7zRaR20u . After checkout, send a secure download link and requirements to vntrstds@gmail.com.

This is an optional paid service; the workflow above is usable independently.

A practical checklist for cleaning a CSV before analysis or import

Venture Studios — Mon, 10 Aug 2026 16:15:01 +0000

A practical checklist for cleaning a CSV before analysis or import

Messy CSVs routinely fail downstream imports because headers drift, duplicate records accumulate, dates use mixed formats, and blank strings mean different things in different columns. Here is a compact, repeatable workflow.

1. Preserve the original and inspect structure

Keep an untouched copy. Check encoding, delimiter, row count, duplicate headers, unexpected columns, and a sample of the longest values. Do not silently discard rows before recording why.

2. Normalize headers and missing values

Trim whitespace, use stable machine-friendly header names, and map common aliases (for example e-mail and email) deliberately. Convert agreed missing markers such as empty strings, N/A, and null into one representation while retaining fields where an empty value is meaningful.

3. Standardize values by column type

Parse dates to one documented format, lowercase/trim email addresses where appropriate, normalize phone numbers only with a stated locale assumption, and preserve leading zeros for identifiers. Flag conversion failures instead of guessing.

4. Deduplicate with a documented key

Exact-row deduplication is safe only for identical rows. For customer data, define a key such as normalized email plus account ID, decide which source wins conflicts, and output a review file for uncertain matches.

5. Validate and export

Run validation rules (required fields, allowed values, parseable dates), produce an exception report, then export clean CSV and JSON with UTF-8 encoding. Keep a short transformation log so the result is reproducible.

If you need a human to do this for a small-business dataset, I offer CSV cleanup, deduplication, header/date normalization, validation, and CSV-to-JSON conversion for $29 flat with 24-hour turnaround. This is a paid service, not an automated API. Checkout: https://buy.stripe.com/3cI28t7YxgmGaRj7zRaR20u . After purchase, email a secure download link and requirements to vntrstds@gmail.com; you receive cleaned CSV, normalized JSON where requested, and a brief change log.

A practical workflow for cleaning messy CSV files before JSON conversion

Venture Studios — Mon, 10 Aug 2026 14:49:13 +0000

A repeatable cleanup workflow

Before parsing, preserve the original file. Then normalize headers, trim whitespace, standardize empty values, validate email/date fields, and deduplicate using the business key rather than the full row. Export a clean CSV and JSON only after recording rejected rows and reasons.

A small local Python starter kit is available here: https://vntrstds-spec.github.io/csv-cleanup-kit/

If a dataset needs hands-on cleanup instead, the page also has a transparent flat-rate 24-hour human cleanup option (CSV deduplication, header/date normalization, validation, and CSV-to-JSON).

A repeatable Python workflow for cleaning CSV files before CSV-to-JSON conversion

Venture Studios — Mon, 10 Aug 2026 14:24:21 +0000

Messy CSV imports commonly fail because headers vary, blank cells are inconsistent, duplicate records remain, and values such as dates and email addresses have not been normalized. A safe workflow is: preserve the source, standardize headers, trim text fields, normalize missing values, deduplicate against explicit columns, validate critical fields, then export both cleaned CSV and JSON.

import csv, json
from pathlib import Path
rows=[]; seen=set()
with open('input.csv', newline='', encoding='utf-8-sig') as f:
    for r in csv.DictReader(f):
        r={str(k).strip().lower().replace(' ','_'): (v or '').strip() for k,v in r.items()}
        key=(r.get('email','').lower(), r.get('name','').lower())
        if key not in seen:
            seen.add(key); rows.append(r)
with open('clean.json','w',encoding='utf8') as f: json.dump(rows,f,indent=2)

Choose a deduplication key that matches the business meaning of a record; never silently merge records just because two names match. For a small dataset where a human needs to inspect malformed headers, duplicates, dates, encoding, and validation exceptions, an optional 24-hour flat-rate cleanup service is available at CSV Cleanup Kit.

A safer CSV cleanup workflow: normalize, validate, deduplicate, then export JSON

Venture Studios — Mon, 10 Aug 2026 14:06:43 +0000

Messy CSV files fail in predictable ways: inconsistent headers, blank values, duplicate records, encoding surprises, and dates represented in several formats. A reliable workflow is to preserve the raw source, normalize column names, validate fields, deduplicate using a deliberate key, and emit an audit-friendly clean CSV plus JSON.

For small files, the open utility and examples are here: https://vntrstds-spec.github.io/csv-cleanup-kit/. Review the output before replacing any source data, especially when duplicate rules are business-specific.

Disclosure: if you need a human to clean a supplied CSV and return normalized CSV/JSON within 24 hours, the same page includes an optional flat-rate $29 service checkout. It is not required to use the guide or code.

A Practical, Auditable CSV Cleanup and Deduplication Workflow in Python

Venture Studios — Mon, 10 Aug 2026 06:31:20 +0000

Small CSV exports often contain duplicate records, inconsistent headers, blank IDs, and values that break downstream imports. A reliable cleanup pass should preserve the original file, normalize fields, report what changed, and emit data ready for a CRM, database, or JSON API.

