git clone --depth 1 https://github.com/NVIDIA/skills /tmp/doca-argus && cp -r /tmp/doca-argus/skills/doca-argus ~/.claude/skills/doca-argusSKILL.md
# DOCA Argus Service > **Currently-promoted successor.** DOCA Argus is NVIDIA's primary, > currently-promoted framework for runtime threat detection and host > memory forensics on BlueField. It **supersedes the older, > library-based DOCA App Shield approach** (the DOCA App Shield > library is **not covered by this bundle** — it is policy-excluded > from the public release; see [AGENTS.md `## Non-goals`](../../../AGENTS.md#non-goals-questions-the-agent-should-recognize-and-refuse-politely) > item 7 and route to the public docs via > [`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md)). > When a request is "introspect host processes / detect suspicious > activity / runtime security" and asks for the *currently-supported* > choice, **Argus is the answer to name first**; the App Shield > library is the lower-level fallback only for genuinely custom > DPU-side tooling Argus cannot express, and it lives outside this > bundle. **Where to start:** This skill is for *operating* the DOCA Argus Service container, not for *linking against* a library. Argus is the packaged security agent that ships as a container and surfaces findings on its API / dashboard / forwarded SIEM; it is *not* a host-side agent the user installs as a host package, *not* a programming surface, and *not* the same thing as the DOCA App Shield library (the *lower-level* introspection library a developer would use to BUILD custom security tooling — Argus is what most operators want INSTEAD; the App Shield library is not covered by this bundle). If the user wants to *deploy* the Argus container, open [`TASKS.md`](TASKS.md) and start at [`## configure`](TASKS.md#configure). If the question is *what shape of service is Argus, what does it detect, and how does it expose findings*, start at [`CAPABILITIES.md`](CAPABILITIES.md). If DOCA is not installed on the BlueField yet, route to [`doca-setup`](../../doca-setup/SKILL.md) first. If the user's real question is *"I want to write a custom security tool against host kernel state from the BlueField side"*, the right answer is **not** this skill — that is the DOCA App Shield library, which is not covered by this bundle; route the user to the public docs via [`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md) instead. ## Example questions this skill answers well The CLASSES of Argus questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance. - **"For a production BlueField security workflow, do I deploy Argus, or do I build my own on top of the DOCA App Shield library?"** — worked example: *"I want runtime security on a fleet of BlueField-3s protecting a production database tier; what should I reach for first?"*. Answered by the Argus-vs-App-Shield path-selection rule in [`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy) + the path-selection step in [`TASKS.md ## configure`](TASKS.md#configure). - **"What four configuration axes do I have to decide before starting the Argus container?"** — worked example: *"production host monitored by Argus, findings forwarded to Splunk, low false- positive budget"*. Answered by the four-axis configuration table in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the four-axis step in [`TASKS.md ## configure`](TASKS.md#configure). - **"Argus's container is running but I see no findings — what did I miss?"** — worked example: *"container green, no findings have arrived in 24h"*. Answered by the detection-policy and sampling rows in [`CAPABILITIES.md ## Error taxonomy`](CAPABILITIES.md#error-taxonomy) + the layered ladder in [`TASKS.md ## debug`](TASKS.md#debug). - **"I am getting hundreds of findings an hour and they look like noise — is Argus broken?"** — worked example: *"too many findings; security ops is starting to ignore the channel"*. Answered by the calibration-period and detection-policy rules in [`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy) + the layered ladder in [`TASKS.md ## debug`](TASKS.md#debug). - **"How do I pair Argus with my existing SIEM (Splunk / ELK / …)?"** — worked example: *"forward findings to Splunk for the security ops team to review"*. Answered by the forwarding-axis row in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the forwarding step in [`TASKS.md ## configure`](TASKS.md#configure). - **"My Argus deployment is impacting the workload's performance — what do I tune?"** — worked example: *"production host CPU is up noticeably since Argus started"*. Answered by the sampling-axis row in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the sampling-tuning row in [`TASKS.md ## debug`](TASKS.md#debug). ## Audience This skill serves **external security operators and platform teams who deploy the DOCA Argus Service container** to get runtime security on a BlueField + host pair, with findings flowing into the team's existing SIEM. Concretely: people running the Argus container on BlueField Arm, choosing its detection policy / forwarding destination / sampling / host coverage from the public Argus guide, wiring the SIEM-side ingest so findings reach the security ops team, and validating the end-to-end pipeline before trusting the channel for production-grade decisions. It is **not** for NVIDIA developers contributing to Argus itself, and it is **not** a programming guide for *building security tools on top of* DOCA libraries (that is [`doca-programming-guide`](../../doca-programming-guide/SKILL.md) plus the matching `libs/<library>` skill — and for the App Shield library that custom security tooling builds on, the public docs, since App Shield is not covered by this bundle). Argus is a **service**, not a library: the operator runs a container and consumes findings via
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.