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

83%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

75%Weight 40%Scale 1-5

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

The body is highly actionable with comprehensive executable code and a clear workflow sequence, and it uses a clean one-level reference structure. Its main weakness is conciseness: the Quick Reference and Common Workflow sections duplicate Core Capabilities content, and the overview inlines more API detail than a lean SKILL.md would.

Suggestions

Remove the Quick Reference section or reduce it to only operations not already shown in Core Capabilities, since shortest paths, centrality, and I/O are demonstrated twice.

Trim the Common Workflow Pattern to focus on the sequence and decision points rather than re-listing code already shown in the capability sections.

Move the fuller generator, I/O, and visualization code blocks into the corresponding reference files, keeping only a single representative snippet per capability in SKILL.md.

Dimension	Reasoning	Score
Conciseness	Mostly code with little conceptual padding, but the Quick Reference section duplicates much of the Core Capabilities content (shortest_path, centrality, I/O shown twice) and the Common Workflow Pattern re-shows code already presented, so it could be tightened beyond minor trims.	3 / 5
Actionability	Provides fully executable, copy-paste-ready code across graph creation, algorithms, generators, I/O, and visualization, including pandas/numpy integration and publication-quality figure export covering the common cases.	5 / 5
Workflow Clarity	The Common Workflow Pattern gives a clear five-step sequence (Create/Load, Examine, Analyze, Visualize, Export) with code, but validation checkpoints are mostly implicit (step 2 prints stats but is not framed as a verify gate) rather than explicit.	4 / 5
Progressive Disclosure	Well-structured sections with one-level-deep references that all exist and are signaled inline plus summarized in Resources, but the SKILL.md body itself inlines substantial API detail (full generator, I/O, visualization code) that partly belongs in the reference files.	4 / 5
	Total	16 / 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 states concrete capabilities, gives explicit 'Use when' triggers with named algorithms and domains, and is clearly distinct from other skills. Its only weakness is slightly incomplete natural-term coverage (no nodes/edges synonyms or file formats), capping trigger_term_quality at 4.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions including 'creating, analyzing, and visualizing complex networks', 'computing graph algorithms (shortest paths, centrality, clustering)', 'detecting communities', and 'generating synthetic networks', giving comprehensive coverage rather than minor gaps.	5 / 5
Completeness	Explicitly answers 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.	5 / 5
Trigger Term Quality	Good natural-term coverage with 'network/graph data structures', 'relationships between entities', and domain examples (social, biological, transportation, citation), but missing common synonyms (nodes/edges) and any file extensions that anchor 5 requires.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (Python graph/network analysis) with distinctive triggers like shortest paths, centrality, communities, and synthetic network generation, minimizing overlap with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/coding/networkx/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

Table of Contents

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