u/JSChronicles

First off, I just want to say that this is not AI generated and I am genuinely asking a question on how to properly share a project that you're actually excited on and that actually has real world usage without being tossed into a void or told this is just AI slop.

How do you gain project recognition or share anymore? I wanted to share one of my projects that I've been working on for months as an example.

I came to the subreddit thinking I would be able to just simply share my project and I got a warning saying that showcasing is no longer allowed because of the problem with tooling being just generated en masse at an infathomable speed. People were complaining so much, rightly so, that the subreddit laid down new rules and so did other subreddits.

I followed the rules on each subreddit and posted into basically a void for projects thinking that people would want detailed and professional sounding wording and instead I was met with "another AI slop tool" from multiple people. People left comments saying I was using buzzwords, except those words are used to describe the actual technical definition, such as "bounded concurrency".

I thought that I should pay attention to my grammar and make sure it sounds decent, instead I was DM'd that I should just quit making AI generated content.

My project is literally used in two companies right now to help speed up AWS governance and security. I use my own project for my own AWS organization and accounts that I own. I figured people would like to have an easier "control plane" via python for AWS but that wasn't well received.

I guess just finally sharing a tool I’ve been working on for a while. I’ve been using it at work and improving it over the past several months.

The tool is called Anvil.

Anvil is a declarative AWS execution engine for running Python tasks across AWS accounts and regions.

It's built to help teams run repeatable AWS workflows across organizations, accounts, and regions: inventory, validation, enforcement, cleanup, reporting, and similar operational work. It also works well for ad hoc tasks like updating trust relationships, counting resources, removing IAM users, or finding inactive access keys.

- Works for org admins, but also for direct access to one account or a small set of accounts.

- Runs across targeted org accounts quickly and returns structured logs/results for coverage-focused security work.

- Uses YAML for workflow definition and plain Python files for task logic.

- Handles auth, role assumption, account filtering, dependencies, regions, orgs, concurrency, fail-fast, and results.

- Uses the normal `boto3` credential chain: profiles, env vars, SSO, instance roles, etc.

- Passes each task the account ID, account name, region, metadata, and authenticated AWS session.

- Handles the management account session separately when AWS Organizations discovery is needed.

One part that I'm personally ecstatic on is the result scanning. After a run, Anvil can scan the structured results and identify failed accounts. You can then rerun just those failed accounts without manually editing the original YAML file.

Example:

# View failed results from one explicit run results file.
anvil results --status failed --results-file ./results/orgs/2026-05-01T183012Z/results.jsonl

# Rerun only the failed accounts from that results file.
anvil results --status failed --results-file ./results/orgs/2026-05-01T183012Z/results.jsonl --rerun

The result files are JSONL, so scanning stays fast even when the output is large. In my testing, Anvil can pull the needed failure information from a 139,000+ line result file in under 500ms, so you are not stuck waiting just to figure out what failed.

There is a template repo It includes setup instructions, multiple workflows, and agent skills to help create or modify plugin tasks.

Anvil’s usefulness is not limited to whatever ships in the next release. It ships with stock tasks for some AWS operations, and plugin tasks let teams add their own policies, naming rules, reports, cleanup logic, or internal platform behavior as normal Python files. The YAML stays consistent whether you’re running stock inventory/validation work or organization-specific logic. The difference is only where the task implementation lives.

There is still more work to do, and I’m adding more packaged tasks so people have more useful building blocks out of the box.

If Anvil itself feels like too much tooling for your use case, I also created a Standalone Multi-Account Script Template. It is basically a smaller template for running one task across accounts, where you replace a small part of the script with your own logic. You do not get all the extra pieces Anvil provides, but it can still give a big performance improvement over sequential account-by-account scripts.

Note: PyPI is currently blocked because the anvil package name appears to be tied up by an abandoned project, so for now installation points directly at the repo:

dependencies = [
"anvil @ git+https://github.com/JSChronicles/anvil.git@3d60bb2ae8b3495d31a2e458eba7006d393659d8",
]

I’m interested in feedback from people in general.