graph-explore — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited graph-explore (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Connect to a graph file, analyze its structure and key nodes, find paths, and export visualizations.
connect_database with the file path from $ARGUMENTS. The tool auto-detects graph formats including DOT, GML, GraphML, and Mermaid. Note the assigned database name.get_graph_stats with the database name. Review: node count, edge count, density, whether the graph is directed or undirected, connected components count, and average degree. This gives an overview of the graph's scale and connectivity.get_neighbors for the top 3 highest-degree nodes to understand what they connect to. These hubs are often the most important entities in the network.get_edges to retrieve a sample of edges. Look for patterns: are edges weighted? Do they have labels or types? Is the graph sparse or dense? Identify any isolated components or bridges.get_node to retrieve node attributes and metadata. Note any labels, types, or properties that provide context.find_path between them. Review the shortest path length and the intermediate nodes. This reveals how information or relationships flow through the network.export_graph with a suitable format (DOT for Graphviz rendering, or the original format for round-tripping). For large graphs, suggest filtering to a subgraph around nodes of interest before exporting.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.