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 Lzy599775/agent-auto-sci-skills --skill networkxgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/networkx)<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/networkx"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/networkx/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/lzy599775/agent-auto-sci-skills/networkx"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/networkx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00084 | $0.04182 |
| Opus 5.5 | $0.00034 | $0.01673 |
| Sonnet 5.5 | $0.00017 | $0.00836 |
| Haiku 4.5 | $0.00008 | $0.00418 |
Grade A, and why
networkx 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 13d 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.
This is a copy
91% identical to networkx — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NetworkX
Overview
NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities.
This skill targets tested NetworkX 3.7 (Python >=3.12, excluding 3.14.1). Several pre-3.0 APIs (nx.info, nx.write_gpickle, nx.read_shp) and the 3.4-era nx.random_tree no longer exist — current replacements are used throughout this skill.
Review sources, executed examples, optional-dependency limits, and reproducibility notes are in references/review.md. Reference snippets are API patterns: supply the stated graph type and input files; they are not one sequential script.
When to Use This Skill
Invoke this skill when tasks involve:
- Creating graphs: Building network structures from data, adding nodes and edges with attributes
- Graph analysis: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
- Graph algorithms: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
- Network generation: Creating synthetic networks (random, scale-free, small-world models) for testing or simulation
- Graph I/O: Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices)
- Visualization: Drawing and customizing network visualizations with matplotlib or interactive libraries
- Network comparison: Checking isomorphism, computing graph metrics, analyzing structural properties
Core Capabilities
1. Graph Creation and Manipulation
NetworkX supports four main graph types:
- Graph: Undirected graphs with single edges
- DiGraph: Directed graphs with one-way connections
- MultiGraph: Undirected graphs allowing multiple edges between nodes
- MultiDiGraph: Directed graphs with multiple edges
What ships with it
6 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.
- 13d ago Changed · +20 lines · +1 tokens per session 981a213f33dd
- 1mo ago Changed · +16 lines 60895e38e1d4
- 1mo ago First seen · 441 lines · 83 tokens per session scan A 0ecb45e64a64
networkx is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 9d ago), licensed MIT. It adds 84 tokens to every session and 4,182 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to networkx, differing in 0 lines, and is treated as a copy.
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