agentleFS
w2urw2ur/claude-code-setup/skills/testing-conventions/SKILL.md

testing-conventions

Skill2 starsChanged 2 months agoMITView on GitHub

Test conventions across this portfolio — regression-test comment format, property-test setup for TS and Python, the financial-math arbitrary settings that were tuned by measurement, and why a failing check may be pinning a defect rather than reporting one. Load before writing tests, adding a regression test to a bug fix, setting up property tests, or when a test disagrees with what you expect.

2 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When a check disagrees with what you expect, read the test's intent before assuming the code is wrong.

    line 42

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Testing conventions

The rules live in ~/.claude/CLAUDE.md. This skill holds the formats and settings.

Unit and regression

  • Unit tests for all logic.
  • A regression test alongside every bug fix, carrying the commit that fixed it:

    // Regression: <commit-hash> — <bug description>

    The hash makes the test self-documenting: a future reader can pull the fix and the reasoning without archaeology in the test file itself.

Property tests for pure transforms

  • TypeScript → fast-check
  • Python → hypothesis
  • File naming: foo.property.test.ts alongside the unit tests.

Financial-math arbitraries — settings tuned by measurement

settingvaluewhy
float arbitrariesnoNaN, noDefaultInfinityotherwise every property fails on degenerate inputs rather than on real ones
runs≥ 1000the default run count does not reach the interesting tail
tolerance1e-6not 1e-10 — measured too tight at scale. Accumulated float error over a long series exceeds 1e-10 legitimately, so the tighter tolerance reports false failures.

The 1e-10 value looks more rigorous and is the obvious thing to reach for. It was tried and it was wrong. Use 1e-6.

A test can pin a defect — read its intent before "fixing" it

When a check disagrees with what you expect, read the test's intent before assuming the code is wrong. Twice in a single day the test was the one asserting the truth:

  • test_check_ignores_generic_data_drift asserted "data is synced but not guarded" — the surprising behaviour was the deliberate one, named in the test.
  • A Python and a TypeScript implementation each pinned the other's answer as correct, so "fixing" either one to match intuition would have broken the pair.

A test whose name states an intent is evidence about the design, not just about the code path.

What a green suite does not cover

Two measured blind spots in this portfolio:

  • UI components are not unit-tested by convention. In my-fitness-app, 1,155 tests passed against a tree that would not build — a deleted module was still imported by two components. Only the build gate caught it.
  • A route-handler test never runs the middleware. A gated API route can ship 100% broken with a fully green suite. Test the middleware path separately.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.