Skip to content
TrustedSkillsbeta
Back to all skills

Network Analysis

Checked
aj-geddes·vlatest·other·MIT

Identifies key connections, influencers, and vulnerabilities within complex networks using graph theory and data analysis.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it Network Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Network Analysis" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/network-analysis/SKILL.md
Scanned
3 files, 311 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

This skill enables analysis of network structures using graph theory and data analysis techniques. It identifies key connections, influential nodes, and community structures within networks. The tool can be used to map relationships in social networks, organizational hierarchies, or other interconnected systems, providing insights into their dynamics and vulnerabilities. It leverages Python libraries like NetworkX for these analyses.

When to use it

  • Analyzing social networks to identify influential users and community structures.
  • Mapping organizational hierarchies and identifying key connectors or bottlenecks.
  • Studying citation networks to find impactful research papers and collaboration patterns.
  • Building recommendation systems based on network relationships.
  • Analyzing supply chain networks to optimize logistics and identify vulnerabilities.

Key capabilities

  • Calculates various centrality measures (Degree, Betweenness, Closeness, Eigenvector).
  • Identifies communities within a network.
  • Determines the clustering coefficient of a network.
  • Provides insights into node importance and control over paths.
  • Uses Python libraries like NetworkX for analysis and visualization.

Example prompts

  • "Analyze this social network data to identify the most influential users."
  • "Map out the organizational structure and highlight key connectors."
  • "What are the main communities within this citation network?"

Tips & gotchas

  • Requires providing network data in a suitable format (e.g., as a graph).
  • The skill uses Python, so familiarity with basic Python concepts may be helpful for interpreting results or customizing analyses.