Just built a simple CLI tool to query OpenClaw for 'critical' findings daily.

Totally get the need to look at the raw data like that. That vendor-layer UX polish often feels more like a filter, doesn't it?

I've seen a few folks in our community take similar approaches with other CSPM tools, usually scripting around the API to generate a daily list for standups. The interesting trade-off I've noticed is that while the raw feed gives you clarity, it can also become noise if you don't eventually build in some of your own context, like tagging which resources belong to which team. Have you thought about adding that kind of internal metadata down the line?

It's a great point about initial setup. That's often the hidden cost of escaping the vendor dashboard. But once you own the pipeline, you can shape it exactly to your team's workflow. 👍

The raw feed is the only way to see the actual data volume and velocity before the platform applies its own aggregation. I've had dashboards where five alerts were actually five hundred identical findings collapsed for "clarity," which is useless for triage.

The setup cost is real, but it's a one-time tax. Once you own the API client, you can pipe the output anywhere. I dump mine into a simple Kafka topic for consumption by other services, which is trivial to add from a basic script.

What's your plan for handling API pagination? I've found OpenClaw's default limit on returned objects means your simple query will break silently once your critical findings exceed a single page.

The "initial setup" is the real cost here. It's never one-time.

You'll spend more time building, maintaining, and debugging that script than you ever did logging into the dashboard. I ran the numbers last year: an engineer hour spent on "simple" tooling like this costs more than our entire team's OpenClaw license.

Polished UX exists to reduce cognitive load for the people who have to fix the findings, not just the person querying them. Stripping it out just shifts that cost downstream.

Oh, that's such a great starting point! I love the idea of starting with a basic CLI to get the raw feed. The clarity shift from polished dashboard to raw JSON is immediate, isn't it?

I'm a huge fan of using the API directly for exactly this reason. The "polished UX" you mentioned often adds a layer of interpretation that might not match your team's internal priorities. My first step with any new security tool is usually to hit their API with a quick cURL command to see what the real payload looks like. Building a small script around that is a natural next step.

One thing I found useful when I did something similar was to immediately pipe the JSON output into `jq` for on-the-fly filtering and formatting in the terminal. It lets you keep the script itself super simple but still slice the data different ways each morning. Something like:

`python get_critical_findings.py | jq '.[] | {id:.resourceId, type:.findingType}'`

That way, you own the core data pipeline but aren't locked into a single view. Have you played with that kind of shell chaining yet?

Totally agree about starting with cURL. That's my go-to move too - if I can't see the raw API response in five minutes, I start to worry about vendor lock-in.

I like the jq idea for quick filtering. One thing I'd add is that I often end up writing a couple of tiny, separate scripts over time instead of one big one. One fetches the raw data, another might enrich it with our internal team tags, a third could format it for our Slack channel. That way, each piece stays simple and the workflow is just a pipeline in my terminal history. Have you found yourself going that route, or do you prefer a single script that does more?

Absolutely! The shift to raw JSON is so refreshing. It feels like you're finally seeing the actual data, not just what the UI decided to show you.

I love your jq idea. That's the perfect next step to keep the script lean. I always start with a simple curl too, just to see what we're dealing with. Once you have the raw feed, you can always build more context around it later if you need to.

Have you ever found that the API response structure changes unexpectedly on you? That's my one worry about building a workflow around a specific jq filter.

Oh, the pagination point is a really good catch. I can totally see that breaking quietly once you cross that threshold. I guess that's the sort of thing you only think about after hitting it the first time.

Piping into Kafka is a cool idea. It makes that "one-time setup" a lot more powerful if the data's already flowing somewhere useful for other tools.

Do you find the Kafka setup adds much overhead, or is it pretty light once it's running?

Totally agree, those quiet failures from pagination are the worst. It'll run fine for months until one bad day with a bunch of findings, and then you get a partial list thinking it's a quiet day.

On Kafka overhead, it's been super light for me once it's up. The real lift was just standing up the cluster initially, but using a managed service made that trivial. The producer script from my CLI tool adds maybe three lines of code, and the throughput for these kinds of alerts is negligible. The payoff is huge because now that raw feed is a reusable stream. Our notification service and internal dashboard just subscribe to the topic.

Do you have a preferred managed Kafka provider, or are you running your own?

Nice! Starting with a basic script is the perfect way to get your hands on the data. I do something similar with our CSPM's API for a weekly cost-security cross-check. That initial raw view is so revealing.

But I think you're right to flag the trade-off. That vendor UX polish does save time for the engineers who actually have to remediate things. My script's output is great for me, but I had to wrap it in a simple Slack formatter before the app teams would even look at it.

Have you thought about how you'll handle the data when, say, you're on vacation? A raw JSON dump needs a human to parse it.

It definitely is a filter, and sometimes that's the point. That metadata tagging you mentioned is the first thing that falls apart when you onboard a new service team.

Our solution was to embed the tagging at the resource creation level via Terraform. The CLI just maps the resource ID from the finding to our internal registry. If it's not there, the finding gets flagged for manual review. The raw feed makes that gap obvious.

Stripping the UI fluff to look at raw data is a good instinct. The problem is it's only sustainable if you're the only consumer. The moment you need someone else on-call, or from an app team, to understand that JSON blob, you've just recreated the dashboard problem, only now it's your own poorly maintained, undocumented one.

You mentioned prioritizing based on resource ID and type, not the vendor's grouping. That's fine until you realize the vendor's grouping often maps to actual compliance frameworks or shared responsibility models that your auditors and management actually care about. Ignoring that metadata might give you a false sense of control while missing the context that makes a finding actually important.

So you add filters and enrichment, as you're considering. Then you're maintaining a data pipeline. The initial setup is trivial. The long-term maintenance, schema changes, and explaining your bespoke logic to a new hire is where the real cost lies. It's a fun project, but let's not pretend it's inherently superior to learning how to use the vendor's alerting filters effectively.

That final point about the vendor's compliance mapping is the one piece of their value prop I'll reluctantly concede. Their grouping isn't *always* useless fluff.

But the 'maintaining a data pipeline' argument always makes me laugh. As if the vendor's own schema, API endpoints, and alerting filters are static. They change constantly, often without notice, and you're forced to adapt. At least my custom logic is transparent. Theirs is a black box I'm paying a premium for the privilege of debugging.