networkx

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

Quality

80%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

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Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-organized, action-oriented overview with real reference files and clear workflow sequencing, but it is longer than necessary due to duplicated content between main sections and the Quick Reference, and it lacks explicit validation checkpoints in its workflow.

Suggestions

Consolidate the 'Quick Reference' section with the corresponding 'Core Capabilities' subsections to remove duplicated code and reduce the body length.

Add an explicit validation/verification step to the 'Common Workflow Pattern' (e.g., assert expected node/edge counts or inspect component structure before exporting results).

Trim explanatory prose about well-known concepts (e.g., what each classic graph is, basic graph-type definitions) and let the inline reference pointers carry that detail.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and code-forward, but ~430 lines with content duplicated across 'Core Capabilities' and 'Quick Reference' sections and some explanations of concepts Claude already knows (graph types, classic graphs) mean it could be tightened; not severely padded, so below the score-4 bar.	3 / 5
Actionability	Extensive executable, copy-paste-ready code covers common cases across creation, algorithms, generators, I/O, and visualization, with only minor gaps (some snippets use illustrative fragments or undefined setup variables).	4 / 5
Workflow Clarity	The 'Common Workflow Pattern' gives a clear 5-step sequence (Create/Load, Examine, Analyze, Visualize, Export) but lacks explicit validation checkpoints or feedback loops, matching the score-3 anchor of sequence present with implicit/missing checkpoints.	3 / 5
Progressive Disclosure	Good structure with one-level-deep references clearly signaled inline ('See references/algorithms.md') and a Resources section listing all five reference files; however, substantial detail and duplicated code are inlined in the main body rather than offloaded to those references, a minor organization gap keeping it below 5.	4 / 5
	Total	14 / 20 Passed

Description

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Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it concisely states what the skill does and when to use it, with concrete actions and natural trigger terms across multiple application domains. It uses third-person voice and avoids vague fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions—'creating, analyzing, and visualizing', 'computing graph algorithms (shortest paths, centrality, clustering)', 'detecting communities, generating synthetic networks'—with comprehensive coverage, matching the score-5 anchor.	5 / 5
Completeness	Explicitly states both what ('Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python') and when ('Use when working with network/graph data structures...') with concrete trigger phrases, matching the score-5 anchor.	5 / 5
Trigger Term Quality	Natural terms and synonyms are well covered ('network/graph data structures', 'relationships between entities', plus domain keywords like 'social networks', 'biological networks', 'transportation systems', 'citation networks'), fitting the comprehensive-coverage anchor.	5 / 5
Distinctiveness Conflict Risk	Clear niche (NetworkX graph analysis) with distinct triggers and minimal overlap risk with other skills, matching the score-5 anchor.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/networkx/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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