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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.

69

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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 a well-structured overview with fully executable examples and excellent one-level-deep reference navigation. Its main weakness is redundancy: the Quick Reference and Resources sections duplicate material already presented, inflating token cost without adding guidance, and the workflow pattern lacks validation checkpoints.

Suggestions

Remove the '## Quick Reference' section — its Basic Operations, Essential Algorithms, and File I/O code blocks duplicate the Core Capabilities sections nearly verbatim and add ~65 lines of token cost with no new guidance.

Drop or drastically shorten the '## Resources' section that re-describes each references/*.md file — each is already pointed to inline in its corresponding section with a description of its contents.

Add a lightweight validation checkpoint to the Common Workflow Pattern (e.g. verifying graph integrity or expected node/edge counts after loading) so failures are caught before analysis runs.

DimensionReasoningScore

Conciseness

The body is mostly tight, executable code with little prose fluff, but contains substantial structural duplication: the '## Quick Reference' section (Basic Operations, Essential Algorithms, File I/O Quick Reference) repeats code already shown in Core Capabilities, and '## Resources' re-describes each references/*.md file already pointed to inline. This fits 'mostly efficient but could be tightened' rather than anchor 2, since the padding is redundant sections rather than over-explanation of known concepts.

3 / 5

Actionability

Fully executable, copy-paste-ready snippets throughout (e.g. 'G = nx.from_pandas_edgelist(df, "source", "target", edge_attr="weight")', 'pos = nx.spring_layout(G, seed=42)'), covering the common cases for creation, analysis, generation, I/O, and visualization. Nothing is pseudocode or abstract.

5 / 5

Workflow Clarity

The 'Common Workflow Pattern' gives a clear 5-step sequence (Create/Load, Examine, Analyze, Visualize, Export) with concrete code per step, but there are no explicit validation or error-recovery checkpoints. Not 3 because the sequence is coherent and complete; not 5 because no feedback loops exist (though this is not a destructive/batch-operation skill, so the cap does not apply).

4 / 5

Progressive Disclosure

Clear overview with well-signaled, one-level-deep references: each Core Capability section ends with '**Reference**: See references/<file>.md' and all five referenced files (graph-basics, algorithms, generators, io, visualization) exist in the bundle. Content is appropriately split between inline quick examples and detailed reference files, and navigation is easy.

5 / 5

Total

17

/

20

Passed

Description

92%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.

A strong description: it states what the skill does and when to use it in explicit, third-person terms with concrete algorithm names and domain examples. Trigger term coverage is good though not exhaustive, missing a few natural synonyms like 'nodes', 'edges', and 'PageRank'.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'creating, analyzing, and visualizing complex networks', 'computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks' — with comprehensive coverage. It exceeds anchor 4 because coverage spans creation, analysis, generation, and visualization with no notable gaps.

5 / 5

Completeness

Explicitly answers both: 'Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python' (what) and 'Use when working with network/graph data structures... detecting communities... visualizing network topologies' (when), with concrete trigger phrases in third person voice.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('network/graph data structures', 'shortest paths', 'centrality', 'detecting communities', 'social networks'), but common user phrases like 'nodes', 'edges', 'PageRank', or library-specific mentions are absent. Better than anchor 3 (several synonyms present) but short of anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

Clear niche — Python graph/network analysis with named algorithms (shortest paths, centrality, community detection) — that is unlikely to trigger for unrelated skills. Distinct triggers minimize conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

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

Reviewed

Table of Contents

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