I built a workflow that checks whether my AI agent's context was enough, then proves it with my own tests

Описание и применение

Создана автоматизированная цепочка инструментов, проверяющая достаточность контекста для AI‑кодера и подтверждающая её тестами.

Задача

Uncertainty whether the context provided to an AI coding agent is sufficient for task completion.

Результат

The workflow identified two verification gaps and a test failure due to a path assumption, and uncovered a bug in the contained runner that hid user site-packages, which was fixed and shipped.

Рабочий процесс

• Clone the target repository
• Run ghostworkflow to execute GhostSlice, GhostVerify, GhostBench, and GhostConsole in sequence
• Review the console board for verified status, gaps, and test results
• Address any identified gaps or failures
• Iterate until all checks pass

Доказательства

GhostSlice cuts a project down to a task bundle and reports the token cut. On my own bench repo: 74,692 tokens full, 29,926 sliced.

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GhostVerify checks the bundle against the task before an agent runs, and says gaps_found when something reachable is missing.

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GhostBench runs the same task full vs sliced and, when you give it a test command, executes your tests in a contained runner (scrubbed environment, your secrets never reach the test process) and labels the result honestly: static analysis is not called a live run.

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I ran the whole thing on its own repo and published the result warts and all: verdict attention, 2 verify gaps, and a test failure that turned out to be a path assumption in one of my own tests.

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The first dogfood run also caught a real bug in the contained runner (it hid the user site-packages, so pytest vanished), which is fixed and shipped.

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