Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/dynatrace-oss/dynatrace-snowflake-observability-agentnpx agentmods add skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflow)<a href="https://agentmods.dev/skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflow"><img src="https://agentmods.dev/badge/skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflow"><img src="https://agentmods.dev/badge/skills/dynatrace-oss/dynatrace-snowflake-observability-agent/dynatrace-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.01433 |
| Opus 5 | $0.00009 | $0.00717 |
| Sonnet 5 | $0.00004 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
dynatrace-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Dynatrace Workflow Creation and Deployment
Use this skill to create and deploy Dynatrace automation workflows (anomaly detection, alerting, scheduled DQL actions) for DSOA.
File Location
docs/workflows/<workflow-name>/<workflow-name>.yml
docs/workflows/<workflow-name>/readme.md
docs/workflows/<workflow-name>/img/.gitkeep
docs/workflows/README.md (index — update after every new workflow)
Workflow YAML Format
Export an existing workflow as reference before writing a new one:
dtctl get workflows -A # list all workflows with IDs
dtctl get workflow <id> -o yaml # export one as a structural template
Typical workflow YAML structure:
# WORKFLOW: <Human-readable title>
# DESCRIPTION: <One-line description>
# OWNER: DSOA Team
# PLUGINS: <comma-separated plugin names>
# TAGS: snowflake, dsoa, <domain>
id: <uuid> # omit on first create; add after dtctl returns the ID
title: "DSOA — <Descriptive Name>"
description: |
<What this workflow does and why>
trigger:
# Option A — scheduled (cron)
schedule:
rule: "0 * * * *"
timezone: UTC
# Option B — event-driven
# event:
# eventType: "davis.problem.opened"
# filter: "event.category == 'AVAILABILITY'"
tasks:
step_query:
name: Query anomaly data
action: dynatrace.automations:run-javascript
input:
script: |
import { queryExecutionClient } from '@dynatrace-sdk/client-query';
export default async function () {
const result = await queryExecutionClient.queryExecute({
body: {
query: `
fetch logs
| filter db.system == "snowflake"
| filter dsoa.run.plugin == "<plugin>"
| summarize count = count(), by: { snowflake.<dim> }
| filter count > 0
`,
requestTimeoutMilliseconds: 30000
}
});
return { anomalyCount: result.result?.records?.length ?? 0, records: result.result?.records };
}
step_notify:
name: Send alert notification
action: dynatrace.slack.connector:send-message
conditions:
- taskId: step_query
condition: "{{ result('step_query').anomalyCount > 0 }}"
input:
channel: "#dsoa-alerts"
message: |
:warning: DSOA anomaly detected
Count: {{ result('step_query').anomalyCount }}
Details: {{ result('step_query').records | dump }}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 196 lines · 18 tokens per session scan A e532289a16bc
dynatrace-workflow is a skill published in the GitHub repository dynatrace-oss/dynatrace-snowflake-observability-agent (10 stars, last pushed 8d ago), licensed MIT. It adds 18 tokens to every session and 1,433 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-02.
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