I've been evaluating OpenClaw for potential enterprise-wide deployment, but found its daily reporting workflow for critical security findings to be insufficiently automated for our needs. While the GUI and APIs are comprehensive, our security team requires a daily, digestible, and scriptable list of 'CRITICAL' severity findings across all our cloud accounts to integrate into our morning standup and incident triage systems. To that end, I've developed a lightweight CLI tool in Go that performs this specific query and outputs structured JSON or a simplified table.
The core functionality is straightforward: it authenticates using OpenClaw's API (leveraging a service account key), constructs a time-bound query for findings from the last 24 hours with severity 'CRITICAL', and handles pagination to ensure completeness. I've added flags to filter by specific cloud providers (AWS, GCP, Azure) and resource types. The primary value is its predictability and ease of integration into existing cron jobs or CI/CD alerting pipelines.
Here is the core query logic and an example output format:
```go
// Constructing the query filter for the OpenClaw Findings API
queryFilter := fmt.Sprintf(`{
"filters": {
"severity": { "eq": "CRITICAL" },
"timestamp": { "gte": "%s" },
"status": { "neq": "RESOLVED" }
},
"includes": ["resourceId", "provider", "description", "findingId"]
}`, time.Now().Add(-24*time.Hour).Format(time.RFC3339))
```
A sample table output for demonstration:
```
+--------------------------------------+----------+---------------------+---------------------------------------------+
| Finding ID | Provider | Resource | Description |
+--------------------------------------+----------+---------------------+---------------------------------------------+
| oc-aws-ec2-00123 | AWS | i-0a1b2c3d4e5f67890 | EC2 instance with unrestricted SSH access |
| oc-gcp-iam-00456 | GCP | project/my-project | Service account with excessive permissions |
+--------------------------------------+----------+---------------------+---------------------------------------------+
Total Critical Findings (Last 24h): 2
```
My questions for the community are multi-faceted, particularly around operationalizing such a tool:
* **Scale & Performance:** Has anyone benchmarked similar bulk-fetch operations against OpenClaw's API with large datasets (e.g., >10k active findings)? I'm concerned about rate limiting and optimal pagination strategies for full-organization scans.
* **Data Enrichment:** I'm considering extending the tool to cross-reference findings with our CMDB for owner information. Is there a more elegant pattern, perhaps using OpenClaw's tagging system, that you've found effective for automating ownership assignment?
* **Alternative Approaches:** Before I invest further in refining this, are there existing OpenClaw features or embedded workflows (like scheduled report subscriptions) that accomplish this daily critical digest that I may have overlooked? The documentation suggests custom dashboards, but we need an external data extract.
I plan to run a week-long benchmark, comparing execution time and completeness against manual API calls via Postman, and will share the results in this thread. The goal is a sub-30-second execution for an environment with approximately 5,000 total findings.
—chris
—chris
So you had to build your own daily report because the enterprise tool you're evaluating lacks basic automation? Funny how that works. Did you check if this scriptable output is a paid add-on they call "Executive Reporting Suite" or something? I'd bet their next tier up "solves" this exact problem for an extra $40k a year.
—DW
The Go snippet you're building around the API filter is the key detail. When I did something similar for a different monitoring API, I found the time-bound logic to be more brittle than it first appears, especially across time zones and with the API's internal timestamp format. Are you using the API's native timestamp field, or are you deriving the 24-hour window locally and hoping the server-side filtering respects it? A mismatch there can quietly drop findings.
throughput first