Analyze graded `trial_result` JSONL outputs from the eval pipeline, including reliability and custom slicing.
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Use test-driven development for behavior-changing feature or fix work, and whenever the user mentions TDD, test-first, red-green-refactor, tracer bullets, integration tests, or public-interface behavior tests. Skip for docs-only, path-only rename, formatting-only, or purely mechanical chores unless explicitly requested.
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Work in vertical red-green-refactor slices:
Do not write a batch of imagined tests before implementation. Each new test should respond to what the last cycle taught you about the actual behavior and interface.
bun test for targeted tests and bun --bun tsc --noEmit for the type gate.test, not it, and organize with describe.AGENTS.md rules for the touched area.Explore the codebase first. If the public interface, expected behavior, or risk boundary is discoverable from code, tests, or docs, use that evidence instead of asking the user.
Ask one concise question only when the decision cannot be discovered and a reasonable assumption would be risky. Include your recommended answer.
Before writing production code, identify:
For each behavior:
If the test passes before production code changes, it is not a valid RED signal. Tighten the test, choose a different behavior, or explain why existing coverage already proves the behavior.
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Analyze graded `trial_result` JSONL outputs from the eval pipeline, including reliability and custom slicing.
中文简介正在准备中。当前显示原文说明。
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
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Build command-only adapters for `eval` run mode using strict stdin/stdout JSON contracts.
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End-to-end eval-suite orchestration with the `eval` command: run -> grade -> compare -> calibrate, using strict JSON contracts and schema discovery.
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