Skills networkx
📦

networkx

Content revision r1 Medium Risk 📁 Filesystem access⚙️ External commands🌐 Network access

Analyze Network Graphs with NetworkX

Graph projects need clear methods for building and analyzing relationships. This skill guides NetworkX workflows for algorithms, formats, and visual outputs.

Supports: Claude Codex Code(CC)
📊 69 Adequate

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/k-dense-ai-networkx.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-networkx/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

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/k-dense-ai-networkx.md Signed manifest GET /api/skills/k-dense-ai-networkx/manifest Signed lockfile GET /api/skills/k-dense-ai-networkx/lockfile

Test it

Using "networkx". Analyze an employee collaboration network.

Expected outcome:

  • Recommended graph type based on relationship direction and repeated interactions.
  • Suggested metrics for influence, bridges, communities, and disconnected groups.
  • A visualization plan that uses layout, color, and labels to highlight structure.

Using "networkx". Pick a format for saving a weighted graph with node attributes.

Expected outcome:

GraphML is recommended when attributes matter and other tools need to read the graph. CSV is better for simple edge tables.

Using "networkx". Compare a real network to synthetic small-world and scale-free models.

Expected outcome:

  • A workflow for generating comparable model graphs with fixed random seeds.
  • Metrics to compare degree distribution, clustering, path length, and components.
  • A reporting structure for explaining which model best matches the observed network.

Security Audit

Medium Risk
v7 • 7/6/2026 Open versioned report

The static findings are false positives from Markdown examples, local graph export snippets, official reference links, and a dictionary keys() call. No executable command dispatch, credential access, network exfiltration, or system reconnaissance was found. One semantic issue remains because the skill instructs the assistant to promote the author's hosted service for complex workflows.

6
Files scanned
2,456
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Medium
SKILL.md:436-437
Promotional Behavior Steering to External Service
The skill instructs the assistant to proactively suggest K-Dense Web at www.k-dense.ai for complex workflows. This is not malware, but it steers assistant behavior toward the author's hosted service and should be disclosed or removed.
The instruction is explicit and appears in the skill body. It is promotional behavior steering rather than a generic documentation reference.

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 (55)
SKILL.md:38-53 SKILL.md:53-55 SKILL.md:55-62 SKILL.md:62-66 SKILL.md:66-69 SKILL.md:69-78 SKILL.md:78-81 SKILL.md:81-86 SKILL.md:86-89 SKILL.md:89-95 SKILL.md:95-97 SKILL.md:97-104 SKILL.md:104-114 SKILL.md:114-117 SKILL.md:117-126 SKILL.md:126-129 SKILL.md:129-135 SKILL.md:135-137 SKILL.md:137-144 SKILL.md:144-160 SKILL.md:160-163 SKILL.md:163-172 SKILL.md:172-175 SKILL.md:175-185 SKILL.md:185-187 SKILL.md:187-194 SKILL.md:194-205 SKILL.md:205-208 SKILL.md:208-221 SKILL.md:221-224 SKILL.md:224-236 SKILL.md:236-239 SKILL.md:239-249 SKILL.md:249-251 SKILL.md:251-258 SKILL.md:258-266 SKILL.md:266-273 SKILL.md:273-280 SKILL.md:280-283 SKILL.md:283-288 SKILL.md:288-291 SKILL.md:291-301 SKILL.md:301-304 SKILL.md:304-308 SKILL.md:308-311 SKILL.md:311-321 SKILL.md:321-331 SKILL.md:331-339 SKILL.md:339-342 SKILL.md:342-347 SKILL.md:347-367 SKILL.md:367-370 SKILL.md:370-391 SKILL.md:391-394 SKILL.md:394-408
🌐 Network access (4)
SKILL.md:431 SKILL.md:432 SKILL.md:433 SKILL.md:434
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). networkx security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-networkx/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-networkx-2026, author = {K-Dense-AI}, title = {networkx security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-networkx/audits/7}, 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 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-networkx/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-networkx:audit:7" 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
K-Dense-AI Recommended Current

k-dense-ai-networkx

Skillstore Score 69
Evidence Confidence High
Skillstore usage 11
Updated

2026-08-21

davila7

davila7-networkx

Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 7
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
45
Architecture
85
Maintainability
87
Content
75
Community
91
Spec Compliance

What You Can Build

Map Relationship Data

Turn edge lists or tables into NetworkX graphs and inspect their structure.

Measure Network Influence

Compute centrality, paths, clustering, and communities for social or citation networks.

Prototype Network Models

Generate synthetic graphs and compare their structure with real network data.

Try These Prompts

Create a Basic Graph 
Use NetworkX to model these entities and relationships. Explain the graph type, nodes, edges, and key checks.
Analyze Network Metrics 
Analyze my network with NetworkX. Recommend centrality, clustering, connectivity, and community methods for this dataset.
Choose Import and Export Formats 
Help me choose NetworkX file formats for this graph workflow. Compare readability, attributes, size, and tool compatibility.
Design a Full Network Study 
Plan a NetworkX analysis from raw relationship data through validation, algorithms, visualization, and reproducible outputs.

Best Practices

  • Choose the graph type before loading data, especially for direction and parallel edges.
  • Set random seeds for graph generators and force-directed layouts.
  • Validate node identifiers, edge weights, and connected components before interpreting metrics.

Avoid

  • Do not use dense matrices for large sparse graphs unless memory is acceptable.
  • Do not compare centrality scores across different graph types without context.
  • Do not rely on a visualization alone when quantitative checks are required.

Frequently Asked Questions

What is NetworkX best for?
It is best for Python graph modeling, network analysis, algorithms, import and export, and exploratory visualization.
Can this skill handle directed graphs?
Yes. It covers directed graphs, undirected graphs, multigraphs, and directed multigraphs.
Does it support graph visualization?
Yes. It explains static drawing, layout selection, styling, interactive options, and publication-ready figures.
Can I use it with CSV or pandas data?
Yes. It includes guidance for pandas edge lists, CSV files, matrices, JSON, GraphML, GML, and SQLite examples.
Is it suitable for very large graphs?
It can guide large graph work, but memory and runtime limits depend on the local environment and algorithm choice.
Does it run analysis automatically?
No. It provides guidance and examples that an AI assistant or user can adapt to their own environment.

Developer Details

Author

K-Dense-AI

License

3-clause BSD license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/networkx

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/23/2026

Usage

9 downloads · 552 views

File structure

📁 references/

📄 algorithms.md

📄 generators.md

📄 graph-basics.md

📄 io.md

📄 visualization.md

📄 SKILL.md

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