Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-analyticsgit clone --depth 1 https://github.com/Dynatrace/dynatrace-for-aiWrote 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/dynatrace-for-ai/dt-obs-analytics)<a href="https://agentmods.dev/skills/dynatrace/dynatrace-for-ai/dt-obs-analytics"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-obs-analytics/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/dynatrace-for-ai/dt-obs-analytics"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-obs-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00186 | $0.04226 |
| Opus 5 | $0.00093 | $0.02113 |
| Sonnet 5 | $0.00037 | $0.00845 |
| Haiku 4.5 | $0.00019 | $0.00423 |
Grade A, and why
dt-obs-analytics 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 11d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics — Dashboard & Notebook Query Extraction
A pipeline of three platform JavaScript scripts under scripts/ — two extractors feeding a shared analyzer runner:
scripts/extract-timeseries-dashboard.js ──┐
scripts/extract-timeseries-notebook.js ──┴──► queryset.json ──► scripts/run-analyzer.js (any Davis analyzer)
Each script is invoked via:
dtctl exec function -f scripts/<script>.js --payload '<json>' -o json
-o json wraps the function return value under .result. When you need to inspect the result, read it directly from the output — no jq required. Pass the full raw output as queries to run-analyzer.js and it unwraps automatically.
Parsing a dashboard URL
When the entry point is a Dynatrace dashboard URL, extract the three components the scripts need:
https://<tenant>/ui/apps/dynatrace.dashboards/dashboard/<ID>#from=<from>&to=<to>&vfilter_<name>=<val>...
| URL part | Script destination |
|---|---|
Path segment after /dashboard/ (before #) |
id in extract-timeseries-dashboard.js payload |
#from= value (URL-decode %3A → :) |
timeframe.startTime in run-analyzer.js (only needed if running analysis) |
#to= value (URL-decode) |
timeframe.endTime in run-analyzer.js (only needed if running analysis) |
vfilter_<name>=<value> params |
variables map in run-analyzer.js (strip vfilter_ prefix) |
The timeframe in the URL fragment is the dashboard's display window. It is not injected into the extracted DQL — the extractor returns the DQL verbatim with its original $variable tokens and any embedded | timeframe clauses intact. Use the parsed from/to values only when calling run-analyzer.js to set the analysis window. If the user just wants to list the queries, the timeframe is informational only.
Note: when you pass run-analyzer.js an absolute timeframe.startTime (not a now... expression), it strips any embedded | timeframe ... stage from the DQL before analysis, so the analyzer honors your requested window rather than the query's baked-in one. For relative (now...) windows the embedded | timeframe is left intact. This means the query actually analyzed can differ from the extracted text — expected behavior, noted here so results line up with the window you asked for.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 299 lines · 186 tokens per session scan A c7552cc90197
dt-obs-analytics is a skill published in the GitHub repository Dynatrace/dynatrace-for-ai (137 stars, last pushed today), licensed Apache-2.0. It adds 186 tokens to every session and 4,226 once invoked, about $0.0009 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 2026-08-30.
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