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Jacobson Network Performance

Checked
copyleftdev·vlatest·ai-ml·Apache-2.0

Analyzes network performance data using Jacobson's algorithm to identify congestion points and optimize throughput.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add copyleftdev/sk1llz --skill jacobson-network-performance -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

copyleftdev

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned copyleftdev/sk1llz at 92361f525f.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
92361f525f
Manifest
domains/networking/jacobson/SKILL.md
Scanned
1 file, 592 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The jacobson-network-performance skill analyzes network performance data and provides insights based on Jacobson's diffusion model. It can calculate metrics like packet delay variation (PDV) and estimate network conditions, allowing for proactive adjustments to improve application responsiveness and user experience. This skill is particularly useful in environments where consistent low latency is critical.

When to use it

  • Troubleshooting slow application performance: Identify if network congestion or variability is contributing to perceived slowness.
  • Optimizing real-time applications: Fine-tune settings for online games, video conferencing, or financial trading platforms that demand minimal delay.
  • Predictive maintenance of network infrastructure: Anticipate potential bottlenecks and schedule upgrades before they impact users.
  • Analyzing the impact of new deployments: Quantify how changes to network topology affect overall performance.

Key capabilities

  • Calculates Packet Delay Variation (PDV)
  • Applies Jacobson's diffusion model for analysis
  • Provides insights into network conditions
  • Supports various data input formats

Example prompts

  • "Analyze this packet capture and tell me the PDV."
  • "Based on these latency measurements, what is the estimated network condition?"
  • "Can you identify any patterns in this network performance data that might indicate congestion?"

Tips & gotchas

The skill requires a dataset of network performance metrics (e.g., packet capture or latency measurements) as input. Accuracy depends heavily on the quality and completeness of the provided data; noisy or incomplete datasets will yield less reliable results.