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    QA Test Engineer

    by GarphenGate

    1

    Generate risk-prioritized test cases for functions, APIs, and user stories focusing on edge cases and failure modes.

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    You say

    Generate test cases for a function process_refund(order_id: str, amount: float) that interacts with a payment gateway and updates a database.

    Your agent does

    UNIT: process_refund function Assumptions: Gateway supports partial refunds; order_id must exist in DB.

    P1:

    • Insufficient funds in gateway — input: valid ID, amount > gateway balance → expect: GatewayError, DB status remains 'paid'
    • Duplicate refund call — input: same ID twice → expect: Idempotency check, second call returns 409

    P2:

    • Zero amount refund — input: amount=0.00 → expect: ValidationError

    P3:

    • Max float precision — input: amount=99.999 → expect: Rounding to 99.99

    Not tested here: Actual network latency; DB connection timeouts.

    Missed case: Partial failure where gateway succeeds but DB update fails. You'll need a mock for the gateway that returns success while the DB driver throws.

    About this skill

    The problem

    Writing tests is easy, but identifying the specific scenarios that actually break code is hard. Developers often focus on happy paths while missing critical edge cases, boundary conditions, and state-related failures that cause production incidents.

    What it does

    • Analyzes function signatures, API endpoints, or user stories to extract testable logic.
    • Categorizes cases into happy paths, boundaries, invalid inputs, state effects, and security.
    • Prioritizes cases (P1 to P3) based on the business impact and risk of failure.
    • Identifies necessary mocks or fixtures required to implement complex test cases.
    • Explicitly lists out-of-scope scenarios that require full integration environments.

    Why this beats prompting it yourself

    General-purpose prompts often return generic "test that it works" filler. This skill enforces a strict risk-based priority system and forces the identification of concrete inputs and expected outputs, preventing low-value "theater" testing.

    Use cases

    • Auditing an existing thin test suite for missing boundary logic.
    • Generating a test plan for a new API endpoint before writing a single line of code.
    • Identifying failure modes for functions handling untrusted user input.
    • Drafting QA requirements for a Jira ticket or user story.

    Known limitations

    This skill focuses on test design, not code generation. It does not output runnable test scripts or handle end-to-end browser automation setups.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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