Write Foundry tests for Solidity smart contracts using fuzz and invariant testing.
npx skills add https://github.com/collat-labs/collat --skill testing-collat-labs
Please help me install this Agent Skill. Skill: testing Source: https://github.com/collat-labs/collat/tree/main/.agents/skills/ethskills/testing Command: npx skills add https://github.com/collat-labs/collat --skill testing-collat-labs
This Skill helps you prevent financial and integration failures in Ethereum-style smart contracts by guiding you toward testing strategies that uncover edge cases, economic invariant breaks, and real-world protocol interactions.
Use the testing Skill to write Foundry tests that target edge-case reverts, fuzz important functions, and add invariant checks for your vault-style stateful contract.
High-intent search queries and answers about installing and using this skill.
Invariant testing validates properties that must hold across long, random sequences of actions, making it essential for stateful systems like vaults, AMMs, and lending protocols. It uncovers economic invariant breaks that standard unit tests miss.
Fuzz testing in Foundry uses property-based randomized inputs with proper bounding to find unexpected bugs. You define assertions and input bounds, then the fuzzer generates adversarial inputs to validate custom business logic and failure modes.
Yes, fork testing runs tests against real deployed protocol state on a fork. It detects integration issues that mocks can hide, ensuring your contract handles real economic conditions and external protocol calls correctly.
Focus unit tests on custom business logic, access control boundaries, and economic behavior rather than trivial getters. Meaningful assertions target failure modes and revert conditions specific to your contract's logic.
Mock-only tests hide integration issues because they simulate external protocol behavior artificially. Fork testing against real deployed state reveals actual protocol interactions and integration failures that mocks cannot replicate.
Input bounding restricts fuzz test parameters to realistic ranges, preventing false positives from invalid inputs. Proper bounds ensure randomized testing targets meaningful edge cases within your contract's expected operating parameters.
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