Improve slow Django test suites through measured profiling, safe test settings, database and migration tuning, parallel execution, test-data cleanup, and CI workflow changes.
SkillScore74Maintained
Automatic
◆QA & Test Engineering pack
Last verified 8/16/2026
What it changes
Passed 17 of 23 test cases with the skill, and 15 of 23 without it.
Without skill65%
With skill74%
Checks it passed that the model alone missed
Refers to django-targeted-mocking for mocking external services, time, and settings safely.
Refers to django-test-parallelization for handling order isolation and worker configuration.
Requires reporting baseline and final runtimes from the same command alongside changes and risks.
and 2 more in the full report
5 fixed12 passed either way3 still failing3 broke
Pass-rate lift
+9 pp
Tokens
+13%
Agent turns
0%
Response time
-25%
Model gemini-3.6-flashMeasured 8/16/2026v117 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
We want to instantly speed up our Django test suite by adding --parallel or pytest-xdist. Should we enable parallel execution right at the start of our optimization effort?
What was checked, without and with the skill
Checked
Without skill
With skill
Advises verifying test isolation and shared resource safety before enabling parallel test execution.
Passed
Passed
Refers to django-test-parallelization for handling order isolation and worker configuration.
Missed
Passed
case-14 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md82 lines2 files
Django Test Performance
Use this as the orchestration skill for speeding up a Django test suite. Do not start by changing settings blindly; first measure the suite and then pick the smallest safe optimization with a repeatable verification command.
Core Workflow
Establish a baseline.
Run the same command developers or CI use.
Capture wall-clock time, runner, settings module, database backend, worker count, and branch.
If the cause is unclear, use django-test-profiling before changing behavior.
Classify the dominant cost.
Startup/import/discovery: reduce collection scope and expensive app initialization.
Database creation/migrations: reuse DB locally, squash migration history, or improve database storage.
Per-test setup: reduce fixture data, use factories, and use setUpTestData.
External backends: replace file storage, cache, task queues, and instrumentation with test-safe backends.
Long tail of tests: parallelize after isolation checks.
Apply low-risk wins first.
Fast password hasher.
DEBUG = False in tests.
Disable database serialization when serialized rollback is not needed.
Disable debug toolbar, Sentry/Rollbar/APM initialization, and other instrumentation.
In-memory or local test backends for storage, cache, and task queues.
See speed-wins.md.
Improve data and structure.
Use django-test-data for factory, fixture, setUpTestData, TestCase, and integration-vs-unit decisions.
Improve migrations and database setup.
Use --keepdb or --reuse-db for local repeat runs.
Force rebuilds when migration history changes.
Prefer squashing migrations over disabling migrations globally.
Keep the test database backend close to production unless the project is database-agnostic.
Parallelize only when safe.
Use django-test-parallelization for order isolation, shared resources, and worker configuration.
Re-measure with the original baseline command.
Report before/after runtime and the changed risk surface.
Diagnose and improve slow Django database-backed endpoints with evidence-first profiling, query-plan review, index selection, ORM loading fixes, batching, database-side computation, materialized views, and pagination choices. Use when a Django view, API, job, report, queryset, or database query is slow and the right optimization path is not obvious.
Close match
+18 pp pass-rate lift
hashgraph-online/docs-feedback-triage
Gather, classify, prioritize, respond to, and act on developer documentation feedback from page feedback, support issues, community forums, field notes, sentiment, surveys, user councils, bug reports, and user follow-up. Use when setting up docs feedback channels, triaging docs issues, responding to frustrated users, prioritizing doc fixes, or converting feedback into actionable documentation work.