Rethinking skills and prompts for GPT-6 Astra

As coding agents become more capable, particularly with GPT-6 Astra, existing instructions in skills, AGENTS.md files, and task prompts should be reassessed because excessive or overly prescriptive guidance can consume context, create conflicts, and limit model performance. Effective skills should have concise, specific descriptions that clarify when they apply, use progressive disclosure by routing users to detailed supporting materials only when needed, and avoid rigid step-by-step recipes that may constrain newer models or other contributors’ agents. Repository-wide AGENTS.md instructions should similarly be contextual rather than requiring broad documentation review or redundant testing for every task, while still granting permission for safe autonomous workflows such as running local disposable-fixture tests and fixing related failures. The discussion also emphasizes defining decision boundaries carefully, since Astra’s stronger safety judgment may interpret restrictive approval requirements too literally, and explicitly stating completion criteria when continued implementation, testing, inspection, and iteration are desired. Updating instructions for new models can therefore reduce unnecessary friction while enabling agents to take on more ambitious work.

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