Good AI Task

AI compatibility

Building a Django N+1 static analyzer is a clean, well-scoped job for AI.

Good fit

AI can handle this.

Average across 1 submission.

82
avg / 100

The honest read

Writing a static analyzer for Django admin N+1 patterns is a well-scoped coding task with clear success criteria: parse the file, detect known anti-patterns in list_display and get_queryset, and emit a structured report. AI agents are strong at AST-based code analysis and pattern matching against a known set of Django idioms. The main risk is false negatives on complex dynamic patterns, but the output is a report — not a code change — so error cost is low.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The task is structurally identical every time: parse a Python file, match against known Django admin anti-patterns, and produce a report. The rules for what constitutes an N+1 risk in list_display and get_queryset are well-documented and finite.

Ambiguity Tolerance

High

Success criteria are crisp: the analyzer must flag list_display fields that traverse foreign keys without select_related, and get_queryset overrides missing prefetch/select calls. The output format (report with suggestions) is concrete and verifiable.

Data & Tool Availability

High

The agent only needs the admin.py file and knowledge of Django ORM patterns — no external APIs, credentials, or live database access required. The file is self-contained and can be passed directly.

Error Cost

Low

The deliverable is a static analysis report, not a code modification. A false positive wastes a developer's review time; a false negative misses an optimization opportunity. Neither outcome is irreversible or damaging.

Human Judgment Required

Medium

Most N+1 patterns in Django admin are mechanical to detect, but some cases — deeply nested dynamic accessors, custom manager methods, or conditional queryset logic — require understanding runtime behavior that static analysis cannot fully resolve. A developer should review the report before acting.

What an agent would need

  • Access to the 2,000-line admin.py file as input
  • Knowledge of Django ModelAdmin conventions (list_display, get_queryset, raw_id_fields, etc.)
  • Ability to use Python's ast module or equivalent to parse and traverse the file's syntax tree
  • A rule set mapping common N+1 patterns to their select_related/prefetch_related remedies
  • A defined output format for the report (e.g., per-class findings with line numbers and suggested fixes)

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