Skills networkx

📦

networkx

Name: networkx
Author: davila7

Content revision r2 High Risk 📁 Filesystem access⚙️ External commands🌐 Network access

Analyze Networks with NetworkX

Complex graph data is difficult to model, inspect, and explain. This skill guides NetworkX workflows for construction, analysis, visualization, and export.

Supports: Claude Codex Code(CC)

⚠️ 38 Poor

Stop for confirmation before installing.

Review the plan and obtain explicit user consent before changing files.

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "networkx" from https://skillstore.io/skills/davila7-networkx.md and its manifest at https://skillstore.io/api/skills/davila7-networkx/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-networkx.md Signed manifest GET /api/skills/davila7-networkx/manifest Signed lockfile GET /api/skills/davila7-networkx/lockfile

Test it

Using "networkx". Analyze an undirected collaboration network with 240 nodes and 612 edges.

Expected outcome:

Summary: 240 nodes, 612 edges, density 0.021, and 3 connected components.
Key result: the largest component contains 92 percent of nodes.
Recommendation: inspect component boundaries before comparing centrality scores.

Using "networkx". Plan a reproducible comparison of small-world and scale-free networks with 1,000 nodes.

Expected outcome:

Method: generate both models with fixed seeds and comparable mean degree.
Compare degree distribution, clustering, average path length, and component structure.
Report parameter choices and uncertainty across repeated runs.

Using "networkx". Choose a format that preserves graph attributes across Python and Gephi.

Expected outcome:

Recommendation: use GraphML because it preserves attributes and works across common graph tools. Validate attribute types before export.

Security Audit

High Risk

v10 • 7/23/2026 Open versioned report

All 81 static findings are false positives caused by Markdown syntax, graph terminology, fixed local I/O examples, and documentation links. The audit found one high-severity issue: the pickle example deserializes a file without warning that untrusted data can execute arbitrary code.

Files scanned

2,451

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

High

references/io.md:245-257

Unsafe Pickle Deserialization Guidance

The I/O guide recommends pickle.load for graph files without warning that malicious pickle data can execute arbitrary code during deserialization.

Line 257 directly calls pickle.load on file content, and the surrounding guidance presents it as a normal read path without trust validation.

Risk Factors

📁 Filesystem access (7)

references/io.md:210 references/io.md:223 references/io.md:236 references/io.md:252 references/io.md:278 references/io.md:327 references/io.md:396

⚙️ External commands (50)

SKILL.md:35-50 SKILL.md:50-52 SKILL.md:52-59 SKILL.md:59-63 SKILL.md:63-66 SKILL.md:66-75 SKILL.md:75-78 SKILL.md:78-83 SKILL.md:83-86 SKILL.md:86-92 SKILL.md:92-94 SKILL.md:94-101 SKILL.md:101-111 SKILL.md:111-114 SKILL.md:114-123 SKILL.md:123-126 SKILL.md:126-132 SKILL.md:132-134 SKILL.md:134-141 SKILL.md:141-157 SKILL.md:157-160 SKILL.md:160-169 SKILL.md:169-172 SKILL.md:172-182 SKILL.md:182-184 SKILL.md:184-191 SKILL.md:191-202 SKILL.md:202-205 SKILL.md:205-218 SKILL.md:218-221 SKILL.md:221-233 SKILL.md:233-236 SKILL.md:236-246 SKILL.md:246-248 SKILL.md:248-255 SKILL.md:255-263 SKILL.md:263-270 SKILL.md:270-277 SKILL.md:277-280 SKILL.md:280-285 SKILL.md:285-288 SKILL.md:288-298 SKILL.md:298-301 SKILL.md:301-305 SKILL.md:305-308 SKILL.md:308-318 SKILL.md:318-328 SKILL.md:328-336 SKILL.md:336-339 SKILL.md:339-344

🌐 Network access (4)

SKILL.md:428 SKILL.md:429 SKILL.md:430 SKILL.md:431

Audited by: codex View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

Copy report link

https://skillstore.io/skills/davila7-networkx/audits/10?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/davila7-networkx/security.svg)](https://skillstore.io/skills/davila7-networkx?utm_source=security_passport_badge)

HTML badge

<a href="https://skillstore.io/skills/davila7-networkx?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-networkx/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Embed card

<iframe src="https://skillstore.io/embed/skills/davila7-networkx.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). networkx security audit report (audit version 10) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-networkx/audits/10

BibTeX citation

@techreport{davila7-davila7-networkx-2026,
  author = {davila7},
  title = {networkx security audit report (audit version 10)},
  institution = {Skillstore},
  year = {2026},
  number = {10},
  url = {https://skillstore.io/skills/davila7-networkx/audits/10},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "networkx security audit report (audit version 10)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-networkx/audits/10"
identifiers:
  - type: other
    value: "skillstore:davila7-networkx:audit:10"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

K-Dense-AI Recommended

k-dense-ai-networkx

Skillstore Score 69

Evidence Confidence High

Skillstore usage 12

Updated

2026-09-09

davila7 Current

davila7-networkx

Skillstore Score 38

Evidence Confidence Medium

Skillstore usage 8

Updated

2026-09-09

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Explore relationship data

Transform tabular relationships into graphs, measure structure, rank important nodes, and explain patterns.

Model network behavior

Generate reproducible synthetic networks and compare their topology with observed systems.

Build graph workflows

Choose graph types, algorithms, file formats, and visual outputs for Python applications.

Try These Prompts

Summarize an edge list

Create an undirected NetworkX graph from this edge list: {edges}. Report node count, edge count, density, connected components, and assumptions.

Rank influential nodes

Analyze this weighted directed graph: {data}. Identify influential nodes using three suitable centrality measures. Explain why each measure fits.

Evaluate communities

Compare suitable NetworkX community detection methods for this network: {data}. Recommend one method, set reproducible parameters, and explain evaluation criteria.

Design a graph pipeline

Design a memory-aware NetworkX pipeline for {dataset}. Validate identifiers and weights. Analyze paths and communities. Export interoperable results and a publication-ready visualization.

Best Practices

Confirm graph type, edge direction, weight meaning, and node identifiers before selecting algorithms.
Set random seeds and record model parameters for reproducible generation and layouts.
Validate on a small sample before running expensive metrics on the full graph.

Avoid

Do not apply connected-graph algorithms without checking components and direction.
Do not interpret centrality rankings without considering graph construction and missing relationships.
Do not deserialize pickle files from unknown or untrusted sources.