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-genaigit 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-genai)<a href="https://agentmods.dev/skills/dynatrace/dynatrace-for-ai/dt-obs-genai"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-obs-genai/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-genai"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-obs-genai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00053 | $0.04265 |
| Opus 5 | $0.00026 | $0.02132 |
| Sonnet 5 | $0.00011 | $0.00853 |
| Haiku 4.5 | $0.00005 | $0.00426 |
Grade A, and why
dt-obs-genai 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 12d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Observability (GenAI) Skill
Analyze AI Observability signals from customer GenAI applications using DQL — golden signals, LLM signals, token and cost analytics (with usage attribution and prompt-caching economics), agent signals (including loop/runaway detection and Smartscape topology), conversation/session-level analytics, guardrails, and evaluation quality.
When to Use
Use this skill for observability questions about customer GenAI applications — anything
reading OpenTelemetry GenAI spans (gen_ai.*) or LLM evaluation bizevents. Example triggers:
- "LLM latency", "error rate by model"
- "token usage by model", "token throughput / TPM", "am I hitting rate limits", "provider throttling or 429s"
- "cost by model and provider", "who is driving token spend", "do I have prompt caching"
- "cost per conversation", "most expensive sessions"
- "failing agent tool calls", "find runaway agents"
- "responses truncated or blocked", "finish reasons"
- "failed evaluations", "quality scores"
When NOT to Use
Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI distributed tracing (dt-obs-tracing).
Example Questions
When suggesting follow-up questions (e.g., "give me one example question per topic"), use these canonical, ready-to-ask phrasings — one per capability. Keep suggestions single-clause and avoid the literal phrase "content filter" (use "blocked or safety-filtered" instead); overly long, multi-clause questions can be rejected.
- Traffic, errors & latency: What is my LLM error rate and p95 latency by model in the last 24 hours?
- Token usage & cost: Break down token usage and cost by model and provider in the last 24 hours.
- Agent & tool activity: Show me failing agent tool calls in the last 24 hours.
- Conversation analytics: What are my top 10 most expensive conversations by total token usage in the last 24 hours?
- Guardrails: How many responses were blocked or truncated in the last 7 days, and which model was affected most?
- Evaluation quality: Show me failed evaluations from the last 24 hours with the judge's explanation and the question-answer pair.
What ships with it
7 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.
- 12d ago First seen · 334 lines · 53 tokens per session scan A def0e517f15d
dt-obs-genai is a skill published in the GitHub repository Dynatrace/dynatrace-for-ai (137 stars, last pushed 2d ago), licensed Apache-2.0. It adds 53 tokens to every session and 4,265 once invoked, about $0.0003 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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