System Python Large File Pipeline

Resolve a Python large-file pipeline with an explicit bounded-memory constraint.

Generated example

This page is generated from evaluations/scenarios/system-python-large-file-pipeline.yaml. The scenario YAML remains the executable source of truth.

Request

Implement a data pipeline that processes a large file with bounded memory.

Repository evidence

pyproject.toml

[project]
name = "pipeline"

pipeline.py

Expected resolution

Resolution layer	Expected
Profile	data-pipeline
Modifiers	memory-sensitive
Language adapters	python
Framework adapters	—

Normalized context

artifact_type: data-pipeline
architecture_authority: incremental-improvement
constraints:
  memory_sensitivity: high

Skill modes

Item	Expected
performance-and-resources	budget-constrained

Conflict decisions

Item	Expected
materialization-vs-bounded-memory	prefer-bounded-processing

Significant decisions

• bound-memory-use

Principles under test

• performance-budgeting
• measure-dont-guess

Forbidden outcomes

• materialize unbounded input

Scenario source

schema_version: 0.10.0
id: system-python-large-file-pipeline
description: Resolve a Python large-file pipeline with an explicit bounded-memory
  constraint.
input:
  request: Implement a data pipeline that processes a large file with bounded memory.
  repository_signals:
    pyproject.toml: '[project]

      name = "pipeline"

      '
    pipeline.py: ''
expected:
  context:
    artifact_type: data-pipeline
    architecture_authority: incremental-improvement
    constraints:
      memory_sensitivity: high
  profile: data-pipeline
  modifiers:
  - memory-sensitive
  language_adapters:
  - python
  framework_adapters: []
  significant_decisions:
  - bound-memory-use
  skill_modes:
    performance-and-resources: budget-constrained
  conflicts:
    materialization-vs-bounded-memory: prefer-bounded-processing
forbidden:
- materialize unbounded input
principles_under_test:
- performance-budgeting
- measure-dont-guess