Property based hardening

Skill tuanductran/soulmap-ai/.claude/skills/property-based-hardening

Use Hypothesis and invariant-driven tests to harden SoulMap AI parsers, normalizers, and small deterministic helpers without creating flaky or slow suites.From its SKILL.md

Install

npx -y skills add tuanductran/soulmap-ai --skill property-based-hardening

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One thing to look at

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SKILL.md

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Property-based hardening

Use this skill when a Python surface benefits from invariants over example-by-example tests, especially for parsing, normalization, serialization, and boundary-safe helper logic.

Do not use this skill for

large end-to-end behavior that is clearer as directed examples, use testing-strategy-review
eval dataset authoring, use eval-suite-maintainer
general Python cleanup with no invariant question, use code-quality-review

Mission

Strengthen small but important Python contracts with compact, deterministic property-based tests that still run cleanly in SoulMap's local and CI workflows.

Sources to check first

pyproject.toml
tests/unit/
the helper or parser being hardened
nearby example-driven tests
../rules/python-tooling.md

What to look for

small deterministic functions with clear invariants
parsers or normalizers that should round-trip or preserve constraints
example tests that miss obvious edge cases
Hypothesis strategies that are too broad, slow, or unstable under pytest -n auto
opportunities to shrink a large list of brittle examples into one stronger invariant

Workflow

Name the invariant before writing the strategy.
Keep generated inputs bounded and realistic for the touched helper.
Reuse existing example tests when they explain intent better than a property.
Avoid strategies that create slow, opaque failures or noisy CI runs.
Prefer one or two strong invariants over a large test matrix.
Run the narrow property test first, then the broader suite if the helper is shared.

Expected output

Invariants

State the property being protected in plain language.

Fixes

Summarize the Hypothesis strategy or helper change that made the contract safer.

Validation

State which targeted and broad tests were run.

Definition of done

The hardened surface should be:

protected by a clear invariant
fast enough for normal repo workflows
stable under pytest -n auto
easier to trust than before