Doca Pcc Counters

What it does

This source provides a task-specific instruction workflow for Automation & agents. Its source file is linked below so you can inspect the exact procedure, tools and dependencies. A full editorial review has not been completed.

Best for

Exploring Automation & agents workflows with an agent. Confirm that the source matches your task and environment.

Tradeoffs and requirements

The automated catalog record does not establish task performance or runtime safety. Check supporting scripts and external services in the source before use.

Requires DOCA/MFT on Linux with a ConnectX-6 or newer (not ConnectX-8) or BlueField device, mst tools, mounted debugfs, and root access to the mlx5 diag_cnt interface. The script reads fixed firmware and hardware diagnostics independently of custom PCC code. Its set operation changes which counters are collected, not congestion-control or forwarding behavior; treat later fleet tuning as a separate high-stakes action. The validation steps require a live DOCA install.

Install from the source

Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code. Catalog ingestion never executes skill instructions or install scripts.

npx skills add https://github.com/NVIDIA/skills/tree/25340619336e89bcc40365c28bfe9e790430468e/skills/doca-pcc-counters --skill doca-pcc-counters

Review pinned skill folder ↗ · CLI documentation ↗

After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.

Safety & review signals

Static review only. This is not a security audit or a guarantee of safety.

SKILL.md text only; scripts, dependencies and runtime behavior not audited.

• Shell Or Package Install — review the source context.

Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.

AI Craft Pad Skill Score

86/100 · Metadata coverage 100% · Model 1.0

Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.

Documentation

100/100

100

Maintenance

100/100

100

License Clarity

100/100

100

Repository Health

100/100

100

Compatibility Evidence

80/100

80

Popularity

64/100

64

Originality

80/100

80

Static Safety

60/100

60