100-backend-python

A set of coding rules for a Python backend built with FastAPI, RDFLib, and an AQL query builder. FastAPI handles web requests, RDFLib reads semantic-data queries, and AQL is the query language used by ArangoDB.

In plain words
What is it for?
It is for guiding Python module structure, SPARQL parsing, OWL schema mapping, AQL generation, and support for FastAPI request and response models.
Why use it?
It gives developers consistent conventions for dependencies, formatting, type hints, query parsing, and error handling across the backend.

Cursor rule for Cursor

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.

agentmods
npx agentmods add rules/arango-solutions/arango-sparql-py/100-backend-python
Clone the repo
git clone --depth 1 https://github.com/arango-solutions/arango-sparql-py

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 1,065 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.01065
Opus 5 $0.00000 $0.00532
Sonnet 5 $0.00000 $0.00213
Haiku 4.5 $0.00000 $0.00106

Measured yesterday against content hash 0190b1c15a68, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

.cursor/rules/100-backend-python.mdc · 87 lines

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 (no poetry, no bare pip). Add deps with uv add ...; update lock with uv lock.
  • Python: >=3.11, from __future__ import annotations at top of every module.
  • Lint/format: ruff with line-length = 110, target-version = "py311".
  • Typing: prefer PEP 604 unions (str | None), dataclass for plain data, pydantic.BaseModel for any HTTP request/response or external contract.

SPARQL parsing — always rdflib

  • Entry point for parsing: from rdflib.plugins.sparql.parser import parseQuery followed by from rdflib.plugins.sparql.algebra import translateQuery to 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 one visit_<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-mapper into a single rdflib.Graph at app startup; cache by ontology hash.
  • URI → physical collection/edge/property resolution goes through one SchemaResolver class. 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.

Read the full file on GitHub · 87 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. yesterday First seen · 87 lines · 0 tokens per session scan A 0190b1c15a68

Subscribe to this mod's changes

100-backend-python is a cursor rule published in the GitHub repository arango-solutions/arango-sparql-py (2 stars, last pushed 15d 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.