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 OpenLinkSoftware/ai-agent-skills --skill fuxi-engineergit clone --depth 1 https://github.com/OpenLinkSoftware/ai-agent-skillsWrote 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/openlinksoftware/ai-agent-skills/fuxi-engineer)<a href="https://agentmods.dev/skills/openlinksoftware/ai-agent-skills/fuxi-engineer"><img src="https://agentmods.dev/badge/skills/openlinksoftware/ai-agent-skills/fuxi-engineer.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.02804 |
| Opus 5 | $0.00016 | $0.01402 |
| Sonnet 5 | $0.00006 | $0.00561 |
| Haiku 4.5 | $0.00003 | $0.00280 |
Grade A, and why
fuxi-engineer 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use FuXi for semantic web reasoning (RDF, OWL, SPARQL)
FuXi is a Python bi-directional reasoning engine (forward/bottom-up + backward/top-down) companion to RDFLib.
What I do
- Try steps of reasoning and generation of proofs
- Add annotations to OWL ontologies
- Summarizing an OWL ontology using InfixOWL API
- Performing theorem proving services
- Make use of QLever for SPARQL interlocution with ontologies and/or rules
- Use robot to validate if an ontology is in the OWL 2 RL profile (and therefore can be used with DLP)
- Use riot to convert between RDF formats
When to Use This Skill
- When you need to interpret an OWL ontology rule file
- When you need to answer SPARQL queries
- When you need to add annotations to OWL ontology using InfixOWL API
Basic Principles
The best format for OWL ontologies is OWL/RDF/XML, for compatibility with ontology tools such as protege.
When verbalizing or serializing OWL for human eyes or reviewing narrative readability, the preferred syntax is
Manchester OWL (OWL/RDF/XML):
from rdflib import Graph
from fuxi.cli.renderers import _render_man_owl as render_man_owl
from fuxi.Syntax.InfixOWL import all_classes, all_properties
ontology_graph = Graph().parse("ontology.owl")
for p in all_properties(ontology_graph):
print(p.identifier, list(p.label))
print(repr(p))
for c in all_classes(ontology_graph):
print(c.__repr__(True))
Otherwise, turtle is the preferred format for RDF/XML if it has no rules or N3 if it does. SPARQL files should be managed in separate .rq files.
Some core RDF vocabularies to re-use whenever possible:
- skos (SKOS Simple Knowledge Organization System Reference)
- OBO Information Artifact ontology IAO Information Artifact Ontology
- (Relation Ontology)
- FOAF Vocabulary Specification
- dublin core (DCMI Metadata expressed in RDF Schema Language)
- (https://www.w3.org/TR/rdf-schema/)[RDFS]
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 · 277 lines · 32 tokens per session scan A 93f54955e741
fuxi-engineer is a skill published in the GitHub repository OpenLinkSoftware/ai-agent-skills (38 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 2,804 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 2026-08-30.
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