Borrowing it
Nothing to install: this file belongs to arango-solutions/arango-sparql-py. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/arango-solutions/arango-sparql-py/main/.cursor/skills/sparql-to-aql/SKILL.mdgit clone --depth 1 https://github.com/arango-solutions/arango-sparql-pyWrote 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/arango-solutions/arango-sparql-py/sparql-to-aql)<a href="https://agentmods.dev/skills/arango-solutions/arango-sparql-py/sparql-to-aql"><img src="https://agentmods.dev/badge/skills/arango-solutions/arango-sparql-py/sparql-to-aql/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.
<a href="https://agentmods.dev/skills/arango-solutions/arango-sparql-py/sparql-to-aql"><img src="https://agentmods.dev/badge/skills/arango-solutions/arango-sparql-py/sparql-to-aql.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00094 | $0.02218 |
| Opus 5 | $0.00047 | $0.01109 |
| Sonnet 5 | $0.00019 | $0.00444 |
| Haiku 4.5 | $0.00009 | $0.00222 |
Grade A, and why
sparql-to-aql 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.
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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SPARQL → AQL Porting
This skill is the deterministic recipe for adding or fixing a SPARQL
construct in arango-sparql-py. It exists because the translation
behavior is non-obvious and must match the legacy Foxx service's
semantics — never invented from scratch.
Inputs you will work with
- Legacy source of truth:
references/arango-sparql/src/lib/aql-translator.js— top-level dispatch by query type.pgt-translator.js— Property Graph Topology translation rules.rpt-translator.js— RDF Property Triple translation rules.filter-translator.js— FILTER → AQL boolean expression mapping.triple-constructor.js— CONSTRUCT clause helpers.aql-query-builder.js— fluent AQL builder API to mimic.uri-resolver.js,uri-hasher.js— URI ↔ collection/key mapping.
- Python target:
arango_sparql/translate/parser.py— wrapsrdflib.plugins.sparql.parser.parseQuery+algebra.translateQuery.visitor.py— the Algebra visitor (onevisit_<NodeType>per Algebra op).builder.py— the parameterized AQL builder.resolver.py—SchemaResolverover the in-memory OWL graph.errors.py—SparqlErrorhierarchy.
Workflow
Copy this checklist into your scratchpad and tick items as you go:
Task progress:
- [ ] 1. Identify the SPARQL construct and its Algebra node name
- [ ] 2. Read the legacy JS translator for that construct
- [ ] 3. Locate (or create) the visitor method
- [ ] 4. Implement using the AQL builder (no string concat)
- [ ] 5. Add a golden test
- [ ] 6. Add a pyoxigraph cross-validation test (when applicable)
- [ ] 7. Run pytest and ruff
1. Identify the Algebra node
Run this in a scratch shell or a unit test to see the exact Algebra
node name rdflib produces for your SPARQL fragment:
from rdflib.plugins.sparql.parser import parseQuery
from rdflib.plugins.sparql.algebra import translateQuery
print(translateQuery(parseQuery("SELECT ?s WHERE { ?s ?p ?o OPTIONAL { ?s :name ?n } }")).algebra)
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 · 221 lines · 94 tokens per session scan A 4b0d271762d4
sparql-to-aql is a skill published in the GitHub repository arango-solutions/arango-sparql-py (2 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 2,218 once invoked, about $0.0005 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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