Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ChrisGVE/localdata-mcp --skill graph-data-exploregit clone --depth 1 https://github.com/ChrisGVE/localdata-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chrisgve/localdata-mcp/graph-data-explore)<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/graph-data-explore"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/graph-data-explore.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00561 |
| Opus 5 | $0.00019 | $0.00280 |
| Sonnet 5 | $0.00008 | $0.00112 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
graph-data-explore scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Data Explore
Connect to a graph data file, analyze its structure and key nodes, find paths, and export visualizations.
Steps
-
Connect to the graph. Call
connect_databasewith the file path from$ARGUMENTS. The tool auto-detects graph formats including DOT, GML, GraphML, and Mermaid. Note the assigned database name. -
Get graph statistics. Call
get_graph_statswith 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. -
Identify hub nodes. From the stats, note nodes with the highest degree (most connections). Call
get_neighborsfor the top 3 highest-degree nodes to understand what they connect to. These hubs are often the most important entities in the network. -
Explore structure. Call
get_edgesto 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. -
Inspect specific nodes. For nodes of interest (hubs or user-specified), call
get_nodeto retrieve node attributes and metadata. Note any labels, types, or properties that provide context. -
Find paths. If there are at least two notable nodes, call
find_pathbetween them. Review the shortest path length and the intermediate nodes. This reveals how information or relationships flow through the network. -
Export a visualization. Call
export_graphwith 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. -
Summarize findings. Present:
- Graph type (directed/undirected, weighted/unweighted)
- Scale: node and edge counts, density
- Key structural features: hubs, communities, bridges
- Notable paths or relationships discovered
- Suggestions for further exploration or subgraph analysis
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 34 lines · 38 tokens per session scan A b65e361594fd
graph-data-explore is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 38 tokens to every session and 561 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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