inference

inference is a skill for Claude Code from sparq-org/sparq. It costs 172 tokens per session (33,216 once invoked), scanned A, original, MIT.

An optional Rust component that applies logical rules to an RDF graph, a structured collection of linked data, and adds the facts that follow from those rules.

In plain words
What is it for?
Use it to apply RDFS, OWL 2 RL, or Notation3 rules, query inferred triples, maintain results after inserts or deletes, inspect proof trees, and check for OWL inconsistencies.
Why use it?
It lets queries use inferred facts without requiring each query to repeat the reasoning. It can also update the inferred results as data changes and show why a fact was derived.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the sparq plugin — 55 skills, 20 agents, 2 hooks shipped together

Good fit Use it to apply RDFS, OWL 2 RL, or Notation3 rules, query inferred triples, maintain results after inserts or deletes, inspect proof trees, and check for OWL inconsistencies.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sparq-org/sparq/inference
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 sparq-org/sparq --skill inference
Clone the repo
git clone --depth 1 https://github.com/sparq-org/sparq

Made for: Claude Code.

Or install sparq, the plugin that ships this one along with the rest of its 55 skills, 20 agents, 2 hooks.

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 inference

README.md
[![agentmods](https://agentmods.dev/badge/skills/sparq-org/sparq/inference.svg)](https://agentmods.dev/skills/sparq-org/sparq/inference)
Your own site
<a href="https://agentmods.dev/skills/sparq-org/sparq/inference"><img src="https://agentmods.dev/badge/skills/sparq-org/sparq/inference.svg" alt="Measured on agentmods" height="20"></a>
Per session 172 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 33,216 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 $0.00172 $0.33216
Opus 5 $0.00086 $0.16608
Sonnet 5 $0.00034 $0.06643
Haiku 4.5 $0.00017 $0.03322

Measured 7d ago against content hash 678d5eb7c435, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

inference 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 7d 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/inference/SKILL.md · 737 lines

How it starts

The opening of the file, as written. The whole thing — 737 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sparq-inference

sparq-reason is sparq's opt-in reasoning crate: it forward-chains the deductive closure (RDFS, OWL 2 RL, or user-supplied Notation3 rules) over dictionary-encoded triples and materializes the entailed facts so querying stays exactly as fast as before. The core engine carries zero reasoning code/cost unless you depend on this crate. Reasoning works at the [Id;3] (RDFS/OWL) or n3::Term (N3) level; you wire the result back into a sparq_core::Graph to query it.

Quickstart

Add the dep (native targets only — see Gotchas):

[dependencies]
sparq-core   = "0.1"
sparq-reason = "0.1"   # default features include `parallel`

Parse → materialize the RDFS closure in place → build a queryable graph (the canonical seam, exactly what the CLI does):

use sparq_core::Graph;
use sparq_reason::{materialize, Profile};

// 1. Parse to (Dict, triples) WITHOUT building indexes yet.
let (mut dict, mut triples) = Graph::parse_to_triples(turtle_text, "turtle")?;
let base = triples.len();

// 2. Expand `triples` in place with every entailed triple. Returns NEW triple count.
let added = materialize(Profile::Rdfs, &mut dict, &mut triples);
eprintln!("RDFS: {base} -> {} triples (+{added} entailed)", triples.len());

// 3. Build the indexed graph from the materialized closure and query as usual.
let g = Graph::from_parts(dict, triples);
# Ok::<(), String>(())

CLI equivalent (materialize and optionally dump the closure as N-Triples):

cargo run --release -p sparq-cli -- reason ontology.ttl turtle rdfs            # rdfs | owl | n3
cargo run --release -p sparq-cli -- reason ontology.ttl turtle owl out.nt      # write full closure
cargo run --release -p sparq-cli -- query data.ttl 'SELECT ...' --reason rdfs  # reason then query

# OWL 2 EL classification — the class hierarchy RL cannot reach (opt-in `el` feature). Complete
# for E1+E2 only: the CLI omits `cdomain`, so concrete-domain axioms land in `skipped_axioms`.
cargo run --release -p sparq-cli --features el -- classify ontology.ttl turtle lattice.nt
cargo run --release -p sparq-cli --features el -- query ontology.ttl turtle 'SELECT ...' --reason el

Read the full file on GitHub · 737 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. 7d ago First seen · 737 lines · 172 tokens per session scan A 678d5eb7c435

Subscribe to this mod's changes

inference is a skill published in the GitHub repository sparq-org/sparq (10 stars, last pushed 2d ago), licensed MIT. It adds 172 tokens to every session and 33,216 once invoked, about $0.0009 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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