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.
git 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/rules/arango-solutions/arango-sparql-py/100-backend-python)<a href="https://agentmods.dev/rules/arango-solutions/arango-sparql-py/100-backend-python"><img src="https://agentmods.dev/badge/rules/arango-solutions/arango-sparql-py/100-backend-python/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/rules/arango-solutions/arango-sparql-py/100-backend-python"><img src="https://agentmods.dev/badge/rules/arango-solutions/arango-sparql-py/100-backend-python.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 | $0.00000 | $0.01065 |
| Opus 5.5 | $0.00000 | $0.00426 |
| Sonnet 5.5 | $0.00000 | $0.00213 |
| Haiku 4.5 | $0.00000 | $0.00106 |
Grade A, and why
100-backend-python 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 1mo 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend Python rules
Tooling
- Dependency manager:
uv(nopoetry, no barepip). Add deps withuv add ...; update lock withuv lock. - Python: >=3.11,
from __future__ import annotationsat top of every module. - Lint/format:
ruffwithline-length = 110,target-version = "py311". - Typing: prefer PEP 604 unions (
str | None),dataclassfor plain data,pydantic.BaseModelfor any HTTP request/response or external contract.
SPARQL parsing — always rdflib
- Entry point for parsing:
from rdflib.plugins.sparql.parser import parseQueryfollowed byfrom rdflib.plugins.sparql.algebra import translateQueryto obtain the Algebra tree. - The translator is a visitor over the Algebra, not over the parse
tree. Every Algebra node type (
BGP,Project,Filter,LeftJoin,Union,Slice,OrderBy,Distinct,Extend,Group,AggregateJoin,Service, …) gets onevisit_<NodeType>method. - Unknown nodes must raise a typed error (e.g.
UnsupportedSparqlError) — never fall through silently.
AQL emission — always the builder
- All AQL strings flow through the AQL query builder (port of
references/arango-sparql/src/lib/aql-query-builder.js). Never concatenate AQL with+or f-strings outside the builder. - Variables from SPARQL bind to AQL bind variables (
@var). Literal values always become bind variables; never inline them into the AQL string. - The builder owns aliasing — do not generate FOR-loop variable names manually; ask the builder for a fresh alias.
OWL / schema mapping
- Load the ontology produced by
arango-schema-mapperinto a singlerdflib.Graphat app startup; cache by ontology hash. - URI → physical collection/edge/property resolution goes through one
SchemaResolverclass. SPARQL visitors call the resolver; they never reach into the graph directly. - Treat the OWL graph as immutable after load. If schema can change at runtime, version the resolver and swap atomically.
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.
- 1mo ago First seen · 87 lines · 0 tokens per session scan A 0190b1c15a68
100-backend-python is a cursor rule published in the GitHub repository arango-solutions/arango-sparql-py (2 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,065 tokens. 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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