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 sparq-org/sparq --skill inferencegit clone --depth 1 https://github.com/sparq-org/sparqWrote 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/sparq-org/sparq/inference)<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>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 | $0.00172 | $0.33216 |
| Opus 5 | $0.00086 | $0.16608 |
| Sonnet 5 | $0.00034 | $0.06643 |
| Haiku 4.5 | $0.00017 | $0.03322 |
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.
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
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.
- 7d ago First seen · 737 lines · 172 tokens per session scan A 678d5eb7c435
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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