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

82%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

65%

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

The content is highly actionable with abundant executable examples, but it is overly long and restates API knowledge Claude already has, and its progressive-disclosure references point to files that do not exist in the bundle.

Suggestions

Trim the body to a lean overview and move the detailed API listings into the referenced references/*.md files, which must actually be created so the inline references resolve.

Remove explanations of concepts Claude already knows (floating-point precision, memory/performance basics, hashable node semantics) unless they include a NetworkX-specific non-obvious caveat.

Add an explicit validation/checklist step to the Common Workflow Pattern (e.g. assert graph integrity or sanity-check metric ranges before exporting) to introduce feedback loops.

Dimension	Reasoning	Score
Conciseness	The ~430-line body restates much of the NetworkX API Claude already knows (basic add_node/draw calls, layout functions) and explains general concepts like floating-point precision and hashable node types, so it is padded rather than lean, though it retains genuine structure.	2 / 3
Actionability	Provides concrete, executable, copy-paste-ready code throughout (e.g. nx.shortest_path, nx.from_pandas_edgelist, nx.spring_layout with seed), with specific parameters and a quick-reference section.	3 / 3
Workflow Clarity	The 'Common Workflow Pattern' gives a clear 5-step sequence (Create/Load -> Examine -> Analyze -> Visualize -> Export), but validation/checkpoint steps are implicit and there are no feedback loops for error recovery.	2 / 3
Progressive Disclosure	The body is a long inline API reference that should be split out, and although it signals five references/*.md files, no references/ directory actually exists, so the navigation is dangling rather than a clean one-level-deep split.	2 / 3
	Total	9 / 12 Passed

Description

100%

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 across the board: it states concrete capabilities, provides natural trigger terms, explicitly covers both what and when, and occupies a clear niche unlikely to conflict with other skills.

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions ('creating, analyzing, and visualizing') and elaborates with specifics like 'shortest paths, centrality, clustering', 'detecting communities', and 'generating synthetic networks', matching the multi-action anchor.	3 / 3
Completeness	Explicitly answers both what it does ('Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python') and when to use it ('Use when working with network/graph data structures...').	3 / 3
Trigger Term Quality	Includes natural user-facing terms a person would say ('network/graph data structures', 'shortest paths', 'centrality', 'clustering', 'social networks'), giving good coverage rather than just technical jargon.	3 / 3
Distinctiveness Conflict Risk	Clearly scoped to network/graph analysis in Python with distinct domain triggers (social, biological, transportation, citation networks), making it unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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
referenced_paths_exist	Referenced path issues: 10 missing	Warning

	Total	14 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/general/networkx/SKILL.md
Commit: df37802

Reviewed: about 2 hours ago

Table of Contents

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