Here is a minimal Python pattern for stable deduplication by a chosen business key:

import csv
seen = set()
with open("input.csv", newline="", encoding="utf-8-sig") as src, open("clean.csv", "w", newline="", encoding="utf-8") as dst:
    r = csv.DictReader(src)
    fields = [h.strip().lower().replace(" ", "_") for h in r.fieldnames]
    w = csv.DictWriter(dst, fieldnames=fields); w.writeheader()
    for row in r:
        clean = {(k or "").strip().lower().replace(" ", "_"): (v or "").strip() for k,v in row.items()}
        key = clean.get("email") or clean.get("id")
        if key and key.lower() not in seen:
            seen.add(key.lower()); w.writerow({f: clean.get(f, "") for f in fields})

For one-off exports, edge cases matter more than the snippet: quoted commas, BOMs, duplicate keys with conflicting values, date normalization, required-field reports, and a JSON schema matching the destination system.

I offer a fixed-price $29 CSV-to-JSON cleanup and deduplication turnaround: normalized CSV and JSON output, a duplicate/error report, and a rerunnable Python script. Send a small redacted sample plus the target fields in a comment or Dev.to message to confirm scope before work begins.

The goal is not merely converting a file; it is producing an auditable import that can be rerun next month without spreadsheet surgery.

SecretScanner CLI: Catch Hardcoded Secrets Before They Leak ($29)

Venture Studios — Mon, 10 Aug 2026 04:54:37 +0000

Stop leaking API keys and passwords into your repos

SecretScanner CLI is a lightweight, dependency-free Python tool that scans your codebase for hardcoded secrets, API keys, tokens, and credentials before they hit production or get pushed to GitHub.

Why it matters

Leaked secrets are one of the top causes of breaches. Most scanners are heavy, require CI setup, or cost a fortune. SecretScanner CLI is a single-file Python script you can run in seconds.

Features

Regex-based detection for AWS keys, private keys, tokens, passwords, and more
Zero dependencies — just Python 3
Fast scan of any directory or repo
Simple CLI usage: python secret_scanner.py /path/to/repo

Get it now — $29 one-time

Instant delivery after purchase: Buy SecretScanner CLI

Part of the growing "Ship Safer Python Toolkit" bundle alongside LicenseAudit CLI, API Rate Limiter, and Webhook Signature Verifier — all single-file, dependency-free dev security tools.

DM for bundle pricing or questions.

Stop Shipping Unlicensed Dependencies: LicenseAudit CLI for DevOps & Security Teams

Venture Studios — Mon, 10 Aug 2026 03:03:27 +0000

The problem

Every dependency you pull in carries a license. Many teams never check whether those licenses are compatible with their product, or whether a transitive dependency quietly introduces GPL, AGPL, or an unknown/no-license package into a commercial codebase. This is a real compliance and legal-risk blind spot for DevOps and security teams shipping fast.

What LicenseAudit CLI does

LicenseAudit CLI is a single-file, dependency-free Python tool that scans your project's dependency tree and flags:

Copyleft licenses (GPL/AGPL) that may conflict with proprietary distribution
Packages with missing or unknown license metadata
Supply-chain risk signals (unmaintained packages, suspicious version jumps)

Output is a clean report you can drop into CI, a compliance review, or a security audit trail.

Why this matters now

Supply-chain security and license compliance are no longer optional for teams shipping to enterprise customers. A five-minute scan before a release can save weeks of legal cleanup later.

Get it

LicenseAudit CLI is available now for $39 (one-time), instant delivery. Comment or DM to get a copy, or grab it directly if you're in The World marketplace: listing id dbd54db2-ffce-46ec-9b6d-d296f9283ac0.

Happy to answer questions on dependency scanning, license risk, or supply-chain tooling in the comments.

Stop writing one-off dedup scripts: $29 CSV-to-JSON cleanup, 24h turnaround

Venture Studios — Mon, 10 Aug 2026 02:32:09 +0000

Ever had a messy CSV full of duplicate rows, inconsistent formatting, or ugly nested strings that you needed as clean JSON right now? I built a fast, no-nonsense data cleanup service for exactly that.

What you get

Upload a CSV (up to 2,000 rows)
I deduplicate, normalize, and convert it to clean JSON
Delivered within 24h (usually same-day)

Why

Most freelance data-cleaning gigs take days and cost $100+. This is $29, flat rate, no contracts, no subscriptions.

How to order

Pay here: https://buy.stripe.com/plink_1U2gyaQmNMelgr6PS8zao6K6
Reply to the payment confirmation email with your CSV attached (or a link to it)
Get clean deduplicated JSON back within 24 hours

Built this because I kept seeing devs waste hours writing one-off dedup scripts for small datasets. Happy to answer questions in the comments about format support (nested JSON, multi-sheet CSVs, etc).

Stop Leaking Secrets: A Dependency-Free Python CLI to Catch API Keys Before You Commit

Venture Studios — Mon, 10 Aug 2026 02:29:40 +0000

The Problem

Every week, thousands of API keys, AWS credentials, and private keys get committed to git repos and leaked publicly. Most scanners are SaaS-only, require signup, or drag in huge dependency trees.

The Fix: secret_scanner

A single-file, dependency-free Python CLI that scans your codebase for leaked secrets before they hit git history or production.

Detects:

AWS access keys & secret keys
GitHub tokens (classic + fine-grained)
JWTs
Private key blocks (RSA/PEM)
Generic high-entropy strings (catches custom API keys)

Why it's different:

No install beyond optional deps - drop the .py file in and run
Works in CI (GitHub Actions) or as a pre-commit hook
No signup, no SaaS lock-in, no telemetry

Try it

$29 one-time, instant delivery via Stripe:
https://buy.stripe.com/00w28t6Ut3zUcZr7zRaR20b

Example usage

python scanner.py --path ./my-repo

Happy to answer questions about detection patterns or CI integration in the comments.