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-sec-contextualizationgit 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-sec-contextualization)<a href="https://agentmods.dev/skills/dynatrace/dynatrace-for-ai/dt-sec-contextualization"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-sec-contextualization/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-sec-contextualization"><img src="https://agentmods.dev/badge/skills/dynatrace/dynatrace-for-ai/dt-sec-contextualization.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 | $0.00229 | $0.02142 |
| Opus 5 | $0.00114 | $0.01071 |
| Sonnet 5 | $0.00046 | $0.00428 |
| Haiku 4.5 | $0.00023 | $0.00214 |
Grade A, and why
dt-sec-contextualization 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Contextualization Skill
Resolve security signals and entity attribute sets to runtime Dynatrace
Smartscape entities, summarize findings across entity levels, and connect
signals that land on different levels (e.g. a detection on a K8S_POD vs.
a CVE on a KUBERNETES_NODE) via a shared runtime entity.
What This Skill Covers
- Identity → Smartscape mapping — given a row carrying any of
dt.smartscape_source.id,container_image.digest,container_image.id,host.ip,dt.entity.*, ork8s.*fields, resolve it to a Smartscape entity at any requested level (CONTAINER / K8S_POD / workload / K8S_NODE / HOST / cloud /GENAI_SERVICE— AI/GenAI workloads). - Artifact → runtime bridge —
container_image.digest→smartscapeNodes CONTAINER→is_part_of.*→ parent workload orruns_on.host→ HOST. Works without pre-enricheddt.smartscape_source.id. - Cross-level correlation — tiered entity matching to determine whether two findings (e.g. a detection and a CVE from different legs) relate through a shared runtime entity. Tier 1: exact entity id match; Tier 2: same workload/pod/host by name; Tier 3: same namespace/cluster (context-only — does not contribute to scoring).
- Pod → node topology — resolve
K8S_PODto itsK8S_NODEviak8s.node.name(co-projected field) or Smartscape edge traversal. Enables "detection hit pod X — does that pod run on a vulnerable node?" - Coverage match recipes — 2-way and 3-way container→workload match patterns shared across dt-sec-insights coverage counting queries.
- Entity enrichment — given findings, IoC matches, or raw Smartscape nodes, produce per-entity risk-level breakdowns and entity-key bundles for downstream scoring.
- IoC enrichment — attribute an already-matched IoC (IP / domain / URL /
email / CVE / hash / MITRE TTP) with adversary context (actor, malware family,
MITRE technique, targeting, provider) by reverse-looking-up the ingested
THREAT_REPORTevents whose observable arrays contain that IoC.
What ships with it
4 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 · 132 lines · 229 tokens per session scan A cb43fb4888ca
dt-sec-contextualization is a skill published in the GitHub repository Dynatrace/dynatrace-for-ai (137 stars, last pushed today), licensed Apache-2.0. It adds 229 tokens to every session and 2,142 once invoked, about $0.0011 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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