graph-data-explore

graph-data-explore is a skill for Claude Code from ChrisGVE/localdata-mcp. It costs 38 tokens per session (561 once invoked), scanned A, original, Apache-2.0.

A graph exploration tool examines connected data made of nodes and links, such as relationships in DOT, GML, GraphML, or Mermaid files.

In plain words
What is it for?
Use it to count nodes and links, find highly connected nodes, inspect relationships, find paths, and create visualizations.
Why use it?
It removes the need to inspect graph files manually to understand their size, structure, important nodes, and connections.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the localdata-mcp plugin — 18 skills, 11 agents, 1 MCP server shipped together

Good fit Use it to count nodes and links, find highly connected nodes, inspect relationships, find paths, and create visualizations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chrisgve/localdata-mcp/graph-data-explore
Install

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.

Any agent
npx skills add ChrisGVE/localdata-mcp --skill graph-data-explore
Clone the repo
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

Wrote 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.

agentmods badge for graph-data-explore

README.md
[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/graph-data-explore/github.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/graph-data-explore)
Your own site
<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/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for graph-data-explore

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 561 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.00561
Opus 5.5 $0.00015 $0.00224
Sonnet 5.5 $0.00008 $0.00112
Haiku 4.5 $0.00004 $0.00056

Measured 2mo ago against content hash b65e361594fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

skills/graph-data/graph-data-explore/SKILL.md · 34 lines

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

  1. Connect to the graph. Call 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.

  2. Get graph statistics. Call 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.

  3. Identify hub nodes. From the stats, note nodes with the highest degree (most connections). Call 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.

  4. Explore structure. Call 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.

  5. Inspect specific nodes. For nodes of interest (hubs or user-specified), call get_node to retrieve node attributes and metadata. Note any labels, types, or properties that provide context.

  6. Find paths. If there are at least two notable nodes, call find_path between them. Review the shortest path length and the intermediate nodes. This reveals how information or relationships flow through the network.

  7. Export a visualization. Call 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.

  8. 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

Read the full file on GitHub · 34 lines

Changes

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.

  1. 2mo ago First seen · 34 lines · 38 tokens per session scan A b65e361594fd

Subscribe to this mod's changes

graph-data-explore is a skill published in the GitHub repository ChrisGVE/localdata-mcp (15 stars, last pushed 4d ago), licensed Apache-2.0. It adds 38 tokens to every session and 561 once invoked, about $0.0002 per session on Opus 5.5. 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.

Related

graph-data-explore compared with skills from other repositories

graph-data-explore next to 6 skills from other repositories
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