What is rai-graph-analysis?

A skill for choosing and running graph algorithms on RelationalAI models. Graph algorithms examine connected data for patterns such as importance, communities, paths, and dependencies.

Building graphs from an ontology, analyzing centrality, communities, connectivity, reachability, dependencies, and shortest paths, then using the results in later reasoning.

It helps match a structural question about a network to an appropriate algorithm and configure it correctly.

rai-graph-analysis — Skill for Claude Code by RelationalAI

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 RelationalAI/rai-agent-skills --skill rai-graph-analysis

Clone the repo

git clone --depth 1 https://github.com/RelationalAI/rai-agent-skills

Made for: Claude Code.

Or install rai, the plugin that ships this one along with the rest of its 12 skills.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-graph-analysis/github.svg)](https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-graph-analysis)

Your own site

<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-graph-analysis"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-graph-analysis/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-graph-analysis"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-graph-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 9,273 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.

Model	Per session	Once invoked
Fable 5.1	$0.00077	$0.09273
Opus 5	$0.00039	$0.04637
Sonnet 5	$0.00015	$0.01855
Haiku 4.5	$0.00008	$0.00927

Measured 11d ago against content hash 1720ad91ba4a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

rai-graph-analysis 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 11d ago.

The scan reads SKILL.md. This mod also ships 13 executable files (examples/centrality_weighted_undirected.py, examples/chained_graph_rules.py, examples/co_occurrence_wcc_bottleneck.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/rai/skills/rai-graph-analysis/SKILL.md · 487 lines

How it starts

The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Summary

What: Graph algorithm selection and execution — building Graph instances from PyRel ontology patterns and running the right algorithm to answer structural questions about the data.

When to use:

Building a Graph instance from an existing PyRel model (choosing node concept, edge construction, directed/weighted)
Selecting which graph algorithm answers a given question (centrality, community, reachability, etc.)
Configuring algorithm parameters (direction, weights, aggregator)
Extracting and binding graph results to model properties
Feeding graph outputs into downstream reasoning (optimization, rules, predictions)

When NOT to use:

Discovering whether graph analysis is appropriate for a dataset — see rai-discovery
PyRel syntax reference (imports, types, model patterns) — see rai-pyrel
Ontology design decisions (concept modeling, data mapping) — see rai-ontology
Optimization formulation (variables, constraints, objectives) — see rai-prescriptive-problem
Business rule authoring (validation, classification, alerting) — see rai-pyrel
GNN graph construction for predictive pipelines — see rai-predictive-modeling

Overview (process steps):

Study the existing model — understand base definitions, coding conventions, and what's already wired
Start from the question — identify which concepts and relationships are relevant, and what kinds of analysis best speak to the question
Determine which relevant concepts should constitute nodes in the graph
Determine what edges would best capture the information relevant to the question and the planned analysis
Figure out how to derive those edges from the relevant relationships (sometimes a pass-through, often involving filters or more substantial logic)
Choose which Graph constructor pattern fits given the node/edge decisions
Select the specific algorithm(s) best suited to the question
Execute, extract results, and blend back into the model for downstream use

Read the full file on GitHub · 487 lines

Files

What ships with it

18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

11d ago First seen · 487 lines · 77 tokens per session scan A 1720ad91ba4a

Subscribe to this mod's changes

rai-graph-analysis is a skill published in the GitHub repository RelationalAI/rai-agent-skills (4 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 9,273 once invoked, about $0.0004 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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