networkx

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

Quality

58%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/networkx/SKILL.md

Quality

Content

63%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 content is highly actionable with comprehensive executable examples and a clear workflow pattern, but it is weighed down by a redundant Quick Reference section and, most seriously, by references to five reference files that are not present in the bundle.

Suggestions

Create the missing references/*.md files (graph-basics, algorithms, generators, io, visualization) or remove the dangling reference pointers and the Resources section that advertises them.

Remove or trim the 'Quick Reference' section, which repeats Basic Operations, Essential Algorithms, and File I/O already covered in 'Core Capabilities'.

Add at least one validation/verification checkpoint to the Common Workflow Pattern (e.g. assert expected node/edge counts after loading, verify connectivity assumptions before analysis).

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient code examples rather than concept padding, but the 'Quick Reference' section (Basic Operations, Essential Algorithms, File I/O) substantially duplicates content already shown in 'Core Capabilities', which is unnecessary redundancy that could be trimmed.	3 / 5
Actionability	It provides extensive, copy-paste-ready, executable code across creation, algorithms, generators, I/O, and visualization, with only minor gaps (e.g. `community` used in the workflow without its import, bare `source`/`target` placeholders in Quick Reference).	4 / 5
Workflow Clarity	The 'Common Workflow Pattern' gives a clear 5-step sequence (Create/Load -> Examine -> Analyze -> Visualize -> Export); because this is an analytical, non-destructive workflow the missing-validation cap does not apply, though the absence of any checkpoints keeps it from a 5.	4 / 5
Progressive Disclosure	References are well-signaled and one level deep ('See references/graph-basics.md', etc.), but the referenced files do not exist (no references/ directory), so navigation is broken; additionally ~430 lines of inline code duplicate what the references are meant to expand on.	3 / 5
	Total	14 / 20 Passed

Description

53%Weight 40%Scale 1-5

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 clearly identifies the package and its domain with three action verbs, but it lacks any 'Use when...' trigger guidance and relies on generic verbs, which caps both completeness and specificity. Trigger-term coverage is reasonable but thin on synonyms.

Suggestions

Add an explicit 'Use when...' clause listing concrete trigger scenarios (e.g. 'Use when working with graph or network data, social networks, knowledge graphs, or running graph algorithms').

Replace generic verbs ('manipulating', 'analyzing') with specific capabilities (e.g. 'computing centrality, finding shortest paths, detecting communities').

Include natural synonyms users say, such as 'nodes', 'edges', 'knowledge graphs', and 'graph algorithms', to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	The description names the domain ('complex networks and graphs') and three actions ('creating, manipulating, and analyzing'), but the verbs are generic/high-level rather than the concrete operations (e.g. shortest paths, centrality, community detection) that anchor 4 expects.	3 / 5
Completeness	It gives a clear 'what' (a Python package for creating/manipulating/analyzing networks and graphs) but has no 'Use when...' clause or equivalent trigger guidance, so per the rubric completeness is capped at 3.	3 / 5
Trigger Term Quality	It includes the natural terms 'networks' and 'graphs' that users would say, but misses common variations and synonyms (nodes, edges, graph algorithms, knowledge graph, social network), matching the anchor-3 'some relevant keywords but missing common variations'.	3 / 5
Distinctiveness Conflict Risk	Naming the specific package NetworkX and the distinct graph/network domain gives it a clear niche with only minor overlap risk; it is not quite a 5 because the trigger phrasing is not explicit enough to fully eliminate conflict risk.	4 / 5
	Total	13 / 20 Passed

Validation

81%

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 — 13 / 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
referenced_paths_exist	Referenced path issues: 10 missing	Warning

	Total	13 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/networkx/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

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